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A Brief History of Cloud Application Architectures

  • Lübeck University of Applied Sciences

Abstract and Figures

This paper presents a review of cloud application architectures and its evolution. It reports observations being made during a research project that tackled the problem to transfer cloud applications between different cloud infrastructures. As a side effect, we learned a lot about commonalities and differences from plenty of different cloud applications which might be of value for cloud software engineers and architects. Throughout the research project, we analyzed industrial cloud standards, performed systematic mapping studies of cloud-native application-related research papers, did action research activities in cloud engineering projects, modeled a cloud application reference model, and performed software and domain-specific language engineering activities. Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and its related application architecture evolution can be seen as a steady process to optimize resource utilization in cloud computing. Second, these resource utilization improvements resulted over time in an architectural evolution of how cloud applications are being built and deployed. A shift from monolithic service-oriented architectures (SOA), via independently deployable microservices towards so-called serverless architectures, is observable. In particular, serverless architectures are more decentralized and distributed and make more intentional use of separately provided services. In other words, a decentralizing trend in cloud application architectures is observable that emphasizes decentralized architectures known from former peer-to-peer based approaches. This is astonishing because, with the rise of cloud computing (and its centralized service provisioning concept), the research interest in peer-to-peer based approaches (and its decentralizing philosophy) decreased. However, this seems to change. Cloud computing could head into the future of more decentralized and more meshed services.
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A Brief History of Cloud Application Architectures
Nane Kratzke ID
Lübeck University of Applied Sciences, Department of Electrical Engineering and Computer Science,
23562 Lübeck, Germany;
Received: 14 July 2018; Accepted: 27 July 2018; Published: 14 August 2018
Featured Application: This paper features system and software engineering use cases for
large-scale (business) Cloud-native applications (e.g., Netflix, Twitter, Uber, Google Search).
Such Cloud-native applications (CNA) provide web-scalability and independent deployability
of their components and enable exponential user growth. Furthermore, migration and
architecture transformation use cases of existing tiered and on-premise (business) applications
are additionally of interest. Thus, questions of how existing and not cloud-ready applications
are migratable into cloud environments are covered as well.
This paper presents a review of cloud application architectures and its evolution. It reports
observations being made during a research project that tackled the problem to transfer cloud
applications between different cloud infrastructures. As a side effect, we learned a lot about
commonalities and differences from plenty of different cloud applications which might be of value
for cloud software engineers and architects. Throughout the research project, we analyzed industrial
cloud standards, performed systematic mapping studies of cloud-native application-related research
papers, did action research activities in cloud engineering projects, modeled a cloud application
reference model, and performed software and domain-specific language engineering activities.
Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and
its related application architecture evolution can be seen as a steady process to optimize resource
utilization in cloud computing. Second, these resource utilization improvements resulted over
time in an architectural evolution of how cloud applications are being built and deployed. A shift
from monolithic service-oriented architectures (SOA), via independently deployable microservices
towards so-called serverless architectures, is observable. In particular, serverless architectures
are more decentralized and distributed, and make more intentional use of separately provided
services. In other words, a decentralizing trend in cloud application architectures is observable
that emphasizes decentralized architectures known from former peer-to-peer based approaches.
This is astonishing because, with the rise of cloud computing (and its centralized service provisioning
concept), the research interest in peer-to-peer based approaches (and its decentralizing philosophy)
decreased. However, this seems to change. Cloud computing could head into the future of more
decentralized and more meshed services.
cloud computing; service-oriented architecture; SOA; cloud-native; serverless; microservice;
container; unikernel; distributed cloud; P2P; service-to-service; service-mesh
1. Introduction
Even tiny companies can generate enormous economic growth and business value by providing
cloud-based services or applications—Instagram, Uber, WhatsApp, NetFlix, and Twitter—and
many astonishing small companies (if we relate the modest headcount of these companies in their
founding days to their noteworthy economical impact) whose services are frequently used. However,
Appl. Sci. 2018,8, 1368; doi:10.3390/app8081368
Appl. Sci. 2018,8, 1368 2 of 26
even a fast-growing start-up business model should have its long-term consequences and dependencies
in mind. A lot of these companies rely on public cloud infrastructures—currently often provided
by Amazon Web Services (AWS). However, will AWS still be the leading and dominating cloud
service provider in 20 years? IT history is full of examples of large and well-known companies failing
and (almost) dieing: Atari, America Online (AOL), Compaq, Hewlett Packard, Palm, and Yahoo.
