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SeMoDe – Simulation and Benchmarking Pipeline for Function as a Service

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ISSN 0937-3349
Nr. 105
SeMoDe – Simulation and Benchmarking
Pipeline for Function as a Service
Johannes Manner
November 2021
SeMoDe – Simulation and Benchmarking Pipeline for
Function as a Service
Johannes Manner
Lehrstuhl für Praktische Informatik, Fakultät WIAI
Otto-Friedrich-Universität Bamberg
An der Weberei 5, 96047 Bamberg
Tool Documentation Version 1.1
Cloud computing started with the promise of delivering computing resources elastically at
scale, pay per use and on demand self-service to name a few capabilities. In early 2016,
Amazon Web Services (AWS) launched a new product called AWS Lambda which started
the so called serverless hype and established a new cloud delivery model, namely Function
as a Service (FaaS). FaaS offerings keep the promise of delivering computing resources on
demand. They dynamically scale up and down function instances and introduce the most
fine-grained billing model across all as-a-service offerings by accounting on a milliseconds
basis. Despite this flexibility and the possibility to concentrate on the business functionality,
a FaaS user loses operational control. Only a few configuration options remain to tune the
functions. The first pay-as-you-go billing model raises new questions for performance-cost
In order to choose a suitable configuration dependent on the use case and get a solid un-
derstanding of performance impact of FaaS platforms, SeMoDe implements a benchmarking
and simulation pipeline. It calibrates a physical developer machine, simulates the function
in different settings which are comparable to those of cloud offerings and enables a decision
guidance to choose an appropriate configuration when deploying it. Based on a Structured
Literature Review (SLR) to show the benchmarking and simulation efforts, I suggest a check-
list for conducting fair, repeatable and meaningful benchmarks with a focus on documenting
the experiments.
Cloud Computing, Function as a Service, Benchmarking, Simulation
Distributed Systems Group
Otto-Friedrich Universität Bamberg
An der Weberei 5, 96047 Bamberg, GERMANY
Prof. Dr. rer. nat. Guido Wirtz
Due to hardware developments, strong application needs and the overwhelming influence
of the net in almost all areas, distributed systems have become one of the most important
topics for nowadays software industry. Owing to their ever increasing importance for ev-
eryday business, distributed systems have high requirements with respect to dependability,
robustness and performance. Unfortunately, distribution adds its share to the problems of
developing complex software systems. Heterogeneity in both, hardware and software, per-
manent changes, concurrency, distribution of components and the need for inter-operability
between different systems complicate matters. Moreover, new technical aspects like resource
management, load balancing and guaranteeing consistent operation in the presence of partial
failures and deadlocks put an additional burden onto the developer.
The long-term common goal of our research efforts is the development, implementation and
evaluation of methods and tools that support the realization of robust and easy-to-use software
for complex systems in general while putting a focus on the problems and issues regarding
distributed systems on all levels. Our current research activities are focussed on different
aspects centered around Cloud computing, esp. Microservice and Serverless architectures:
Integration Testing of Serverless Applications: Many cloud platform providers offer
Function as a Service (FaaS) now which got popular with the introduction of Ama-
zon’s AWS Lambda in 2014. These offerings are based on serverless functions whose
statelessness helps handle dynamic workloads by scaling them dynamically.Serverless
functions are often combined with other services like data storage , e.g., to save the
state of the application. The interactions of these services with serverless functions
build complex systems. The aim of this project is to support the integration testing
process for serverless applications. While it is easy to test single functions in isolation,
the emerging behavior caused by the integration of serverless functions with other
services needs to be tested. Therefore, the relevant aspects of an application have to
be modeled to support the creation of test cases. Coverage criteria are created and
their applicability is investigated. Furthermore, the automatic creation of test cases
for serverless applications shall be supported.
Benchmark and Simulation of Cloud Functions (FaaS): The goal of this project is to
understand runtime characteristics of the platform as well as characteristics of the
deployed cloud functions and take dependent services like database access into consid-
eration when building a local clone of the platform at a developer’s machine. These
aspects allow to configure cloud functions appropriately to the specified requirements
upfront. Furthermore, a simulation and benchmarking tool to conduct repeatable and
fair experiments is under development.
Architecting Cloud-native Applications: Cloud-native applications are designed and
built to maximally exploit the benefits offered by modern cloud computing. This com-
prises several aspects, such as a fine-grained modular architecture, using existing cloud
services instead of custom solutions, exploiting the rapid elasticity of cloud comput-
ing, achieving robustness by distributing an application over independent nodes, and
finally a faster and more agile development process. The goal of this project is to
analyze how the development of such cloud-native applications can be supported and
improved with regard to all these aspects. The focus is on the overarching architecture
of a cloud-native application, specifically how the individual components are combined
and how they interact, rather than focusing on individual components.
Universal Cloud-Edge-IoT Orchestration: The emergence of the Internet of Things
(IoT) is a significant development in today’s information technology and involves the
ability of physical devices to exchange data over networks. Often, the generated data
is transferred to the cloud and processed there. Likewise, the cloud may take control of
the devices. The ever-increasing number of data-generating IoT devices is creating new
challenges that require modifications of the already existing Cloud-edge architectures.
This project aims to realize an abstracted, configurable and simplified management
of cloud-edge/edge-IoT architectures based on already popular container orchestration
platforms like Kubernetes and other platforms and technologies in order to make the
use of edge computing easier for even more application areas and users.
Visual Programming- and Design-Languages: The goal of this long-term effort is the
utilization of visual metaphors and languages as well as visualization techniques to
make design- and programming languages as well as distributed systems more under-
standable and, hence, more easy-to-use.
More information about our work, i.e., projects, papers and software, is available at our
homepage (see above). If you have any questions or suggestions regarding this report or our
work in general, don’t hesitate to contact me at
Guido Wirtz
Bamberg, May, 2021
Contents I
1 Introduction 1
2 Related Approaches 3
3 Checklist for FaaS Benchmarking 11
4 System Architecture 12
4.1 Database Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2 Package Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5 Setup 16
5.1 System Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.2 Provider Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.3 System Startup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6 Supported Functionality 17
6.1 Web Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.1 Pipeline Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.2 Visualizing Execution Data . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.2 Benchmarking Cloud Function Platforms . . . . . . . . . . . . . . . . . . . . . . 26
6.2.1 Implementing a Function under Benchmark . . . . . . . . . . . . . . . . 26
6.2.2 Benchmarking Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.2.3 Submitting Requests based on Arbitrary Load Pattern . . . . . . . . . . 28
6.3 Simulating Local Cloud Function Executions . . . . . . . . . . . . . . . . . . . . 30
6.3.1 Calibrating Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3.2 Simulation based on Calibration . . . . . . . . . . . . . . . . . . . . . . . 31
6.4 RESTAPI......................................... 31
6.4.1 REST API Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.4.2 Exposed REST API Endpoints . . . . . . . . . . . . . . . . . . . . . . . . 32
6.5 CLI Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Contents II
7 Conclusion and Future Work 33
Acknowledgment 38
A User Management 38
List of previous University of Bamberg reports 39
List of Figures III
List of Figures
1 SLR conducted at dblp on 23rd of June 2021. . . . . . . . . . . . . . . . . . . . . 3
2 Number of experiments per provider. . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Overall System Architecture of SeMoDe ........................ 12
4 Database Layout of SeMoDe .............................. 13
5 Package Diagram of SeMoDe .............................. 15
6 Setups Page to See Existing Setups and Create Them . . . . . . . . . . . . . . . 18
7 Benchmark Configuration Page I . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8 Benchmark Configuration Page II . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
9 Calibration Configuration Page I . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10 Calibration Configuration Page II . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
11 Calibration Configuration Page III . . . . . . . . . . . . . . . . . . . . . . . . . . 25
12 Benchmark Publicly Visible Page . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
13 User and Platform Perceived Performance (based on Figure 1 in [1]) . . . . . . 28
14 CPU performance computed by LINPACK at different CPU Quotas (based
on Figure 1 in [2]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
15 Open API Specification (OAS) for exposed REST API. . . . . . . . . . . . . . . 32
16 Users Page for Administrators to Change Roles and Passwords. . . . . . . . . . 38
List of Tables IV
List of Tables
1 Research conducted by using SeMoDe. ........................ 2
2 SLRs of the conducted SLR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 FaaS research context and related papers to the intended aims of SeMoDe. . . 5
4 Papers Related to SeMoDe Research Goals. . . . . . . . . . . . . . . . . . . . . . 7
Listings V
1 Centerpiece of the BenchmarkExecutor Class . . . . . . . . . . . . . . . . . . . . 29
2 Start SeMoDe as CLI application . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Abbreviations VI
ACM Association for Computing Machinery
ARN Amazon Resource Name
AWS Amazon Web Services
AZF Azure Functions
DTO Data Transfer Object
FaaS Function as a Service
GCF Google Cloud Functions
GFLOPS Giga FLoating point Operations Per Second
HPC High Performance Computing
IaaS Infrastructure as a Service
IaC Infrastructure as Code
IAM Identity and Access Management
IEEE Institute of Electrical and Electronics Engineers
JIT Just In Time
JRE Java Runtime Environment
JPA Java Persistence API
JWT JSON Web Token
NIST National (US) Institute of Standard and Technology
OAS Open API Specification
ORM Object Relational Mapping
OS Operating System
PaaS Platform as a Service
RTT Round Trip Time
SLR Structured Literature Review
1 Introduction 1
1 Introduction
FaaS, a concept where ephemeral and stateless cloud functions run only upon request, is the
next evolution in cloud computing [3, 4]. It enhances the cloud computing capabilities [5]
defined by the National (US) Institute of Standard and Technology (NIST) to prior service
models like Platform as a Service (PaaS) or even Infrastructure as a Service (IaaS). Broad
network access and resource pooling characteristics do not significantly change when com-
paring FaaS with PaaS. Whereas on-demand self-service results in a provisioning of cloud
function instances for every single request which also positively influences the rapid elastic-
ity characteristic. Most importantly, the billing model is leading to a measured service in
100 millisecond chunks. In December 2020, AWS Lambda1, the first commercial offering of
cloud functions, recently announced the most fine-grained billing model in a granularity of
The motivation for this report and the ongoing research project3is to shed some light onto
cloud function platforms’ inner workings and their runtime behavior. The only possible way
to understand the improved cloud computing characteristics by FaaS is to deploy different
functions in a production environments and assess the results. This procedure is often
time-consuming and costly and the results of these benchmarks are seldom repeatable nor
portable to other functions [6]. Also other benchmarking characteristics [7, 8] like simplicity
or efficiency are hard to achieve when designing a benchmark but even more challenging
when other researchers want to assess and repeat the experiments since a lot of publications
try to “fool the masses with irreproducible results”4.
Nevertheless, benchmarks are an important step when developers want to deploy a func-
tion. They decide on the configuration of a function based on the benchmark data, but the
functions deployed in production and the benchmark functions often have different charac-
Therefore, the second aspect of our research prototype is the simulation of cloud function
behavior on a local machine. This removes the burden of costly experiments in the cloud and
enables an understanding of the runtime behavior of the cloud functions. The tool provides
a simulation pipeline with the assistance of collected data from selected benchmarks and
enables developers to assess their function under development, before deploying it, to find
the best configuration for their requirements, e.g. lowest cost or shortest execution time. To
do so, it collects data from cloud providers to understand their platform implementation and
the impact of different resource configurations on the function execution first. In a second
step, the prototype executes a similar calibration on a developer machine to relate the local
performance to the cloud performance. Based on this data, local function executions can
then predict the execution behavior of a deployed function before deploying it.
The first research question motivates the SLR, while the second builds upon the results and
condenses best practices for documenting experiments. Furthermore, the following three
statements highlight the contribution SeMoDe makes to answer the research questions.
2 granularity-adds-
4Title of the keynote from Lorena Barba at the 2021 edition of IEEE International Parallel and Dis-
tributed Processing Symposium, GuH-6VFU.