Even Microsoft—still a prospering company—is no longer the dominating software company that it
used to be in the 1990s and 2000s. Microsoft is even a good example of a company that has evolved and
transformed into a cloud service provider. This might be because cloud providers are becoming more
and more critical for national economies. Cloud providers run a significant amount of mission-critical
business software for companies that no longer operate their own data-centers. Moreover, it is very
often economically reasonable if workloads have a high peak-to-average ratio [
]. Thus, cloud
providers might become (or even are) a too-big-to-fail company category that seems to become
equally important for national economies like banks, financial institutions, electricity suppliers, public
transport systems. Although essential for national economies, these financial, energy, or transport
providers provide just replaceable goods or services—commodities. However, the cloud computing
domain is still different here. Although cloud services could be standardized commodities, they are
mostly not. Once a cloud-hosted application or service is deployed to a specific cloud infrastructure,
it is often inherently bound to that infrastructure due to non-obvious technological bindings. A transfer
to another cloud infrastructure is very often a time-consuming and expensive one-time exercise.
A good real-world example here is Instagram. After being bought by Facebook, it took over a year for
the Instagram engineering team to find and establish a solution for the transfer of all its services from
AWS to Facebook data centers. Although no downtimes were planned, noteworthy outages occurred
during that period.
The NIST definition of cloud computing defines three basic and well-accepted service
categories [
]: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Sevice
(SaaS). IaaS provides maximum flexibility for arbitrary consumer-created software but hides almost
no operation complexity of the application (just of the infrastructure). SaaS on the opposite hides
operation complexity almost entirely but is too limited for many use cases involving consumer-created
software. PaaS is somehow a compromise enabling the operation of consumer-created software
with a convenient operation complexity but at the cost of following resource efficient application
architectures and accepting to some degree lock-in situations resulting from the platform.
Throughout a project called CloudTRANSIT, we searched intensively for solutions to overcome
this “cloud lock-in”—to make cloud computing an actual commodity. We developed and evaluated
a cloud application transferability concept that has prototype status but already works for
approximately 70% of the current cloud market, and that can be extended for the rest of the market
share [
]. However, what is essential: we learned some core insights from our action research with
1. Practitioners prefer to transfer platforms (and not applications).
2. Practitioners want to have the choice between platforms.
Practitioners prefer declarative and cybernetic (auto-adjusting) instead of workflow-based
(imperative) deployment and orchestration approaches.
Practitioners are forced to make efficient use of cloud resources because more and more systems
are migrated to cloud infrastructures causing steadily increasing bills.
Practitioners rate pragmatism of solutions much higher than full feature coverage of cloud
platforms and infrastructures.
All these points influence ulteriorly how practitioners nowadays construct cloud application
architectures that are intentionally designed for the cloud. This paper investigates the observable
evolution of cloud application architectures over the last decade.
Appl. Sci. 2018,8, 1368 3 of 26
2. Methodology and Outline of This Paper
Figure 1presents the research methodology for this paper. The remainder of this paper follows
this structure. Section 3presents an overview of the research project CloudTRANSIT that build the
foundation of our problem awareness of cloud application architectures. The project CloudTRANSIT
intentionally tackled the cloud lock-in problem of cloud-native applications and analyzed how
cloud-applications can be transferred between different cloud infrastructures at runtime without
downtime. From several researchers as well as reviewer feedback, we get to know that the insights
we learned about cloud architectures merely as a side-effect might be of general interest for the cloud
computing research and engineering community.
Figure 1. Research methodology.
One thing we learned was the fact that cloud-native applications—although they are all
different—follow some common architectural patterns that we could exploit for transferability.
Section 4presents a reference model that structures such observable commonalities of cloud application
architectures. Based on that insight, the obvious question that arises is what long-term trends exist that
influence current shapes of cloud application architectures? Section 5will investigate such observable
long-term trends. In particular, we will investigate the resource utilization evolution in Section 5.1
and the architectural evolution in Section 5.2 and ends to some degree the observable status quo.
However, the question is whether these long-term trends will go on in the future and can they be used
for forecasts? Although forecasts are tricky in general and our research has not invented a crystal
ball, Section 6will take a look on the road ahead mainly by extrapolating these identified trends.
Some aspects can be derived from the observed long-term-trends regarding optimization of resource
efficiency in Section 6.1 and architectural changes by a Scopus based literature trend analysis in
Section 6.2. Apparently, this paper is not the only one reflecting and analyzing cloud application
architecture approaches, and the reader should take related work in Section 7into account as well.
Finally, we look at our brief history of cloud architectures and long-term trends. Assuming that these
long-term trends will go on in the future for a while, we draw some conclusions on the road ahead in
Section 8.
3. Problem Awareness (from the Research Project Cloud TRANSIT)
Our problem awareness results mainly from the conducted research project CloudTRANSIT. This
project dealt with the question of how to transfer cloud applications and services at runtime without
downtime across cloud infrastructures from different public and private cloud service providers to
tackle the existing and growing problem of vendor lock-in in cloud computing. Throughout the project,
we published more than 20 research papers. However, the intent of this paper is not to summarize
these papers. The interested reader is referred to the corresponding technical report [3] that provides
an integrated view of these outcomes.
Appl. Sci. 2018,8, 1368 4 of 26
This paper strives to take a step back and review the observed state of the art related to how
cloud-based systems are being built today and how they might be built tomorrow. It might be of
interest for the reader to get an impression of how the foundation for these insights has been derived
by understanding the mentioned research project.
The project analyzed commonalities of existing public and private cloud infrastructures via
a review of industrial cloud standards and cloud applications via a systematic mapping study of
cloud-native application-related research [
]. Action research projects with practitioners accompanied
this review. Latest evolutions of cloud standards and cloud engineering trends (like containerization)
were used to derive a reference model that guided the development of a pragmatic cloud-transferability
solution. We evaluated this reference model using a concrete project from our action research
activities [
]. This solution intentionally separated the
infrastructure-agnostic operation
of elastic
container platforms (like Swarm, Kubernetes, Mesos/Marathon, etc.) via a
definition of cloud-native applications and services via an
unified cloud
application modeling language
. Both components are independent but complementary and provide
a solution to operate elastic (container) platforms in an infrastructure-agnostic, secure, transferable,
and elastic way. This multi-cloud-scaler is described in [
]. Additionally, we had to find a solution to
describe cloud applications in a unified format. This format can be transformed into platform-specific
definition formats like Swarm compose, Kubernetes manifest files, and more. This Unified Cloud
Application Modeling Language (UCAML) is explained in [
]. Both approaches mutually influenced
each other and therefore have been evaluated in parallel by deploying and transferring several cloud
reference applications [
] at runtime [
]. This solution supports the public cloud infrastructures of
AWS, Google Compute Engine (GCE), and Azure and open source infrastructure OpenStack. It covers
approximately 70% of the current cloud market. Because the solution can be extended with cloud
infrastructure drivers, the rest of the market share can also be supported by additional drivers of
moderate complexity.
However, what is even more essential: We learned some core insights about cloud application
architectures in general by asking the question of how to transfer this kind of applications without
touching their application architectures. Let us investigate this in the following Section 4.