1 Introduction 2
RQ1 - Which tools and experiments exist for benchmarking FaaS platforms and performing
RQ2 - How should a FaaS experiment be documented and which items are necessary for a
convincing data evaluation?
S1 -SeMoDe implements a novel concept to reach dev-prod parity for cloud functions based
on its benchmarking and simulation features.
S2 -SeMoDe enables reproducibility and documentation of experiments through its design
and versioning of experimental settings.
S3 -SeMoDe invites other researchers to collaborate, reconstruct already conducted exper-
iments and share new benchmarking results based on its public interface.
These statements are explained in detail, when we look at SeMoDe’s capabilities and the de-
sign decisions. Related approaches are discussed in Section 2 based on a SLR answering RQ1.
Lessons learned from the SLR are summarized in a checklist to emphasize important aspects
for building a fair, repeatable and meaningful FaaS benchmark (see RQ2). Section 4 in-
troduces the overall architecture of the prototype by discussing the different components
and tools, the database and package layout. The subsequent Section describes the setup
and configuration of related provider tools which are necessary to execute benchmarks on
public cloud provider hosted FaaS platforms. Section 6 describes the features the prototype
implements, especially the configuration via the web interface and the already mentioned
benchmarking and simulation pipeline. We conclude the technical report with Future Work
At the time of writing this technical report, SeMoDe contributed to the evaluation or practical
part of five papers. I contributed to all these papers as the main author. They are ordered
anti-chronological in Table 1.
Table 1: Research conducted by using SeMoDe.
Title Venue Presented Ref
Optimizing Cloud Function Configuration via Lo-
cal Simulations
CLOUD 2021 [9]
Why Many Benchmarks Might Be Compromised SOSE 2021 [2]
Impact of Application Load in Function as a Ser-
SummerSoC 2019 [10]
Cold Start Influencing Factors in Function as a
WoSC 2018 [1]
Troubeshooting Serverless Functions: A Combined
Monitoring and Debugging Approach
SummerSoC 2018 [11]
2 Related Approaches 3
2 Related Approaches
A lot of FaaS research was done conducting benchmarks and investigating special properties
of the platforms as well as finding ways to optimize the cloud functions runtime behavior. In
this Section, I will highlight some related respectively competing approaches to the research
prototype described in this report.
To get a solid basis and a repeatable literature design, I did a SLR based on the guidelines
of Kitchenham and Charters [18]. After defining a short review protocol5, I picked the
dblp computer science bibliography6as a source since it includes work from journals and
conference proceedings from different publishers like Association for Computing Machinery
(ACM) and Institute of Electrical and Electronics Engineers (IEEE).
Initial Search Filtering Skimming
62 21 39
cloud function
aws lambda
function as a service
Searching for
Reading abstract and
results/conclusion if
Backward Search
Figure 1: SLR conducted at dblp on 23rd of June 2021.
Figure 1 shows the SLR process. Since the advanced search at dblp was disfunctional when
combining several search terms with an OR operator, I decided to conduct multiple searches
with the keywords presented in the initial search phase. I used a set of generic keywords
to get a broad basis for the further steps. This resulted in 1240 entries. To handle the
literature, JabRef7was used which helped with removing duplicates in this first phase.
Since this Section is about finding competing approaches to the already published papers,
I excluded the publications mentioned in Table 1. The remaining 1163 entries were then
filtered by the search feature of JabRef. Similarly to the search on dblp, I also decided
to perform a single search for benchmark,simulation,experiment,tool and simulator. The
filtering keywords were chosen due to the focus on understanding benchmark and simulation
approaches in FaaS as well as looking at experiments and tools. The filtering process was
done on the title and other metainformation like the conference or journal name. After
removing five duplicates, I ended up with 62 papers which I skimmed in the next phase.
For most of them, it was sufficient to read the abstract to evaluate their relevance. To
remove doubts on ambiguous papers, I read the research questions and the results part to
make a well justified decision. After the skimming phase, 21 papers were left which I read
in detail while answering the questions of the review protocol. Since the search was only
done on dblp and the keywords were quite generic for the initial search and the filtering
process, I decided, when discussing the SLR process with colleagues, to make a backward
5Protocol plus results of the SLR,
2 Related Approaches 4
search to identify further relevant research. The snowballing process was done for all 21
papers in the third step and also the papers which were identified during snowballing. After
finishing this recursive snowballing process, 39 papers were part of the final set. The following
Tables show the literature set by categorizing them in SLRs (Table 2), papers which are not
directly related to experiments on public cloud providers (Table 3), competing approaches,
and insights which are important for the prototype design and the improvement of empirical
FaaS research (Table 4).
Table 2: SLRs of the conducted SLR.
Ref Authors Title Year
[19] Yussupov et al. A Systematic Mapping Study on Engineering Function-
as-a-Service Platforms and Tools
[20] Scheuner and
Function-as-a-Service performance evaluation: A multi-
vocal literature review
[6] Kuhlenkamp
and Werner
Benchmarking FaaS Platforms: Call for Community
Table 2 includes SLRs, which are not discussed in detail here, but each addresses a specific
aspect of the FaaS research domain. Yussupov and others [19] motivate the work on a
consistent benchmark and simulation framework due to their SLR being focused on different
platforms and tools. Performance aspects are discussed in Scheuner’s and Leitner’s
work [20] where they included 112 sources from academic and gray literature. Especially
interesting is the design of different experiments found in literature and their impacts on the
data evaluation and therefore on the results. Kuhlenkamp and Werner [6] motivated
the community to participate in the benchmarking process by revealing issues about the
reproducibility of conducted research. The authors stated in their RQ3 that only 3 out
of 26 experiments were reproducible based on the provided information. This motivates
my statement S2 to enable other researchers to reproduce the experiments conducted with
SeMoDe by looking at the experiment documentation.
Table 3 contains the publications, I classified as related to my empirical FaaS research but
not directly supporting my efforts in building the benchmark and simulation pipeline in-
tended by SeMoDe. Nevertheless, the contributions of these papers are important to stress
the context and related research areas. The first entry [21] is a vision paper about how
a benchmark in the FaaS area should look like with a focus on real world experiments to
overcome microbenchmarks. Mohanty and others [22] evaluated the open source platforms
Fission8, Kubeless9and OpenFaaS10. They focused on testing how concurrent users and the
autoscaling property, which is based on Kubernetes Horizontal Pod Autoscaler, influence the
number of running function instances. Since open source solutions based on K8s are not in
the focus of this work, the paper is not included in Table 4 for further investigation. The
same holds true for the edge computing frameworks, where the first work [23] used AWS
Greengrass11 and Azure IoT Edge12 for latency measurements and cost comparisons. The
second publication [24] implemented an extension for CloudSim [35], a popular simulator for
2 Related Approaches 5
Table 3: FaaS research context and related papers to the intended aims of SeMoDe.
Ref Authors Title Year
[21] van Eyk et al. Beyond Microbenchmarks: The SPEC-RG Vision for a
Comprehensive Serverless Benchmark
[22] Mohanty et al. An Evaluation of Open Source Serverless Computing
[23] Das et al. EdgeBench: Benchmarking Edge Computing Platforms 2018
[24] Jeon et al. A CloudSim-Extension for Simulating Distributed
[25] Kritikos and
Simulation-as-a-Service with Serverless Computing 2019
[26] Bardsley et al. Serverless Performance and Optimization Strategies 2018
[27] Gan et al. An Open-Source Benchmark Suite for Microservices and
Their Hardware-Software Implications for Cloud & Edge
[28] Malla and
HPC in the cloud: Performance comparison of function
as a service (FaaS) vs infrastructure as a service (IaaS)
[29] Malawski et
Serverless execution of scientific workflows: Exper-
iments with HyperFlow, AWS Lambda and Google
Cloud Functions
[30] Giménez-
Alventosa et
A framework and a performance assessment for server-
less MapReduce on AWS Lambda
[31] Quaresma et
Validation of a simulation model for FaaS performance
benchmarking using predictive validation
[32] Gias and
COCOA: Cold Start Aware Capacity Planning for
Function-as-a-Service Platforms
[33] McGrath and
Serverless Computing: Design, Implementation, and
[34] Ustiugov et al. Benchmarking, Analysis, and Optimization of Serverless
Function Snapshots
2 Related Approaches 6
cloud offerings, with the aim to support a distributed FaaS architecture comprised of cloud,
fog and edge.
Another subarea are use case implementations for using FaaS platforms as execution envi-
ronment for simulations [25], cloud functions as part of an e-commerce system [26], building
microservice architectures with predictable performance [27] or checking the High Perfor-
mance Computing (HPC) capabilities of cloud function offerings compared to IaaS [28] or
against each other [29]. A map reduce use case was implemented by Giménez-Alventosa
and others [30]. Of particular interest is their data analysis where they grouped the invoca-
tions of the functions by the executing VM. In the resulting histogram, there were deviations
of up to approximately 30% in execution time. Since they missed to also record the physical
machine’s information, first of all the CPU specifications, the reason for this huge deviation
remains unanswered. When relating these measures with other experiments [14, 15, 16], one
explanation could be the different physical machines used for deploying the VMs and execut-
ing the cloud function. These insights were one reason to include the VM identification and
the hardware specification of the executing machine in my checklist and also in the function
trigger response discussed in Section 6.2.1.
The last group of papers optimized some aspects of FaaS platforms by making a custom
implementation or configuration. These aspects are also out of scope for implementing a
benchmark and simulation pipeline for assessing function performance and characteristics
on public FaaS platform offerings, but highlight shortcomings in today’s FaaS offerings.
Quaresma and others [31] built a simulator to predict their prior introduced suppression
of garbage collection during function execution and compare this to invocations on pub-
lic providers. The results presented there are hard to interpret since a lot of deployment
and configuration details were missing. Solving the startup of unnecessary instances in
over-provisioning scenarios are researched by Gias and Casale [32]. They implemented a
queuing based approach for on premise platforms which breaks the scaling on demand princi-
ple of FaaS. The next work [33] implemented a prototype in .NET deployed on Azure’s cloud
platform, compared it to public offerings and showed some benefits especially for throughput
(executions per second).
Noteworthy is the effort of Ustiugov and others [34]. Their major research goal is to
provide a serverless open source playground13 for experimentation on various layers of the
system stack like Firecracker hypervisor14 , which was open-sourced by AWS and used for
their production AWS Lambda environment.
SeMoDe tackles public cloud provider offerings and tries to understand their design, scheduling
and quality of service. Since the remaining 22 publications listed in Table 4 are more closely
related to these efforts, they are discussed in more detail. Figure 2 shows a distribution of
which platforms were investigated in the selected papers. Commonly, a few functions were
often used to research different aspects of FaaS namely matrix multiplication (5), hello world
functions (3), prime number search (3), integer factorization (2) and LINPACK (2).
My ordering of the platforms is similar to Scheuner and Leitner [20] where AWS Lambda
is the most investigated platformed followed by Azure Functions (AZF), Google Cloud Func-
tions (GCF) and lastly IBM OpenWhisk. Important for repeatable research is to enable
other researcher to reproduce the experiments. Only 17 out of the 22 publications have open
sourced their prototypes.
One of the first benchmark papers was published in 2018 [36] which includes all major cloud
2 Related Approaches 7
Table 4: Papers Related to SeMoDe Research Goals.
Ref Authors Title Year
[36] Back and An-
Using a Microbenchmark to Compare Function as a Ser-
vice Solutions
[37] Pellegrini et
Function-as-a-Service Benchmarking Framework 2019
[38] Somu et al. PanOpticon: A Comprehensive Benchmarking Tool for
Serverless Applications
[39] Jackson and
An Investigation of the Impact of Language Runtime on
the Performance and Cost of Serverless Functions
[40] Kuntsevich et
Demo Abstract: A Distributed Analysis and Bench-
marking Framework for Apache OpenWhisk Serverless
[41] Lee et al. Evaluation of Production Serverless Computing Envi-
[42] Martins et al. Benchmarking Serverless Computing Platforms 2020
[43] Kim and Lee FunctionBench: A Suite of Workloads for Serverless
Cloud Function Service
[44] Kim and Lee Practical Cloud Workloads for Serverless FaaS 2019
[45] Copik et al. SeBS: A Serverless Benchmark Suite for Function-as-a-
Service Computing
[46] Grambow et
BeFaaS: An Application-Centric Benchmarking Frame-
work for FaaS Platforms
[47] Bortolini and
Investigating Performance and Cost in Function-as-a-
Service Platforms
[48] Wang et al. Peeking Behind the Curtains of Serverless Platforms 2018
[49] Lloyd et al. Serverless Computing: An Investigation of Factors In-
fluencing Microservice Performance
[50] Pons and
Benchmarking Parallelism in FaaS Platforms 2020
[51] Kuhlenkamp
at al.
Benchmarking Elasticity of FaaS Platforms as a Foun-
dation for Objective-driven Design of Serverless Appli-
[52] Maissen et al. FaaSdom: a benchmark suite for serverless computing 2020
[53] Malawski et
Benchmarking Heterogeneous Cloud Functions 2017
[54] Pawlik et al. Performance evaluation of parallel cloud functions 2018
[55] Figiela et al. Performance evaluation of heterogeneous cloud func-
[56] Yu et al. Characterizing serverless platforms with serverlessbench 2020
[57] Mahmoudi and
SimFaaS: A Performance Simulator for Serverless Com-
puting Platforms
2 Related Approaches 8
AWS Lambda Google Cloud Function Azure Function IBM OpenWhisk OpenFaaS OpenWhisk (local deployment)
Occurence In Experiments
0 5 10 15 20
20 16 16 11 1 1
Figure 2: Number of experiments per provider.
providers namely AWS Lambda, GCF, AZF and IBM OpenWhisk. Their motivation was
to understand the maturity of different platforms as well as the relation of performance,
function settings and cost. The individual aspects are answered, but their inter-relations
were not approached in detail. Pellegrini and others [37] focused on measuring routing
properties which was done via a proxy function to understand the inner platform routing.
Since the implementation is not open sourced and due to some missing information about the
configuration of the experiment, the paper is only partly interpretable. Another work [38]
implements a tool for deploying function and other components to different providers. The
function deployment can also, as described in the paper, be done by using the serverless
framework15 which is also used by other papers included in this SLR, like [53, 36, 42, 51]. The
benefits over generic Infrastructure as Code (IaC) tools like Terraform16 or provider specific
like AWS CloudFormation17 are not clear since the preliminary results are not discussed in
Different languages and their effects on execution time were investigated by Jackson and
Clynch [39] for AWS Lambda and AZF. What is missing in this paper is the memory
setting for AWS Lambda, which has an influence on the execution time [9]. Another language
comparison is done by Kuntsevich and others [40] tackling a locally hosted OpenWhisk
installation. Due to the nature of the paper being a demo, they only included first results
on the execution time behavior for concurrent requests. Another concurrency study [41]
was motivated to compare FaaS to an IaaS VM offering. A data intensive application was
used to show the fit of FaaS for distributed computing where also other researchers see the
benefits of this computing model like Jonas and others [58]. A lot of execution data and its
analysis was published by Martins and others [42] which contains some noise. Especially
the Round Trip Time (RTT) is included in the data without clarification. Furthermore, the
HelloWorld use cases in different languages are limited in their interpretation since there are
constellations where a function running in Java is faster than the JS counterpart as shown
in [1]. Therefore, the use case respectively function characteristic is crucial to choose the right
programming language and platform. One experiment in Martin’s work is comparable to
other research, e.g. [59, 1, 15, 60, 61, 9], where a fibonacci function was executed at different
memory settings on different providers to understand the resource scaling strategies.
As argued before [9], microbenchmarking can unravel some platform mystery when designing
2 Related Approaches 9
experiments in a way so that a single aspect is isolated. For that reason, Kim and Lee [43,
44] published a benchmark suite comprised of microbenchmarking and application level
benchmarks as a foundation for experiments to discuss about common cloud functions and
applications in research. Nevertheless, they argued that microbenchmarking is important
but not sufficient for the requirements real world use cases present. Authors of other real
world experiments like SeBS [45] and BeFaaS [46] enforced that argument but are aware
that real world use cases are often not that precise in their conclusions due to the noise
of the collected data. However, they share insights on the adaptation of the technology.
SeBS [45] concludes that IO bound functions in general do not profit from cloud function
execution model due to the double billing problem [62]. They also included a cost analysis
and compared their real world examples to IaaS solutions. Despite beeing also real world
conform by implementing a webshop and traffic light use case, BeFaaS [46] has a different
focus. They focus on federated workloads where their traffic light application is comprised
of a cloud and edge layer. The latter is implemented by using their tinyFaaS [63] system.
Some information like the configurations for the cloud functions is missing and, therefore,
the comparison of different providers is limited.
An important aspect in FaaS research is to understand the resource scaling of the providers.
AWS Lambda claims that it “allocates CPU power in proportion to the amount of memory
configured”18. One study confirmed this statement in an ideal world with CPU intensive
functions [47] which corresponds to our findings [9]. Resource scaling and runtime prediction
is not that deterministic on the other platforms namely GCF and IBM OpenWhisk. Since
we already know that the physical host executing the function determines the resource as-
signment and ultimately the execution time, Wang and others [48] found a way to uniquely
identify VMs when executing the cloud functions. Their approach differs from the VM
uptime approach suggested by Lloyd and others [49] and is collision free. Back in 2018,
they recognized performance drops when a lot of function request were made concurrently,
leading to a placement strategy where AWS Lambda executes multiple cloud function on
the same VM. Therefore, performance isolation was not guaranteed. In 2020, Pons and
López [50] also tackled the request parallelism in FaaS, however, they did not recognize a
noisy neighbor effect in their data. A hint that the AWS Lambda platform improved in this
property which is also supported by the AWS Security19 documentation where the software
stack AWS Lambda is running on is depicted. They also discovered in their work that the
different scheduling strategies, how requests are distributed to instances, impact the fit of
different platforms for application classes like IO bound functions and AZF. Kuhlenkamp
and others [51] were interested in another scaling aspect. They created five workloads with
different characteristics to understand the scaling, i.e. the creation of new cloud function
instances. To distinguish between the execution time, understanding the network overhead,
request scheduling etc. at the platforms, they used the same measurement points as intro-
duced in Figure 13. Since they only executed a single function (prime number search) on
a single memory setting, their relation to SeMoDe - finding the optimal configuration for a
function - is limited, but related to understanding the impact of the application workload
on the number of instances [10].
As stated, deploying cloud functions can be done via custom tools like the serverless frame-
work or generic IaC tools like Terraform. One design decision of SeMoDe is to not rely on
these tools and implement the deployment and monitoring of the function in one compre-
2 Related Approaches 10
hensive tool. This gives the user the freedom to investigate special hypotheses and using
the deployment information for starting the benchmark and getting the logs from the dif-
ferent providers. The same approach was also chosen by Maissen and others [52] where
they showed the steps which are needed for deploying the cloud functions to the different
platforms, see Figure 4 in their publication. In SeMoDe, I implemented the same deploy-
ment steps. They also documented different processors and cold start times by executing
the cloud functions in various regions. Their application is also able to estimate the price
of the executed benchmarks based on the pricing schemes of the providers. Another set of
publications [53, 54, 55] were done by a research group from AGH University of Science and
Technology in Poland. Especially their hypotheses based approach inspired our work [1]
as well as the LINPACK use case, where pure performance of the cloud function based on
GFLOPS is measured which is used in our publications, too [2, 9]. They raised questions
about the computational performance proportional to function size, the network perfor-
mance, overhead introduced by network and scheduling, reusage of application instances
and their recycling time as well as the heterogeneous hardware. When they conducted
their research on AWS Lambda back in 2017, the service was limited to a single vCPU and
1536MB memory, but they already found a linear relationship as stated by the AWS Lambda
documentation. For other providers, they did not find that relation. When designing the
functionality of SeMoDe in 2018, this was a major reason to focus on AWS Lambda since the
linear scaling of resources is predictable and a prerequisite to build repeatable benchmarks
and simulation models. In an ideal world, for embarrassingly parallel computation, the ser-
vice implementation of AWS Lambda leads to a situation of constant cost when raising the
memory setting of the function as depicted in their cost calculation in Figure 12 in [55]. Or
to say it differently, a user gets the same portion of computing power over time for the same
price as also confirmed in our work [9]. Similar to Maissen, they also found different CPU
models executing the cloud function, which could also explain the scatter plot in Figure 5
in [55] (respectively Figure 4 in [53]), where two performance ranges for LINPACK were
visible. The last included benchmarking paper in the SLR is another workbench, called
ServerlessBench [56]. They included an experiment with an multi-threaded application com-
pared to parallel function instances. Since the memory configuration of the different cloud
functions included in the plots are missing, the results are only partly interpretable. Their
second implication is that splitting parallelizable parts of a function into several concurrently
executed functions is use case dependent. We showed [9] that an efficient fork join imple-
mentation of a prime number search can also benefit from multi-threading. Nevertheless,
their focus on splitting functions dependent on their resource needs, i.e. IO-bound function
and CPU-bound functions, is interesting for further investigation.
Most of the papers included in the SLR are benchmarking papers. Since the aim of SeMoDe
is to build a simulator based on benchmarking data, we explicitly included simulation as
a search term. Only two publications were identified with a strong focus on simulating
the FaaS offering. The first simulation paper was classified as out of scope of SeMoDe’s
capabilites by extending CloudSim [24]. The second published an interesting approach and
first ideas for a simulator focused on FaaS which simulates the scheduling, initialization etc.
of new instances were introduced by Mahmoudi and Khazaei [57]. They included a short
evaluation of their ideas and compared the results to platform data from AWS Lambda. They
especially focused on scaling strategies, i.e. scale by request, a fixed concurrency threshold
and metrics based scaling. SimFaaS predicts cold start probability, average response time,
rejection of request similar to our prior work [10]. The configuration of the function and its
tuning was out of scope of this work.
3 Checklist for FaaS Benchmarking 11
3 Checklist for FaaS Benchmarking
In the previous Section, I compared the different benchmarking approaches to argue which
aspects are important for a reproducible and interpretable experiment design. SeMoDe helps
in this case, since a lot of data, which is necessary to draw strong conclusions, is collected
due to the design and implementation of the prototype. A thorough documentation of
open source research tools is the enabler for interpreting results and reproducing them [64]
which motivates this work in general and the following checklist. Each item is attached
to some references, where details about the data extraction on the respective platform or
the methodology is described in detail. As stated when writing about the flaws of some
experiments, this checklist tries to help an experimenter to build a multi-dimensional data
set for FaaS benchmarking research.
Physical Machine Configuration - Obviously the physical machine and its performance
influence measurements. A lot of experimenters do not include this information in
their experiment description. The suggestion is to document at least the CPU model,
the model number and the Operating System (OS) [52, 53]. Also check the machine’s
performance where components of your benchmarking process are executed [2, 9].
VM/Container Identification - In the FaaS research area, most of the platforms are
designed with some virtualization layers. Note the executing VM or the container
configuration and use collision free approaches to identify the executing unit [48].
Function Configuration - The function configuration is often omitted in papers as stated
in the SLR. Since the resource scaling is determined by the configuration20, i.e. the
memory setting which influences the resource scaling at AWS Lambda and GCF, this
information is vital for classifying the results [9, 47].
Programming Language - The programming language has a big influence on the execu-
tion time of the function when thinking about the Just In Time (JIT) compiler of Java
compared to interpreted languages [1, 39, 40, 42].