4. Reference Modeling—How Cloud Applications Look
Almost all cloud system engineers focus on a common problem. The core components of their
distributed and cloud-based systems like virtualized server instances and basic networking and storage
can be deployed using commodity services. However, further services—that are needed to integrate
these virtualized resources in an elastic, scalable, and pragmatic manner—are often not considered
in standards. Services like load balancing, auto-scaling or message queuing systems are needed to
design an elastic and scalable cloud-native system on almost every cloud service infrastructure. Some
standards like AMQP [
] for messaging (dating back almost to the pre-cloud era) exist. However,
mainly these integrating and “gluing” service types—that are crucial for almost every cloud application
on a higher cloud maturity level (see Table 1)—are often not provided in a standardized manner by
cloud providers [
]. It seems that all public cloud service providers try to stimulate cloud customers
to use their non-commodity convenience service “interpretations” to bind them to their infrastructures
and higher-level service portfolios.
Furthermore, according to an analysis we performed in 2016 [
], the percentage of these
commodity service categories that are considered in standards like CIMI [
], OCCI [
], CDMI [
], OCI [
], TOSCA [
] is even decreasing over the years. This mainly has to do with the fact
that new cloud service categories are released faster than standardization authorities can standardize
existing service categories. Figure 2shows this effect by the example of AWS over the years. This is
how mainly vendor lock-in emerges in cloud computing. For a more detailed discussion, we refer
to [5,13,21].
Appl. Sci. 2018,8, 1368 5 of 26
Table 1. Cloud Application Maturity Model, adapted from OPEN DATA CENTER ALLIANCE [22].
Level Maturity Criteria
3 Cloud - Transferable across infrastructure providers at
native runtime and without interruption of service.
- Automatically scale out/in based on stimuli.
2 Cloud - State is isolated in a minimum of services.
resilient - Unaffected by dependent service failures.
- Infrastructure agnostic.
1 Cloud - Composed of loosely coupled services.
friendly - Services are discoverable by name.
- Components are designed to cloud patterns.
- Compute and storage are separated.
0 Cloud - Operated on virtualized infrastructure.
ready - Instantiateable from image or script.
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Figure 2. Decrease of standard coverage over years (by example of AWS).
Therefore, all reviewed cloud standards focus on a minimal but necessary subset of popular
cloud services: compute nodes (virtual machines), storage (file, block, object), and (virtual private)
networking. Standardized deployment approaches like TOSCA are defined mainly against this
commodity infrastructure level of abstraction. These kinds of services are often subsumed as IaaS
and build the foundation of cloud services and therefore cloud-native applications. All other service
categories might foster vendor lock-in situations. This might sound disillusioning. In consequence,
many cloud engineering teams follow the basic idea that a cloud-native application stack should be
only using a minimal subset of well-standardized IaaS services as founding building blocks. Because
existing cloud standards cover only specific cloud service categories (mainly the IaaS level) and do
not show an integrated point of view, a more integrated reference model that takes best practices of
practitioners into account would be helpful.
Very often, cloud computing is investigated from a service model point of view (IaaS, PaaS, SaaS),
a deployment point of view (private, public, hybrid, community cloud) [
]. Alternatively, one can look
from an actor point of view (provider, consumer, auditor, broker, carrier) or a functional point of view
(service deployment, service orchestration, service management, security, privacy) as it is done by [
Points of view are particularly useful to split problems into concise parts. However, the viewpoints
mentioned above might be common in cloud computing and useful from a service provider point of
view but not from cloud-native application engineering point of view. From an engineering point of
view, it seems more useful to have views on technology levels involved and applied in cloud-native
application engineering. Practitioner models do this often.
Appl. Sci. 2018,8, 1368 6 of 26
By using the insights from our systematic mapping study [
] and our review of cloud
standards [
], we compiled a reference model of cloud-native applications. This layered reference
model is shown and explained in Figure 3. The basic idea of this reference model is to use only
a small subset of well-standardized IaaS services as founding building blocks (Layer 1). Four primary
viewpoints form the overall shape of this model.
1. Infrastructure provisioning:
This is a viewpoint being familiar for engineers working on the
infrastructure level and how IaaS is understood. IaaS deals with the deployment of separate
compute nodes for a cloud consumer. It is up to the cloud consumer what he does with these
isolated nodes (even if there are hundreds of them).