Data Measurement Procedure - For the interpretation of the data, the measurement
methodology is important to state in the experiment like Figure 13 shows [1, 51].
Workload - Describe your workload and publish a distribution of it. This gives a first hint
on the instance parallelism the FaaS platform has to handle [10, 50, 51].
Cold/Warm Distinction - One of the major benefits of FaaS is scaling on demand. Since
this results in function instance creation and cold starts, it is important to state whether
the functions faced a cold start or whether the function instance was reused [1, 49].
Multithreaded Implementation - Due to the per-request execution model and the most
fine-grained billing model on millisecond basis, the implementation of cloud functions
directly influences the application/function characteristics which is different to e.g.
PaaS where single functionality in the scope of a cloud function is hidden within a
larger application/microservice. Therefore, the multithreading aspect when optimizing
performance and costs of cloud function is important to consider for FaaS experiments
20 AZF uses a different approach to select the configuration for the user when executing the function.
4 System Architecture 12
when using multi-core configurations [9, 56]. This is different to long running applica-
tions in a PaaS area where multi-threaded implementations of pieces of functionality
are suspended for code readability and maintenance which is also addressed by static
code quality tools21 .
4 System Architecture
SeMoDe is a full stack app implemented in Java. PostgreSQL22 is used as relational database
solution, Spring Boot23 as framework for the implementation of our application, Thymeleaf24
as a server side templating engine and Open API Specification25 to describe the implemented
REST API. Figure 326 shows the different layers of the app and references the related Sections
of this report in parentheses.
Database (4.1)
Business Logic (4.2, 6.2 & 6.3)
Thymeleaf (6.1) REST API (6.4) CLI (6.5)
Figure 3: Overall System Architecture of SeMoDe
4.1 Database Layout
The database layout shown in Figure 4 visualizes all tables and their attributes. Primary
keys are the first attribute of the specific entity layouted in bold. The italic and bold-faced
attributes are foreign keys. The cardinalities of the relationships are directed as implemented
in the Java Persistence API (JPA) mapping. They can be interpreted as follows:
Asetup config is associated with exactly one user, whereas a user can have 0 to N setup
The central table is setup_config which is associated with a user. Normally, new users
get the role USER but can request a role change by an administrator of the application.
Only the owner (associated user) or users with the role ADMIN can see and change the setup
configuration and all related entities. These are the only roles currently defined.
26 Logos for Thymeleaf, OAS, Spring Boot, and PostgreSQL are from the corresponding tool sites.
4 System Architecture 13
-provider_calibration_i d
1 1
Figure 4: Database Layout of SeMoDe
Furthermore, the setup configuration is associated with a single benchmarking and calibra-
tion configuration. Only the currently active configurations are associated and only these
are editable. If a change occurs, the tool increments the version number. Both tables, cal-
ibration_config and benchmark_config, contain a column setup_name which enables a
bidirectional relationsship. This is not implemented in JPA but helps in optimizing retrieval
operations for a single setup when using native queries, e.g. getting all versions for a specific
setup by only querying a single table.
A calibration configuration further has two self-references. One is named local_calibration
and the other is named provider_calibration. Both of them are needed to compare dif-
ferent virtualized environments with each other, a more detailed explanation can be found
in [9]. To enable this comparison, calibration_events are needed, which store the Giga
FLoating point Operations Per Second (GFLOPS) information for specific memory settings
or CPU quotas of the local environment. Sometimes misconfigured systems can be identified
where the performance scaling has an insufficient linear correlation when increasing CPU
resources as [2] shows and, therefore, cannot be used for the simulation approach proposed.
profile_records are the result of simulations and a decision basis for configuring cloud
function locally.
Conceptually, the CalibrationConfig contains a few objects in our implementation to en-
capsulate specific aspects, but the @Embedded JPA annotation is used to store the attributes
of these embedded classes in the same table for sake of simplicity, readability and speed of
database retrieval of the experiment configuration.
The BenchmarkConfig includes all provider related information which are necessary for
deployment. Currently SeMoDe is limited to AWS, but an extension for other providers is
work in progress. As for the calibration case, the provider related information are embedded
for sake of simplicity.
4 System Architecture 14
When conducting a benchmark experiment and invoking endpoints, the execution data is
stored in the corresponding logging service of the platform. In case of AWS, this service
is CloudWatch27. When we fetch the data from this service, we analyze it and store the
execution metrics in our performance_data table. Furthermore, we locally log the start and
end time of each request as well as a unique identifier (UUID). Those pieces of information,
together with the result of the platform (returned HTTP body from the API invocation) are
stored in the tables localrestevent and provider_event. Based on the unique identifier
(platform_id in provider_event), the tool is capable of matching the local REST event and
the provider execution data from the specific log handler and associate the provider events
with the corresponding performance data. In cases where, for example, the API gateway
timeout is reached or unforeseen errors occur, no local rest events respectively provider events
exists which limits the possibilities to investigate the client perceived overhead. However,
the data for investigating the platform can be stored in either way.
The database connection is configured via properties. For changing the default value which
assumes a running PostgreSQL at http://localhost:5432 with a postgres user secured
by admin password, all database related properties have to be altered.
4.2 Package Diagram
Under src/main/resources Spring Boot’s can be found where
the settings for all properties, e.g. database connection, logging levels, custom environment
variables etc. can be changed. The Thymeleaf HTML templates are stored in the templates
folder in the same directory together with the static content like css, images and javascript
The application code is located under src/main/java (gradle convention) and the classes
are placed in a package structure which will be explained in the following. This report only
describes the structure and some classes which are especially important for the architecture of
the prototype and highlights design decisions and extension points. The main package de.-
uniba.dsg.serverless contains the classes for starting the spring boot application. Since
I implemented a Spring web application, a web server (Tomcat) is started by default. To
suppress this and use our prototype as CLI tool, an ArgumentProcessor class is implemented
which needs another system property set at runtime. See the comment within the argument
processor class for further information on how to avoid starting the web server.
All other classes are included in four packages simulation.load,users,cli and pipeline.
The first subpackage simulation.load contains the implementation of a simulation. When
given an arbitrary load pattern and a simulation input (average execution time, cold start
time and container warm period), the tool computes the number of cold starts and running
instances at a given point [10]. The users subpackage contains the model, controller and
services for the user management of the application. Furthermore, it contains the security
configuration for the web controllers which is session-based via a custom login page and
a prior registration in the web frontend. The REST API on the other hand, is secured
on a request basis where a user has to authenticate upfront and receives a JSON Web
Token (JWT). The cli package contains a UtitlityFactory class where a builder pattern
is implemented. Via the name of the implemented CustomUtility, the specific function is
4 System Architecture 15
users simulation.load cli
Figure 5: Package Diagram of SeMoDe
executed. This is also a mechanism to allow for an easy extension of the CLI features and
test some functionality before integrating it into the pipeline as well as to provide stand
alone features.
The most important package is pipeline. It contains the business logic for the benchmark
and simulation pipeline. Before talking about these two packages, I will describe the other
subpackages. All JPA related interfaces are included in repo. Spring Data JPA is used as
on Object Relational Mapping (ORM) implementation. It generates the queries at compile
time based on a declarative, human readable approach28. Where this naming scheme is
insufficient for expressing queries, we use native queries and interface based Data Transfer
Object (DTO) projection29. In our model package, all configuration related classes are
included, see Figure 4. The benchmark.model and calibration.model classes contain the
specific business related model classes like performance_data or calibration_event known
from Figure 4. controller and service contain classes which are annotated with Spring
stereotype of the same name. The architectural decision is to have no direct access from
a controller to a repository class, so a controller class has only dependency injected service
objects, but no repository objects. Furthermore, the service classes do not contain any state
since the only source of truth is the database. Therefore, scaling and concurrency issues
introduced by the middleware do not occur in SeMoDe. The util package includes the
5 Setup 16
custom SeMoDeException as well as some static helper classes and Spring beans. As already
mentioned in the introduction to this Section, we also expose a REST API implemented in
the rest package. This package also contains subpackages for controllers, dtos, services
and a security implementation via JWT.
The missing two packages calibration and benchmark implement the centerpiece of Se-
MoDe. The prototype contains a few generic interfaces which are extension points to enable
the support for different FaaS platforms and establish a generic interface to deal with their
heterogeneity. Two of them are BenchmarkMethods and LogHandler in the benchmark pack-
age. The first is included in the methods subpackage, where currently only AWS is supported.
This interface defines methods for deploying, starting, fetching data and undeploying cloud
functions to keep classes like the BenchmarkExecutor vendor independent. The latter is
included in provider package where the major four cloud function platforms (AWS, Azure,
Google and IBM) are supported for investigating the logs and enable a vendor specific casting
of the log data to generate performance_data entries.
The third and last extension point is the interface CalibrationMethods in calibration.-
provider. This mechanism is similar to the benchmark extension at BenchmarkMethods.
Here, AWSCalibartion and LocalCalibration implement this interface to deploy, start and
undeploy the calibration. A local deployment is of course not necessary since we execute
a Docker container to get the calibration_events. The other subpackages deal with dif-
ferent steps in the calibration and simulation pipeline. local contains the classes for the
local calibration of a machine’s hardware based on the execution of Docker containers and
a calibration method. mapping contains the statistical evaluation of the tool with linear re-
gression models, computation of the coefficient of determination (R2) and a MappingMaster
to compare different environments. profiling as the last subpackage of calibration, in-
cludes the classes for the local execution and simulation of a function based on the mapping
information from the computed linear models.
5 Setup
5.1 System Setup
Since the prototype is implemented in Java, we need an installed Java Runtime Environ-
ment (JRE) version 11 or higher. Furthermore, to run the PostgreSQL database and use
the calibration feature, we have to install Docker30 since our local simulation approach will
highly depend on Docker. Since we use the Java API client for Docker31, a user has to set
the host and port, DOCKER_HOST system property, and the DOCKER_TLS_VERIFY property to
false. This is only relevant for users under Windows since the system defaults support is
tied to Linux. The last setup step is to install npm and run npm install in src/main/re-
6 Supported Functionality 17
5.2 Provider Setup
Since only AWS is supported at time of writing, I will explain the steps which are needed to
get the interaction with AWS work in the following.
1. Install the AWS CLI and configure it with a public and private key32. This is a pre-
requisite to ensure the prototype has access to the AWS services and can deploy cloud
functions, invoke functions via gateways and read logs. The best way to do this is to
create another user in the AWS Identity and Access Management (IAM) console and
use this user and its credentials for the prototype invocations. It needs at least access
to S3, ApiGateway, Lambda and CloudWatch.
2. You have to create a new IAM role for your executing lambda functions to specify the
permissions, which the Lambda functions need. The following AWS managed policies
are attached to this execution role:
5.3 System Startup
I specified a docker-compose file for the database container. To start up the container,
run the docker-compose up command within the root directory of the application. Do not
start SeMoDe within a container since we use the running Docker environment in the course
of the simulation pipeline and need full access to the system. Due to the shared database33,
a configuration of our prototype respectively the experiments on one machine with a web
browser and a second machine (preferably a Linux server) for running the experiments is
desirable. Without other custom applications running on this Linux server, results can be
derived as clean as possible.
To start the application, run the gradlew bootRun command. This automatically downloads
the gradle distribution used and executes the wrapper to start the application.
6 Supported Functionality
This section explains the supported functionality and highlights some important design ideas,
classes, code and publications where SeMoDe’s capabilities contribute to the technical real-
ization of the presented ideas.
33 Change the and especially the spring.jpa.datasource.* properties when you
want to integrate over the database.
6 Supported Functionality 18
6.1 Web Interface
6.1.1 Pipeline Configuration
One important functionality is the pipeline configuration. Since I implemented a Spring Boot
Web application, a configuration is possible via the web browser. As already mentioned, I use
Thymeleaf as a HTML templating engine. In the following we will see different screenshots
of this web interface together with a short explanation of the various input fields.