2. Clustered elastic platforms:
This is a viewpoint being familiar for engineers who are dealing
with horizontal scalability across nodes. Clusters are a concept to handle many Layer 1 nodes
as one logical compute node (a cluster). Such kind of technologies is often the technological
backbone for portable cloud runtime environments because they are hiding complexity (of
hundreds or thousands of single nodes) appropriately. Additionally, this layer realizes the
foundation to define services and applications without reference to particular cloud services,
cloud platforms or cloud infrastructures. Thus, it provides a foundation to avoid vendor lock-in.
3. Service composing:
This is a viewpoint familiar for application engineers dealing with Web
services in service-oriented architectures (SOA). These (micro)-services operate on a Layer 2
cloud runtime platform (like Kubernetes, Mesos, Swarm, Nomad, and so on). Thus, the complex
orchestration and scaling of these services are abstracted and delegated to a cluster (cloud
runtime environment) on Layer 2.
4. Application:
This is a viewpoint being familiar for end-users of cloud services (or cloud-native
applications). These cloud services are composed of smaller cloud Layer 3 services being operated
on clusters formed of single compute and storage nodes.
Figure 3. Cloud-native stack observable in a lot of cloud-native applications.
For more details, we refer to [3,5]. However, the remainder of this paper follows this model.
5. Observable Long-Term Trends in Cloud Systems Engineering
Cloud computing emerged some ten years ago. In the first adoption phase, existing IT-systems
were merely transferred to cloud environments without changing the original design and architecture
of these applications. Tiered applications were merely migrated from dedicated hardware to virtualized
hardware in the cloud. Cloud system engineers implemented remarkable improvements in cloud
Appl. Sci. 2018,8, 1368 7 of 26
platforms (PaaS) and infrastructures (IaaS) over the years and established several engineering trends
currently observable.
All of these trends try to optimize specific quality factors like functional stability, performance
efficiency, compatibility, usability, reliability, maintainability, portability, and security of cloud services
to improve the overall quality of service (QoS). The most focused quality factors are functional stability,
performance efficiency, and reliability (including availability) [
]. Therefore, these engineering
trends listed in Table 2seem somehow isolated. We want to review these trends from two different
In Section 5.1, we will investigate cloud application architectures from a resource utilization
point of view over time.
In Section 5.2, we will investigate cloud application architectures more from an evolutionary
architecture point of view focusing mainly functional stability and reliability but also addressing
compatibility, maintainability, and portability according to [26].
In both cases, we will see that the wish to make more efficient use of cloud resources had impacts
on architectures and vice versa.
Table 2. Some observable software engineering trends coming along with cloud-native applications.
Trend Rationale
Microservices can be seen as a “pragmatic” interpretation of SOA. In addition to SOA,
microservice architectures intentionally focus and compose small and independently
replaceable horizontally scalable services that are “doing one thing well.” [2731]
DevOps is a practice that emphasizes the collaboration of software developers and IT
operators. It aims to build, test, and release software more rapidly, frequently, and more
reliably using automated processes for software delivery [
]. DevOps foster the need
for independent replaceable and standardized deployment units and therefore pushes
microservice architectures and container technologies.
Cloud Modeling
Softwareization of infrastructure and network enables to automate the process of software
delivery and infrastructure changes more rapidly. Cloud modeling languages can express
applications and services and their elasticity behavior that shall be deployed to such
infrastructures or platforms. There is a good survey on this kind of new “programming
languages” [34].
Deployment units wrap a piece of software in a complete file system that contains
everything needed to run: code, runtime, system tools, system libraries. Thus, it is
guaranteed that the software will always run the same, regardless of its environment.
This deployment approach is often made using container technologies (OCI standard [
Unikernels would work as well but are not yet in widespread use. A deployment unit
should be designed and interconnected according to a collection of cloud-focused patterns
like the twelve-factor app collection [
], the circuit breaker pattern [
] or cloud computing
patterns [37,38].
Appl. Sci. 2018,8, 1368 8 of 26
Table 2. Cont.