Setup Configuration
Only visible
for admins
Only visible
for logged
in users
Column is
only visible
for admins
Figure 6: Setups Page to See Existing Setups and Create Them
There is a dualism in the way the pipeline setup will be configured. This is also visible in
Figure 4 where two configuration classes dominate the database layout or Figure 5 which
shows that business logic is mainly implemented in the two packages benchmark and cal-
ibration. Figure 6 depicts the main setup page, where new configurations can be created.
The screenshot shows the application when a user is logged in. We use Spring Security and
especially the Thymeleaf security integration to decide - based on the role - which elements
of the page will be rendered. In this case, the logged in user deployer has also the adminis-
trator role and can therefore see the Users hyperlink34 in the navbar and the Owner column.
Via the binoculars, a user can go to the benchmark configuration page and via the tools icon
to the calibration detail page.
6 Supported Functionality 19
Figure 7: Benchmark Configuration Page I
6 Supported Functionality 20
Benchmark Configuration
Figures 7 and 8 show the screenshots for the setups/{setupName}/benchmark35 endpoint.
The first screenshot contains all editable input fields to configure the benchmark. Via the
Versions dropdown, a user can see the configuration of older versions. Technically, asyn-
chronous requests via JavaScript (AJAX ) are made to an endpoint to get the benchmark
configuration based on the selected version. Only the latest version is editable for the input
fields, despite the description and version visible fields, which are editable for every version.
The dropdown information is structured as follows:
“{version} - Platform events: {perfromance data entries} (database id)”
The number of performance data entries in the database indicate if a benchmark was executed
based on this version and if the data was fetched from the corresponding provider. The
performance entries and especially the memory_used and precise_duration are later used
for displaying the data graphically as can be seen in Figure 8. The primary key of the
benchmark configuration is also displayed in parenthesis for an in-depth investigation and
custom queries when accessing the database as an administrator.
A custom Description is the next input field. This can be used to make notes about the
configuration during the experiment planning and execution, but also for publicly marked
experiments when the field Version Visible is set. Then a non-authenticated user can see a
subset of the information presented here, more details about the publicly available website
will be given in a subsequent Section.
The following three fields are connected to each other. Different Benchmarking Modes are
implemented to support clean test beds as well as arbitrary load patterns. Further details
on the benchmarking modes implemented and the load pattern generation can be found in
Section 6.2.2. Based on the selected mode, different Benchmarking Parameters have to be
specified to define the invocation process. Since the prototype is currently only capable of
invoking REST endpoints, we added some Post Body Arguments in JSON format as input
for the cloud function. When a benchmark is executed, the experiment start and end time
is set, displayed at Experiment Time and overwritten with null values when the next version
is created and stored in database.
As mentioned before, currently only AWS is supported. Therefore, in the next paragraph
of the website, the AWS specific function configuration is set. The first fields are self-
explanatory, where the Description/Function Name is added to the overall description. This
enables the user to describe the function in this context. The AWS Amazon Resource Name
(ARN) Lambda Role is the identifier of the created role presented in Section 5.2. All other
fields are similar to the fields at the AWS dashboard where you can also use the ZIP upload
Deployment Internals are the next paragraph and completely filled and cleared by the tool,
see Figure 8. This procedure enables a check and possible intervention of the experimenter
when some components malfunction. It is furthermore needed to undeploy the deployed
function at a later point in time. Via the Update Configuration button, a user of the tool
can store a new version of the configuration. It is disabled in the screenshot since version 47
34 An explanation of the user’s page is done in Appendix A.
35 I use a curly bracket semantics in the style of specifying controller paths in Spring Web for dynamically
changing fields.
6 Supported Functionality 21
Figure 8: Benchmark Configuration Page II
6 Supported Functionality 22
is not the most recent version, but a version with Benchmark Points as can be seen by the
diagram. Whenever a version with associated performance data is selected, a new set of
data points is included in the diagram where the last version data can also be removed via
the Remove Dataset button. Also the actions Deploy,Undeploy and Execute Benchmark
are disabled. Fetch Data from a provider’s logging service like AWS CloudWatch is possible
when an experimenter forgot to do so, but already fetched data cannot be fetched again.
Calibration Configuration
Figures 9, 10 and 11 show screenshots for the setups/{setupName}/calibration endpoint.
The structure of the calibration webpage is similar to the benchmark webpage described in
the previous Section. Due to this similarity, I will only describe new fields and elements.
Local Configuration section includes the configuration for the local experiment machine and
the upfront calibration of the used hardware. The Local Steps property describes the re-
source increase for the start of a Docker container, whose location is specified in the Docker
Source Folder property. This CPU resource limitation is done via the cpus command line
argument when starting a container. The Number of local calibrations describes how often
the calibration is executed on every Local Steps setting. A result of this calibration can be
seen in Figure 10 in the first diagram.
In the AWS configuration, the only change is the Bucket name where the output of the
calibration function should be stored. The mechanism here is different to the benchmark-
ing example since the API Gateway runs into a timeout after 30 seconds and cannot get
the response directly from the platform. Additionally, the LogHandler for AWS does not
parse the execution data of the calibration function. Therefore, the result of the calibration
on AWS Lambda is fetched from this bucket and analyzed via the LinpackParser class.
Linpack [12, 13] is currently the only option to calibrate the hardware stacks and sample
function are included in the functions folder of our prototype36.
The next paragraph in the webpage is a mapping step from the local calibration (and there-
fore the local machine stack) and the provider calibration. Based on the two calibrations,
the tool computes two linear regressions for every architectural stack. It equals the two
equations and computes local CPU equivalents for defined platform configuration as seen
in the Memory Sizes on Provider Platform field. It is possible due to the two dropdown
fields to select different configurations, which is especially interesting when changing BIOS
or kernel settings to influence the CPU frequency scaling [2], the algorithm used to balance
performance and power consumption or turbo boost options to name only a few. Currently,
selected calibrations are displayed in brackets and their data is used for generating the two
diagrams. The Machine Configuration paragraph is for documentation purposes to describe
fair and repeatable benchmarks with as much information as possible.
In Figure 11, a user describes the Folder where the source code of the function under test,
which is a candidate for a cloud function deployment, is located. Environment Variables can
be passed by a file to SeMoDe which sets the variables when starting the containers. The
Number of Profiles for every memory size configured in the previous screenshot is set. Via
the dropdown Previous Profiles, a user can select a profile and sees also the version number
of the calibration. This enables a user to select the version dropdown in Figure 9 to see the
configuration yielded to this profile. The buttons are similar to the previous benchmarking
6 Supported Functionality 23
Figure 9: Calibration Configuration Page I
6 Supported Functionality 24
Figure 10: Calibration Configuration Page II
6 Supported Functionality 25
Figure 11: Calibration Configuration Page III
6 Supported Functionality 26
case. Deploy Calibration,Start Calibration and Undeploy Calibration are referred to AWS.
All other buttons should be self explanatory via their labeling.
6.1.2 Visualizing Execution Data
In Figure 7, there was a field which marks the configuration and the benchmarking results
as publicly available. Figure 12 shows the condensed benchmarking information and the
possibility to download the data (via the Download button) for further analysis.
6.2 Benchmarking Cloud Function Platforms
6.2.1 Implementing a Function under Benchmark
First discussed in the database layout, the function under benchmark is invoked via syn-
chronous HTTP calls. To assess the user perceived performance, the start and end time on
the user side are logged as depicted in Figure 13. This is only possible when the API gateway
does not timeout. In case of AWS, the timeout is about 30 seconds. If the request times out,
the invocation succeeds and the start time is logged but the end time is missing as well as
the provider_event. In this case only the performance_data information is present when
fetching the data. This is important to have in mind when designing experiments.
In Figure 13, four invocations are presented, where the orange part is the duration perceived
on the user side and the blue period is the execution time on the platform. This enables
an assessment of round trip times and routing/processing on the FaaS platform. We imple-
mented a custom response to enable a mapping of the local and provider data which will be
discussed later on. This custom response includes all fields of the provider_event table or
ProviderEvent class except for the id field. Especially the CPU information is important
to deal with the heterogeneity of hardware at different regions of a FaaS provider as other
research has shown performance variations, see [14, 15, 16].
6.2.2 Benchmarking Modes
As already shown in Figure 7, there is a dropdown for selecting the benchmarking mode.
The tool has five benchmark modes. The first four modes are artificially created to draw
strong conclusions when conducting experiments. The last option is for real world traces
as described in the following enumeration. For each mode, the mandatory attributes are
described and can be configured in the Benchmarking Parameters input field, see also Fig-
ure 7:
Mode concurrent - Invoking the function under test once in parallel by executing N
1. Number of requests.
Mode sequentialInterval - Sequentially triggering a function with a fixed time
interval between the starting point of the corresponding function invocations. This
6 Supported Functionality 27
Figure 12: Benchmark Publicly Visible Page
6 Supported Functionality 28
Local REST
FaaS Platform time
1 min 29 min 1 min 29 min
Figure 13: User and Platform Perceived Performance (based on Figure 1 in [1])
can lead to situations, where the invocations are not sequential because the execution
time is greater than the interval. In such a case, the platform instantiates further cloud
functions to serve the requests.
1. Number of requests.
2. Seconds between the request execution start times.
Mode sequentialConcurrent - Combines the previous two invocations modes.
1. Number of executions groups.
2. Number of requests in each group.
3. Delay between termination of group g and start of group g+1 in seconds.
Mode sequentialChangingInterval - This mode triggers functions in an interval
with varying delays between execution start times in a round robin fashion.
1. Number of requests.
2. List of delays.
Mode arbitraryLoadPattern - The function endpoints are triggered based on a csv
file. The file contains only one column and for each row a double value with a relative
timestamp compared to the actual time when the generation is started, e.g. 0 means
now and 5 means now + 5 seconds. This value indicates when a specific REST call,
invoking the cloud function at the platform, should be submitted.
1. File path of the csv load pattern file.
SeMoDe generates a csv file for the first four modes via the LoadPatternGenerator class.
This file is located in the folder setups/setupName/benchmark/loadPattern.csv and over-
written when further benchmarks are executed based on the same configuration.
6.2.3 Submitting Requests based on Arbitrary Load Pattern
Listing 1 contains the centerpiece of the benchmark invocation process. Since the prototype
generically implements the BenchmarkMethods interface, I pass a list of deployed methods
with their endpoints to this function. For the invocation, I implemented two executor services
which handle the concurrent execution of tasks via thread pools. The reason for these two
executors is, that we want to schedule requests based on our generated load pattern. This is
only possible with a ScheduledExecutorService37 , but this executor service is limited in the
number of concurrent threads which are determined by the factory method’s corePoolSize
attribute. Since I do not know the concurrency level a priori due to the arbitrary load
6 Supported Functionality 29
pattern and function execution time, the core pool size cannot be determined. The problem
is, that there is no runtime error when the scheduled requests exceed the core pool size, but
the requests are then queued by the work queue of the thread pool and not executed on the
time specified by the load pattern since SeMoDe requests the cloud function in a synchronous
way and therefore the executing thread blocks (IO wait).