Trend Rationale
Elastic Platforms
Elastic platforms like Kubernetes [
], Mesos [
], or Swarm can be seen as a unifying
middleware of elastic infrastructures. Elastic platforms extend resource sharing and
increase the utilization of underlying compute, network and storage resources for custom
but standardized deployment units.
the term serverless is used for an architectural style that is used for cloud application
architectures that deeply depend on external third-party-services (Backend-as-a-Service,
BaaS) and integrating them via small event-based triggered functions (Function-as-a, FaaS).
FaaS extend resource sharing of elastic platforms by simply by applying time-sharing
concepts [4042].
State Isolation
Stateless components are easier to scale up/down horizontally than stateful components.
Of course, stateful components cannot be avoided, but stateful components should be
reduced to a minimum and realized by intentional horizontal scalable storage systems
(often eventual consistent NoSQL databases) [37].
Versioned REST
REST-based APIs provide scalable and pragmatic communication, means relying mainly
on already existing internet infrastructure and well defined and widespread standards [
Loose coupling
Service composition is done by events or by data [
]. Event coupling relies on messaging
solutions (e.g., AMQP standard). Data coupling often relies on scalable but (mostly)
eventual consistent storage solutions (which are often subsumed as NoSQL databases) [
5.1. A Review of the Resource Utilization Evolution and Its Impact on Cloud Technology Architectures
Cloud infrastructures (IaaS) and platforms (PaaS) are built to be elastic. Elasticity is understood
as the degree to which a system adapts to workload changes by provisioning and de-provisioning
resources automatically. Without this, cloud computing is very often not reasonable from an economic
point of view [
]. Over time, system engineers learned to understand this elasticity options of modern
cloud environments better. Eventually, systems were designed for such elastic cloud infrastructures,
which increased the utilization rates of underlying computing infrastructures via new deployment
and design approaches like containers, microservices or serverless architectures. This design intention
is often expressed using the term ”cloud-native.”
Figure 4shows a noticeable trend over the last decade. Machine virtualization was introduced
to consolidate plenty of bare metal machines to make more efficient utilization of physical resources.
This machine virtualization forms the technological backbone of IaaS cloud computing. Virtual
machines might be more lightweight than bare metal servers, but they are still heavy, especially
regarding their image sizes. Due to being more fine-grained, containers improved the way of
standardized deployments but also increased the utilization of virtual machines. Nevertheless,
although containers can be scaled quickly, they are still always-on components. In addition, “recently,”
Function-as-a-Service (FaaS) approaches emerged and applied time sharing of containers on underlying
container platforms. Using FaaS only, units are executed that have requests to be processed. Due
to this time-shared execution of containers on the same hardware, FaaS enables even a scale-to-zero
capability. This improved resource efficiency can be even measured monetarily [44]. Thus, over time,
the technology stack to manage resources in the cloud got more complicated and harder to understand
but followed one trend—to run more workload on the same amount of physical machines.
Appl. Sci. 2018,8, 1368 9 of 26
Bare Metal Server
Bare Metal
Bare Metal Server
Bare Metal Server
FaaS Runtime
Virtualization Containerization
Dedicated Server
In case of dedicated servers applications (A,
B) are deployed on physical servers. In
consequence, the servers are often over
dimensioned and have inefficient utilization
Machine virtualization is mainly used to
consolidate and isolate applications on
virtual machine instead of dedicated
servers. This increases the application
density on bare metal servers but the
virtual machine images (deployment
unit) are very large.
To pragmatically operate more than one
application per virtual machine,
containerization established as a trend. A
container starts faster than a virtual
machine and shares the operating system
with other containers, thus reducing
deployment unit sizes and increasing
application density per virtual machine.
But a container still requests a share of
CPU, memory, and storage even if the
provided service is hardly requested. It is
more resource efficient, if services would
consume resources only if there are
incoming requests. FaaS runtime
environments enable that services can
timeshare a host. However, this involves to
follow a serverless architecture style.
1 2 3 4
Serverless, FaaS
Bare Metal
Figure 4. The cloud architectural evolution from a resource utilization point of view.