Listing 1: Centerpiece of the BenchmarkExecutor Class
1public List<LocalRESTEvent> executeBenchmark(final List<BenchmarkMethods>
benchmarkMethodsFromConfig) {
3final List<Double> timestamps = this.loadLoadPatternFromFile();
5final ScheduledExecutorService executor = Executors.newScheduledThreadPool(processors());
6final ExecutorService delegator = Executors.newCachedThreadPool();
8final List<Future<LocalRESTEvent>> responses = new CopyOnWriteArrayList<>();
10 long tmpTimestamp = 0;
11 // for each provider
12 for (final BenchmarkMethods benchmarkMethods : benchmarkMethodsFromConfig) {
13 if (benchmarkMethods.isInitialized()) {
14 // for each function
15 for (final String functionEndpoint : benchmarkMethods.getUrlEndpointsOnPlatform()) {
16 // for each timestamp
17 for (final double timestamp : timestamps) {
18 tmpTimestamp = (long) (timestamp 1000);
19 final FunctionTrigger f = new FunctionTrigger(benchmarkMethods.getPlatform(),
benchmarkConfig.getPostArgument(), new URL(functionEndpoint));
20 FunctionTriggerWrapper fWrapper = new FunctionTriggerWrapper(delegator, responses, f);
21 executor.schedule(fWrapper, tmpTimestamp, TimeUnit.MILLISECONDS);
22 }
23 }
24 }
25 }
27 // Shut down the first scheduled service . This means that all wrapper function trigger tasks are
28 // run and the function trigger tasks are submitted.
29 // Time to wait is the last timestamp from now on executing a function.
30 this.shutdownExecService(executor, tmpTimestamp + 30_000);
31 // Wait for the function trigger tasks to terminate (300 seconds timeout)
32 this.shutdownExecService(delegator, tmpTimestamp + 300_000);
34 // collecting the custom responses from the cloud platform
35 List<LocalRESTEvent> events = new ArrayList<>();
36 for (Future<LocalRESTEvent> futureEvent : responses) {
37 events.add(futureEvent.get());
38 }
39 return events;
40 }
This is the reason why executor is only used for asynchronously starting the requests which
is a millisecond task. The synchronous execution is done by a CachedThreadPool38 named
delegator in the snippet which dynamically scales the number of threads in its thread pool.
In line 19, the function trigger, a callable which synchronously executes the HTTP request,
is created. This callable is wrapped in a runnable in line 20, which also gets a reference to the
delegator (the autoscaling executor service) as well as a reference to the thread-safe list where
the Futures39 (handle to get the result of the computation later on) are collected. In line 21,
6 Supported Functionality 30
the executor schedules the wrapped function triggers. Dependent on the start time of the
scheduled callable, it executes a call to the delegator which executes the FunctionTrigger.
After submitting the last request, the scheduled executor service terminates immediately in
line 30. The call in line 32 blocks until the last synchronous invocation returns. It waits the
timeout period for the cloud function to be sure that all functions can terminate correctly on
the selected platform. After the execution, the futures and especially the results are added
to the events list in 37. In the following they are stored in the database.
SeMoDe is dependent on the system resources and the number of threads a system can handle.
Another limiting factor is the amount of memory the system is capable of for the number of
concurrent functions which can be executed in parallel. In this aspect, the implementation
is similar to other load testing tools like JMeter40.
6.3 Simulating Local Cloud Function Executions
6.3.1 Calibrating Hardware
0 1 2 3 4
0 20 40 60 80
Intel i7-2600, Model 42
3.4 GHz - 3.8 GHz
Linux Kernel 5.4.0-65
CPU Quota
0 1 2 3 4
0 50 100 150 200
Intel i7-7700, Model 158
3.6 GHz - 3.9 GHz
Linux Kernel 5.4.0-70
CPU Quota
Figure 14: CPU performance computed by LINPACK at different CPU Quotas (based on
Figure 1 in [2])
During the research on a simulation and benchmarking pipeline for cloud functions, I discov-
ered an interesting non-linear scaling of CPU performance [2]. An execution of LINPACK
showed the behavior as shown in Figure 14. GFLOPS are computed upon the LINPACK
result which is parsed by the LinpackParser. Detailed data is stored in the folder /setups/-
{setupName}/calibration. At this location, the output of the LINPACK computation is
stored, too. As already said, the tool artificially increase the CPU share/quota by using
Docker’s cgroup mechanism via the cpus parameter. Lilja [17] described Linearity as a
6 Supported Functionality 31
characteristic of a good performance metric. To achieve this linearity and detect cases as
in the right side of Figure 14, I implemented a Hardware Calibration Feature41. This
feature computes a linear regression and the R2which shows whether the performance scales
linearly on the corresponding local machine. If this is not the case, as for the i7-7700 ma-
chine, a user has to adjust the kernel and/or bios settings of the machine to enable a linear
scaling of CPU resources which can be checked again with the calibration feature. Otherwise
the calibration data cannot be used for the simulation approach since the performance is
unpredictable for some utilization levels, e.g. for 2.9 to 3.4 CPU quotas at i7-7700.
If the calibration is executed as part of an experiment, a user can also see the data points
as shown in Figure 10 (second diagram). The tool includes two properties as described
in the README where a user of the tool can specify the number of runs for each CPU
quota and the steps in which the CPU quota is incremented. The default values are 1 run
(runs) with steps of 0.1 (steps). A single run is sufficient to get a confirmation through
the statistical output if the compute performance scaling is appropriate for conducting the
simulation experiments.
6.3.2 Simulation based on Calibration
The calibration information is used to formulate an equation to compute the CPU quota
on a local machine based on a potentially interesting memory setting on the cloud provider
to predict the execution behavior of the function under test. The results computed there
are now used in our ContainerExecutor class when starting the containers with the correct
CPU quota. As a result of the container execution, the information are parsed, saved in
files at /setups/{setupName}/calibration/profiles and stored as ProfileRecords in
the database. These records are then displayed at the web interface, see Figure 11, and
give a first hint whether the cloud function would profit proportionally from an resource
increase on the platform. The interested reader is referenced to our paper on Optimizing
cloud function configuration via local simulations [9] where a statistical evaluation of the
described local simulation and prediction is made. This paper also gives the answer to our
first statement S1 where a novel dev-prod parity concept is introduced.
6.4.1 REST API Security
As mentioned in Section 4.2, the basic security configuration is session based, see Securi-
tyConfig.WebSecurityAdapter class. For the REST API, I included another Configura-
tion class (SecurityConfig.ApiSecurityAdapter) to handle requests and secure them via
JWTs42. Therefore, I implemented a custom filter class (JwtAuthenticationTokenFilter)
and registered it in the pipe and filter security architecture of Spring Boot Security. A user
has to send an AuthenticationRequest to the endpoint /api/login with the username and
password. The tool generates a JWTTokenResponse with a Bearer token. In the following,
the user has to include this token in every request as an authorization header. After some
6 Supported Functionality 32
time (specified via the jwt.expiration property), the token expires and the user has to
resend the authentication to get a new token.
6.4.2 Exposed REST API Endpoints
Figure 15: OAS for exposed REST API.
Figure 15 shows the Swagger UI displaying the OAS of our prototype. It highlights, indicated
by the lock, the authorization needed as described in the previous Section. At the /swagger
endpoint, the interested reader can access OAS and the benchmark configurations which are
publicly available via the /benchmarks endpoint. The REST API is an additional option to
access the data and at an early stage.
6.5 CLI Features
Since parts of the overall benchmarking and simulation pipeline consume hours or even days
to complete, I implemented a CLI feature to run the application on the corresponding Linux
machines and to avoid using the web browser since the experiment machines are normally
based on a server image without a graphical user interface.
7 Conclusion and Future Work 33
The command in Listing 2 shows the invocation of the java application. It is vital to set the
property spring.main.web-application-type to NONE here to suppress Spring’s default
behavior to start a web server (TomCat). The ampersand detaches the process from the
console and via > out.txt the standard output is written to the file out.txt. This enables
an error handling and investigation of details when running the prototype.
Listing 2: Start SeMoDe as CLI application
1$ java jar Dspring.main.webapplicationtype=NONE app.jar & > out.txt
7 Conclusion and Future Work
In this technical report, I documented SeMoDe, a research prototype for benchmarking cloud
functions and simulating their local behavior to predict the runtime behavior of function
in the cloud locally. Furthermore, I performed an SLR which gives insights in the current
state of FaaS benchmarking and simulation approaches and led to a condensed checklist to
improve the quality of the documentation and insights of research in this field.
Since the current implementation is only tackling a single function in isolation by conduct-
ing microbenchmarks which is often a main factor of criticism, I plan to extend the scope
of the simulation component to small architectures comprising typical interacting services
like databases, gateways and message queues. A first idea is to use docker-compose43 for
orchestrating the solution and executing the different services. Another aspect for future
work is to extend the publicly available experiments and publish them on our prototypes
website44 to support S3.
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A User Management 38
Many thanks to Tobias Heckel and Martin Endreß contributing to the research prototype. It
was a lot of fun to do a research project in winter term 2018/2019 with you and also writing
the cold start paper ([1]).
Also Martin’s bachelor thesis investigated an interesting aspect, how we can compare different
execution environments and use this information to predict to some extent the execution
behavior on machine A while executing the function on machine B. Part of his work is also
included in the calibration, simulation and mapping part of the research prototype. The
interested reader is referred to our IEEE CLOUD publication ([9]).
A User Management
Administrator can change the role of
every registered user from admin to
user and vice versa.
Administrator can change the user password. Passwords have to
correspond to regular expression,
Figure 16: Users Page for Administrators to Change Roles and Passwords.
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Nr. 10 (1992) Sinz E.J.: Datenmodellierung im Strukturierten Entity-Relationship-Modell
Nr. 11 (1992) Ferstl O.K., Sinz E. J.: Glossar zum Begrisystem des Semantischen Objektmo-
Nr. 12 (1992) Sinz E. J., Popp K.M.: Zur Ableitung der Grobstruktur des konzeptuellen Schemas
aus dem Modell der betrieblichen Diskurswelt
Nr. 13 (1992) Esswein W., Locarek H.: Objektorientierte Programmierung mit dem Objekt-Rol-
Nr. 14 (1992) Esswein W.: Das Rollenmodell der Organsiation: Die Berücksichtigung aufbauor-
ganisatorische Regelungen in Unternehmensmodellen
Nr. 15 (1992) Schwab H. J.: EISREVU-Modellierungssystem. Benutzerhandbuch
Nr. 16 (1992) Schwab K.: Die Implementierung eines relationalen DBMS nach dem
Nr. 17 (1993) Schwab K.: Konzeption, Entwicklung und Implementierung eines computerge-
stützten Bürovorgangssystems zur Modellierung von Vorgangsklassen und Ab-
wicklung und Überwachung von Vorgängen. Dissertation
List of previous University of Bamberg reports 39
List of previous University of Bamberg reports
Nr. 18 (1993) Ferstl O.K., Sinz E.J.: Der Modellierungsansatz des Semantischen Objektmodells
Nr. 19 (1994) Ferstl O.K., Sinz E.J., Amberg M., Hagemann U., Malischewski C.: Tool-Based
Business Process Modeling Using the SOM Approach
Nr. 20 (1994) Ferstl O.K., Sinz E.J.: From Business Process Modeling to the Specication of
Distributed Business Application Systems - An Object-Oriented Approach -. 1st
edition, June 1994
Ferstl O.K., Sinz E.J. : Multi-Layered Development of Business Process Models
and Distributed Business Application Systems - An Object-Oriented Approach -.