5.1.1. Service-Oriented Deployment Monoliths
An interesting paper the reader should dive into is [
]. Service-Oriented Computing (SOC) is
a paradigm for distributed computing and e-business processing and has been introduced to manage
the complexity of distributed systems and to integrate different software applications. A service offers
functionalities to other services mainly via message passing. Services decouple their interfaces from
their implementation. Workflow languages are used to orchestrate more complex actions of services
(e.g., WS-BPEL). Corresponding architectures for such kind of applications are called Service-Oriented
Architectures (SOA) consequently. Many business applications have been developed over the last
decades following this architectural paradigm. In addition, due to its underlying service concepts,
these applications can be deployed in cloud environments without any problems. Thus, they are
cloud ready/friendly according to Table 1. However, the main problem for cloud system engineers
emerges from the problem that—although these kinds of applications are composed of distributed
services—their deployment is not! These kinds of distributed applications are conceptually monolithic
applications from a deployment point of view. Dragoni et al. define such monolithic software as:
“A monolithic software application is a software application composed of modules that are not
independent of the application to which they belong. Since the modules of a monolith depend on said
shared resources, they are not independently executable. This makes monoliths difficult to naturally
distribute without the use of specific frameworks or ad hoc solutions [...]. In the context of cloud-based
distributed systems, this represents a significant limitation, in particular, because previous solutions
leave synchronization responsibilities to the developer [45]”.
In other words, the complete distributed application must be deployed all at once in the case
of updates or new service releases. This monolithic style even leads to situations where complete
applications are simply packaged as one large virtual machine image. This fits perfectly to situations
shown in Figure 4(1 + 2). However, depending on the application size, this normally involves
noteworthy downtimes of the application for end users and limits the capability to scale the application
in the case of increasing or decreasing workloads. While this might be acceptable for some services
(e.g., some billing batch processes running somewhere in the night), it might be problematic for other
kinds of services. What if messaging services (e.g., WhatsApp), large-scale social networks (e.g.,
Facebook), credit card instant payment services (e.g., Visa), traffic-considering navigational services
(e.g., Google Maps), or ridesharing services (e.g., Uber) would go down for some hours just because of
a new service release or a scaling operation?
Appl. Sci. 2018,8, 1368 10 of 26
It is evident that especially cloud-native applications come along with such 24
7 requirements
and the need to deploy, update, or scale single components independently from each other at runtime
without any downtime. Therefore, SOA evolved into a so-called microservice architectural style.
One might mention that microservices are mainly a more pragmatic version of SOA. Furthermore,
microservices are intentionally designed to be independently deployable, updateable, and horizontally
scalable. Thus, microservices have some architectural implications that will be investigated in
Section 5.2.1. However, deployment units should be standardized and self-contained as well in
this setting. We will have a look at that in the following Section 5.1.2.
5.1.2. Standardized and Self-Contained Deployment Units
While deployment monoliths are mainly using IaaS resources in the form of virtual machines
that are deployed and updated less regularly, microservice architectures split up the monolith into
independently deployable units that are deployed and terminated much more frequently. Furthermore,
this deployment is done in a horizontally scalable way that is very often triggered by request stimuli.
If many requests are hitting a service, more service instances are launched to distribute the requests
across more instances. If the requests are decreasing, service instances are shut down to free resources
(and save money). Thus, the inherent elasticity capabilities of microservice architectures are much
more in focus compared with classical deployment monoliths and SOA approaches. One of the critical
success factors for microservice architectures gaining so much attraction over the last years might be
the fact that the deployment of service instances could be standardized as self-contained deployment
units—so-called containers [
]. Containers make use of operating system virtualization instead of
machine virtualization (see Figure 5) and are therefore much more lightweight. Containers enable
making scaling much more pragmatic, and faster and, because containers are less resource consuming
compared with virtual machines, the instance density on underlying IaaS hardware could be improved.
Figure 5. Comparing containers and virtual machines (adapted from the Docker website).
However, even in microservice architectures, the service concept is an always-on concept.