2nd edition, November 1994
Nr. 21 (1994) Ferstl O.K., Sinz E.J.: Der Ansatz des Semantischen Objektmodells zur Modellie-
rung von Geschäftsprozessen
Nr. 22 (1994) Augsburger W., Schwab K.: Using Formalism and Semi-Formal Constructs for
Modeling Information Systems
Nr. 23 (1994) Ferstl O.K., Hagemann U.: Simulation hierarischer objekt- und transaktionsorien-
tierter Modelle
Nr. 24 (1994) Sinz E.J.: Das Informationssystem der Universität als Instrument zur zielgerichte-
ten Lenkung von Universitätsprozessen
Nr. 25 (1994) Wittke M., Mekinic, G.: Kooperierende Informationsräume. Ein Ansatz für ver-
teilte Führungsinformationssysteme
Nr. 26 (1995) Ferstl O.K., Sinz E.J.: Re-Engineering von Geschäftsprozessen auf der Grundlage
des SOM-Ansatzes
Nr. 27 (1995) Ferstl, O.K., Mannmeusel, Th.: Dezentrale Produktionslenkung. Erscheint in CIM-
Management 3/1995
Nr. 28 (1995) Ludwig, H., Schwab, K.: Integrating cooperation systems: an event-based approach
Nr. 30 (1995) Augsburger W., Ludwig H., Schwab K.: Koordinationsmethoden und -werkzeuge
bei der computergestützten kooperativen Arbeit
Nr. 31 (1995) Ferstl O.K., Mannmeusel T.: Gestaltung industrieller Geschäftsprozesse
Nr. 32 (1995) Gunzenhäuser R., Duske A., Ferstl O.K., Ludwig H., Mekinic G., Rieder H.,
Schwab H.-J., Schwab K., Sinz E.J., Wittke M: Festschrift zum 60. Geburtstag von
Walter Augsburger
Nr. 33 (1995) Sinz, E.J.: Kann das Geschäftsprozeßmodell der Unternehmung das unterneh-
mensweite Datenschema ablösen?
Nr. 34 (1995) Sinz E.J.: Ansätze zur fachlichen Modellierung betrieblicher Informationssysteme -
Entwicklung, aktueller Stand und Trends -
Nr. 35 (1995) Sinz E.J.: Serviceorientierung der Hochschulverwaltung und ihre Unterstützung
durch workow-orientierte Anwendungssysteme
Nr. 36 (1996) Ferstl O.K., Sinz, E.J., Amberg M.: Stichwörter zum Fachgebiet Wirtschaftsinfor-
matik. Erscheint in: Broy M., Spaniol O. (Hrsg.): Lexikon Informatik und Kom -
munikationstechnik, 2. Auage, VDI-Verlag, Düsseldorf 1996
List of previous University of Bamberg reports 40
Nr. 37 (1996) Ferstl O.K., Sinz E.J.: Flexible Organizations Through Object-oriented and Trans-
action-oriented Information Systems, July 1996
Nr. 38 (1996) Ferstl O.K., Schäfer R.: Eine Lernumgebung für die betriebliche Aus- und Weiter-
bildung on demand, Juli 1996
Nr. 39 (1996) Hazebrouck J.-P.: Einsatzpotentiale von Fuzzy-Logic im Strategischen Manage-
ment dargestellt an Fuzzy-System-Konzepten für Portfolio-Ansätze
Nr. 40 (1997) Sinz E.J.: Architektur betrieblicher Informationssysteme. In: Rechenberg P., Pom-
berger G. (Hrsg.): Handbuch der Informatik, Hanser-Verlag, München 1997
Nr. 41 (1997) Sinz E.J.: Analyse und Gestaltung universitärer Geschäftsprozesse und Anwen-
dungssysteme. Angenommen für: Informatik ’97. Informatik als Innovationsmotor.
27. Jahrestagung der Gesellschaft für Informatik, Aachen 24.-26.9.1997
Nr. 42 (1997) Ferstl O.K., Sinz E.J., Hammel C., Schlitt M., Wolf S.: Application Objects
fachliche Bausteine für die Entwicklung komponentenbasierter Anwendungssy-
steme. Angenommen für: HMD Theorie und Praxis der Wirtschaftsinformatik.
Schwerpunkheft ComponentWare, 1997
Nr. 43 (1997): Ferstl O.K., Sinz E.J.: Modeling of Business Systems Using the Semantic Object
Model (SOM) A Methodological Framework - . Accepted for: P. Bernus, K.
Mertins, and G. Schmidt (ed.): Handbook on Architectures of Information Systems.
International Handbook on Information Systems, edited by Bernus P., Blazewicz
J., Schmidt G., and Shaw M., Volume I, Springer 1997
Ferstl O.K., Sinz E.J.: Modeling of Business Systems Using (SOM), 2nd Edition.
Appears in: P. Bernus, K. Mertins, and G. Schmidt (ed.): Handbook on Architectu-
res of Information Systems. International Handbook on Information Systems, edi-
ted by Bernus P., Blazewicz J., Schmidt G., and Shaw M., Volume I, Springer
Nr. 44 (1997) Ferstl O.K., Schmitz K.: Zur Nutzung von Hypertextkonzepten in Lernumgebun-
gen. In: Conradi H., Kreutz R., Spitzer K. (Hrsg.): CBT in der MedizinMetho-
den, Techniken, Anwendungen -. Proceedings zum Workshop in Aachen 6. 7.
Juni 1997. 1. Auage Aachen: Verlag der Augustinus Buchhandlung
Nr. 45 (1998) Ferstl O.K.: Datenkommunikation. In. Schulte Ch. (Hrsg.): Lexikon der Logistik,
Oldenbourg-Verlag, München 1998
Nr. 46 (1998) Sinz E.J.: Prozeßgestaltung und Prozeßunterstützung im Prüfungswesen. Erschie-
nen in: Proceedings Workshop „Informationssysteme für das Hochschulmanage-
ment“. Aachen, September 1997
Nr. 47 (1998) Sinz, E.J.:, Wismans B.: Das „Elektronische Prüfungsamt“. Erscheint in: Wirt -
schaftswissenschaftliches Studium WiSt, 1998
Nr. 48 (1998) Haase, O., Henrich, A.: A Hybrid Respresentation of Vague Collections for Distri-
buted Object Management Systems. Erscheint in: IEEE Transactions on Know-
ledge and Data Engineering
Nr. 49 (1998) Henrich, A.: Applying Document Retrieval Techniques in Software Engineering
Environments. In: Proc. International Conference on Database and Expert Systems
List of previous University of Bamberg reports 41
Applications. (DEXA 98), Vienna, Austria, Aug. 98, pp. 240-249, Springer, Lec -
ture Notes in Computer Sciences, No. 1460
Nr. 50 (1999) Henrich, A., Jamin, S.: On the Optimization of Queries containing Regular Path
Expressions. Erscheint in: Proceedings of the Fourth Workshop on Next Genera-
tion Information Technologies and Systems (NGITS’99), Zikhron-Yaakov, Israel,
July, 1999 (Springer, Lecture Notes)
Nr. 51 (1999) Haase O., Henrich, A.: A Closed Approach to Vague Collections in Partly Inacces-
sible Distributed Databases. Erscheint in: Proceedings of the Third East-European
Conference on Advances in Databases and Information Systems – ADBIS’99, Ma-
ribor, Slovenia, September 1999 (Springer, Lecture Notes in Computer Science)
Nr. 52 (1999) Sinz E.J., Böhnlein M., Ulbrich-vom Ende A.: Konzeption eines Data Warehouse-
Systems für Hochschulen. Angenommen für: Workshop „Unternehmen Hoch-
schule“ im Rahmen der 29. Jahrestagung der Gesellschaft für Informatik, Pader-
born, 6. Oktober 1999
Nr. 53 (1999) Sinz E.J.: Konstruktion von Informationssystemen. Der Beitrag wurde in geringfü-
gig modizierter Fassung angenommen für: Rechenberg P., Pomberger G. (Hrsg.):
Informatik-Handbuch. 2., aktualisierte und erweiterte Auage, Hanser, München
Nr. 54 (1999) Herda N., Janson A., Reif M., Schindler T., Augsburger W.: Entwicklung des In-
tranets SPICE: Erfahrungsbericht einer Praxiskooperation.
Nr. 55 (2000) Böhnlein M., Ulbrich-vom Ende A.: Grundlagen des Data Warehousing.
Modellierung und Architektur
Nr. 56 (2000) Freitag B, Sinz E.J., Wismans B.: Die informationstechnische Infrastruktur der
Virtuellen Hochschule Bayern (vhb). Angenommen für Workshop "Unternehmen
Hochschule 2000" im Rahmen der Jahrestagung der Gesellschaft f. Informatik,
Berlin 19. - 22. September 2000
Nr. 57 (2000) Böhnlein M., Ulbrich-vom Ende A.: Developing Data Warehouse Structures from
Business Process Models.
Nr. 58 (2000) Knobloch B.: Der Data-Mining-Ansatz zur Analyse betriebswirtschaftlicher Daten.
Nr. 59 (2001) Sinz E.J., Böhnlein M., Plaha M., Ulbrich-vom Ende A.: Architekturkonzept eines
verteilten Data-Warehouse-Systems für das Hochschulwesen. Angenommen für:
WI-IF 2001, Augsburg, 19.-21. September 2001
Nr. 60 (2001) Sinz E.J., Wismans B.: Anforderungen an die IV-Infrastruktur von Hochschulen.
Angenommen für: Workshop „Unternehmen Hochschule 2001“ im Rahmen der
Jahrestagung der Gesellschaft für Informatik, Wien 25. – 28. September 2001
Änderung des Titels der Schriftenreihe Bamberger Beiträge zur Wirtschaftsinformatik in Bamberger
Beiträge zur Wirtschaftsinformatik und Angewandten Informatik ab Nr. 61
Note: The title of our technical report series has been changed from Bamberger Beiträge zur
Wirtschaftsinformatik to Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik
starting with TR No. 61
List of previous University of Bamberg reports 42
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Nr. 61 (2002) Goré R., Mendler M., de Paiva V. (Hrsg.): Proceedings of the International
Workshop on Intuitionistic Modal Logic and Applications (IMLA 2002),
Copenhagen, July 2002.
Nr. 62 (2002) Sinz E.J., Plaha M., Ulbrich-vom Ende A.: Datenschutz und Datensicherheit in
einem landesweiten Data-Warehouse-System für das Hochschulwesen. Erscheint
in: Beiträge zur Hochschulforschung, Heft 4-2002, Bayerisches Staatsinstitut für
Hochschulforschung und Hochschulplanung, München 2002
Nr. 63 (2005) Aguado, J., Mendler, M.: Constructive Semantics for Instantaneous Reactions
Nr. 64 (2005) Ferstl, O.K.: Lebenslanges Lernen und virtuelle Lehre: globale und lokale
Verbesserungspotenziale. Erschienen in: Kerres, Michael; Keil-Slawik, Reinhard
(Hrsg.); Hochschulen im digitalen Zeitalter: Innovationspotenziale und
Strukturwandel, S. 247 – 263; Reihe education quality forum, herausgegeben durch
das Centrum für eCompetence in Hochschulen NRW, Band 2, Münster/New
York/München/Berlin: Waxmann 2005
Nr. 65 (2006) Schönberger, Andreas: Modelling and Validating Business Collaborations: A Case
Study on RosettaNet
Nr. 66 (2006) Markus Dorsch, Martin Grote, Knut Hildebrandt, Maximilian Röglinger, Matthias
Sehr, Christian Wilms, Karsten Loesing, and Guido Wirtz: Concealing Presence
Information in Instant Messaging Systems, April 2006
Nr. 67 (2006) Marco Fischer, Andreas Grünert, Sebastian Hudert, Stefan König, Kira Lenskaya,
Gregor Scheithauer, Sven Kalle, and Guido Wirtz: Decentralized Reputation
Management for Cooperating Software Agents in Open Multi-Agent Systems,
April 2006
Nr. 68 (2006) Michael Mendler, Thomas R. Shiple, Gérard Berry: Constructive Circuits and the
Exactness of Ternary Simulation
Nr. 69 (2007) Sebastian Hudert: A Proposal for a Web Services Agreement Negotiation Protocol
Framework . February 2007
Nr. 70 (2007) Thomas Meins: Integration eines allgemeinen Service-Centers für PC-und
Medientechnik an der Universität Bamberg – Analyse und Realisierungs-
Szenarien. February 2007 (out of print)
Nr. 71 (2007) Andreas Grünert: Life-cycle assistance capabilities of cooperating Software Agents
for Virtual Enterprises. März 2007
Nr. 72 (2007) Michael Mendler, Gerald Lüttgen: Is Observational Congruence on μ-Expressions
Axiomatisable in Equational Horn Logic?
Nr. 73 (2007) Martin Schissler: out of print
Nr. 74 (2007) Sven Kaffille, Karsten Loesing: Open chord version 1.0.4 User’s Manual.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
74, Bamberg University, October 2007. ISSN 0937-3349.
List of previous University of Bamberg reports 43
Nr. 75 (2008) Karsten Loesing (Hrsg.): Extended Abstracts of the Second Privacy Enhancing
Technologies Convention (PET-CON 2008.1). Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 75, Bamberg University,
April 2008. ISSN 0937-3349.
Nr. 76 (2008) Gregor Scheithauer, Guido Wirtz: Applying Business Process Management
Systems – A Case Study. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 76, Bamberg University, May 2008. ISSN 0937-
Nr. 77 (2008) Michael Mendler, Stephan Scheele: Towards Constructive Description Logics for
Abstraction and Renement. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 77, Bamberg University, September 2008. ISSN
Nr. 78 (2008) Gregor Scheithauer, Matthias Winkler: A Service Description Framework for
Service Ecosystems. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 78, Bamberg University, October 2008. ISSN 0937-
Nr. 79 (2008) Christian Wilms: Improving the Tor Hidden Service Protocol Aiming at Better
Performances. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 79, Bamberg University, November 2008. ISSN 0937-3349.
Nr. 80 (2009) Thomas Benker, Stefan Fritzemeier, Matthias Geiger, Simon Harrer, Tristan
Kessner, Johannes Schwalb, Andreas Schönberger, Guido Wirtz: QoS Enabled
B2B Integration. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 80, Bamberg University, May 2009. ISSN 0937-3349.
Nr. 81 (2009) Ute Schmid, Emanuel Kitzelmann, Rinus Plasmeijer (Eds.): Proceedings of the
ACM SIGPLAN Workshop on Approaches and Applications of Inductive
Programming (AAIP'09), aliated with ICFP 2009, Edinburgh, Scotland,
September 2009. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 81, Bamberg University, September 2009. ISSN 0937-3349.
Nr. 82 (2009) Ute Schmid, Marco Ragni, Markus Knau (Eds.): Proceedings of the KI 2009
Workshop Complex Cognition, Paderborn, Germany, September 15, 2009.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
82, Bamberg University, October 2009. ISSN 0937-3349.
Nr. 83 (2009) Andreas Schönberger, Christian Wilms and Guido Wirtz: A Requirements Analysis
of Business-to-Business Integration. Bamberger Beiträge zur Wirtschaftsinformatik
und Angewandten Informatik Nr. 83, Bamberg University, December 2009. ISSN
Nr. 84 (2010) Werner Zirkel, Guido Wirtz: A Process for Identifying Predictive Correlation
Patterns in Service Management Systems. Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 84, Bamberg University,
February 2010. ISSN 0937-3349.
Nr. 85 (2010) Jan Tobias Mühlberg und Gerald Lüttgen: Symbolic Object Code Analysis.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
85, Bamberg University, February 2010. ISSN 0937-3349.
List of previous University of Bamberg reports 44
Nr. 86 (2010) Werner Zirkel, Guido Wirtz: Proaktives Problem Management durch
Eventkorrelation ein Best Practice Ansatz. Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 86, Bamberg University,
August 2010. ISSN 0937-3349.
Nr. 87 (2010) Johannes Schwalb, Andreas Schönberger: Analyzing the Interoperability of WS-
Security and WS-ReliableMessaging Implementations. Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 87, Bamberg University,
September 2010. ISSN 0937-3349.
Nr. 88 (2011) Jörg Lenhard: A Pattern-based Analysis of WS-BPEL and Windows Workow.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
88, Bamberg University, March 2011. ISSN 0937-3349.
Nr. 89 (2011) Andreas Henrich, Christoph Schlieder, Ute Schmid [eds.]: Visibility in Information
Spaces and in Geographic Environments – Post-Proceedings of the KI’11
Workshop. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 89, Bamberg University, December 2011. ISSN 0937-3349.
Nr. 90 (2012) Simon Harrer, Jörg Lenhard: Betsy - A BPEL Engine Test System. Bamberger
Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr. 90, Bamberg
University, July 2012. ISSN 0937-3349.
Nr. 91 (2013) Michael Mendler, Stephan Scheele: On the Computational Interpretation of CKn
for Contextual Information Processing - Ancillary Material. Bamberger Beiträge
zur Wirtschaftsinformatik und Angewandten Informatik Nr. 91, Bamberg
University, May 2013. ISSN 0937-3349.
Nr. 92 (2013) Matthias Geiger: BPMN 2.0 Process Model Serialization Constraints. Bamberger
Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr. 92, Bamberg
University, May 2013. ISSN 0937-3349.
Nr. 93 (2014) Cedric Röck, Simon Harrer: Literature Survey of Performance Benchmarking
Approaches of BPEL Engines. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 93, Bamberg University, May 2014. ISSN 0937-
Nr. 94 (2014) Joaquin Aguado, Michael Mendler, Reinhard von Hanxleden, Insa Fuhrmann:
Grounding Synchronous Deterministic Concurrency in Sequential Programming.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
94, Bamberg University, August 2014. ISSN 0937-3349.
Nr. 95 (2014) Michael Mendler, Bruno Bodin, Partha S Roop, Jia Jie Wang: WCRT for
Synchronous Programs: Studying the Tick Alignment Problem. Bamberger
Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr. 95, Bamberg
University, August 2014. ISSN 0937-3349.
Nr. 96 (2015) Joaquin Aguado, Michael Mendler, Reinhard von Hanxleden, Insa Fuhrmann:
Denotational Fixed-Point Semantics for Constructive Scheduling of Synchronous
Concurrency. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 96, Bamberg University, April 2015. ISSN 0937-3349.
List of previous University of Bamberg reports 45
Nr. 97 (2015) Thomas Benker: Konzeption einer Komponentenarchitektur für prozessorientierte
OLTP- & OLAP-Anwendungssysteme. Bamberger Beiträge zur Wirtschafts-
informatik und Angewandten Informatik Nr. 97, Bamberg University, Oktober
2015. ISSN 0937-3349.
Nr. 98 (2016) Sascha Fendrich, Gerald Lüttgen: A Generalised Theory of Interface Automata,
Component Compatibility and Error. Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 98, Bamberg University,
March 2016. ISSN 0937-3349.
Nr. 99 (2014) Christian Preißinger, Simon Harrer: Static Analysis Rules of the BPEL
Specication: Tagging, Formalization and Tests. Bamberger Beiträge zur
Wirtschaftsinformatik und Angewandten Informatik Nr. 99, Bamberg University,
August 2014. ISSN 0937-3349.
Nr. 100 (2016) Cedrik Röck, Stefan Kolb: Nucleus - Unied Deployment and Management for
Platform as a Service. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 100, Bamberg University, March 2016. ISSN 0937-
Nr. 101 (2016) Michael Mendler, Partha S. Roop, Bruno Bodin: A Novel WCET Semantics of
Synchronous Programs. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 101, Bamberg University, June 2016. ISSN 0937-
Nr. 102 (2017) Joaquín Aguado, Michael Mendler, Marc Pouzet, Partha Roop, Reinhard von
Hanxleden: Clock-Synchronised Shared Objects for Deterministic Concurrency.
Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik Nr.
102, Bamberg University, July 2017. ISSN 0937-3349.
Nr. 103 (2018) Eugene Yip, Erjola Lalo, Gerald Lüttgen, Michael Deubzer, Andreas Sailer:
Optimized Buering of Time-Triggered Automotive Software. Bamberger Beiträge
zur Wirtschaftsinformatik und Angewandten Informatik Nr. 103, Bamberg
University, September 2018. ISSN 0937-3349.
Nr. 104 (2018) Daniel Hallmann: Die COCOMO-Modelle im Licht der agilen
Softwareentwicklung. Bamberger Beiträge zur Wirtschaftsinformatik und
Angewandten Informatik Nr. 104, Bamberg University, October 2018. ISSN 0937-
Nr. 105 (2021) Johannes Manner: SeMoDe – Simulation and Benchmarking Pipeline for Function
as a Service. Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten
Informatik Nr. 105, Bamberg University, November 2021. ISSN 0937-3349.
List of previous University of Bamberg reports 46
... In the following, we list research which shows these strategies based on empirical data. A Structured Literature Review (SLR) already conducted about benchmarking and simulation in the FaaS domain [21] lists contributions in this area as well as another SLR on performance evaluation [22]. We use these studies as primary sources. ...
... This is only the case for functions executed on homogeneous hardware as experiments on AWS Lambda showed where heterogeneous hardware is also present [26], [28]- [31]. A checklist for making FaaS experiments recommends to add the physical machine configuration and the VM or container identification to every experiment record [21] to avoid misinterpretation of measured data. Multi-threaded functions [10], [32] are another aspect to consider when re-engineering the scaling behavior of resources since AWS Lambda and Google Cloud Functions offer configurations where more than one vCPU is assigned. ...
... Our scaling strategy and the usage of K8s limits guarantee resources on the specific machine which provides an answer for the noisy neighbor problem (RQ3). Inference between the deployed pods is still present which is a difference to the architecture of AWS Lambda, where each function is performance-and security-wise isolated within a single VM 21 . ...
Conference Paper
Full-text available
Open-source offerings are often investigated when comparing their features to commercial cloud offerings. However, performance benchmarking is rarely executed for open-source tools hosted on-premise nor is it possible to conduct a fair cost comparison due to a lack of resource settings equivalent to cloud scaling strategies. Therefore, we firstly list implemented resource scaling strategies for public and open-source FaaS platforms. Based on this we propose a methodology to calculate an abstract performance measure to compare two platforms with each other. Since all open-source platforms suggest a Kubernetes deployment, we use this measure for a configuration of open-source FaaS platforms based on Kubernetes limits. We tested our approach with CPU intensive functions, considering the difference between single-threaded and multi-threaded functions to avoid wasting resources. With regard to this, we also address the noisy neighbor problem for open-source FaaS platforms by conducting an instance parallelization experiment. Our approach to limit resources leads to consistent results while avoiding an overbooking of resources.
... There are several other SLRs which are related to this paper as they searched for literature in a reproducible way in order to better understand the following areas of research concerning FaaS: The studies deal with benchmarking [22], [23], performance evaluations [24] and tooling in the FaaS domain [25]. In another systematic investigation, TAIBI and others [6] examined 24 publications and found 32 patterns for building applications. ...
Conference Paper
Full-text available
Nearly a decade ago, the emergence of an event-driven computing model attracted a lot of attention in industry and academia. This new computing concept has been described with a plethora of terms: Serverless Computing, Function as a Service (FaaS), Serverless functions, cloud functions, server-aware vs. server-less, to name only a few of the most frequently used terms. This enumeration showcases a problem of the domain: the lack of clear terminology, a conceptualization of Serverless and FaaS and a differentiation of the two. To overcome this limitation we present a Structured Literature Review (SLR) and usage trends which help to understand the current usage of the terms. Based on characteristics extracted from several definitions and validated in the course of the SLR we demarcate Serverless and FaaS. Our SLR shows that 90% of the considered papers predominantly use the term Serverless whereas most of the authors intend to write about FaaS in the sense of our definition. Another insight is that the viewpoint on Serverless and FaaS evolved over time and that since 2021 most authors see Serverless as an umbrella term for FaaS and Backend as a Service (BaaS). Some of them also include further cloud service models, i.e. parts of PaaS and SaaS, providing a server-less experience for an application developer. This is an interpretation we share.
... Because these nodes lack significant resources, such as those discovered at a server, the computing public-purpose environment made possible at the edge will necessitate lower resources. Reduced boot times, resource isolation, and rapid deployment are all appealing features [68,69]. According to initial research, mobile containers that multiplex device hardware across multiple virtual devices could perform similarly to native equipment [70,71]. ...
Conference Paper
Full-text available