Thus, at least one service instance (container) must be active and running for each microservice
at all times. Microservice architectures make use of plenty of such small services. To have a lot of small
services is the dominant design philosophy of the microservice architectural approach. Thus, even
container technologies do not overcome the need for always-on components. In addition, always-on
components are some of the most expensive and therefore avoidable cloud workloads according to
Appl. Sci. 2018,8, 1368 11 of 26
Weinmann [
]. Thus, the question arises of whether it is possible to execute service instances only
in the case of actual requests. Moreover, the answer leads to Function-as-a-Service concepts and
corresponding platforms that will be discussed in Section 5.1.3.
5.1.3. Function-as-a-Service
Microservice architectures propose a solution to efficiently scale computing resources that are
hardly realizable with monolithic architectures [
]. The allocated infrastructure can be better
tailored to the microservices’ needs due to the independent scaling of each one of them via
standardized deployment units addressed in Section 5.1.2. However, microservice architectures face
additional efforts like deploying every single microservice and scaling and operating them in cloud
infrastructures. To address these concerns, container orchestrating platforms like Kubernetes [
or Mesos/Marathon [
] emerged. However, this shifts the problem mainly to the operation of
these platforms and these platforms are still always-on components. Thus, so-called Serverless
architectures and Function-as-a-Service platforms have emerged in the cloud service ecosystem.
The AWS lambda service might be the most prominent one, but there exist more like Google Cloud
Functions, Azure Functions, OpenWhisk, Spring Cloud Functions to name just a few. However,
all (commercial platforms) follow the same principle to provide very small and fine-grained services
(just exposing one stateless function) that are billed on a runtime-consuming model (millisecond
dimension). The problem with the term Serverless is that it occurs in two different notions.
“Serverless was first used to describe applications that significantly or fully incorporate third-party,
cloud-hosted applications and services, to manage server-side logic and state. These are typically
“rich client” applications—think single-page web apps, or mobile apps—that use the vast ecosystem
of cloud-accessible databases, authentication services, and so on. These types of services can be described as
“Backend as a Service (BaaS) [40]”.
“Serverless can also mean applications where server-side logic is still written by the application developer,
but, unlike traditional architectures, it is run in stateless compute containers that are event-triggered,
ephemeral (may only last for one invocation), and fully managed by a third party. One way to think of
this is “Functions as a Service” or “FaaS.” AWS Lambda is one of the most popular implementations of
a Functions-as-a-Service platform at present, but there are many others, too [40]”.
In this section, we use the term Serverless computing in the notion of FaaS, and we will mainly
investigate the impact on resource utilization. The upcoming Section 5.2.2 will investigate Serverless
more in architectural terms. FaaS was specifically designed for event-driven applications that require
carrying out lightweight processing in response to an event [
]. FaaS is more fine-grained than
microservices and facilitates the creation of functions. Therefore, these fine-grained functions are
sometimes called nanoservices. These functions can be easily deployed and automatically scaled,
and provide the potential to reduce infrastructure and operation costs. Others like the deployment
unit approaches of Section 5.1.2—that are still always-on software components—functions are only
processed if there are active requests. Thus, FaaS can be much more cost efficient than just containerized
deployment approaches. According to a cost comparison of monolithic, microservice and FaaS
architectures case study by Villamizar et al. cost reductions up to 75% are possible [
]. On the
other hand, there are still open problems like the Serverless trilemma identified by Baldini et al.
The Serverless trilemma “captures the inherent tension between economics, performance, and synchronous
composition” [
] of serverless functions. One obvious problem stressed by Baldini et al. is the
“double spending problem” shown in Figure 6. This problem occurs when a serverless function
is calling another serverless function
synchronously. In this case, the consumer is billed for the
execution of
—although only
is consuming resources because
is waiting for the result of
To avoid this double spending problem, a lot of serverless applications delegate the composition of
fine-grained serverless functions into higher order functionality to client applications and edge devices
Appl. Sci. 2018,8, 1368 12 of 26
outside the scope of FaaS platforms. This composition problem leads to new—more distributed and
decentralized—forms of cloud-native architectures investigated in Section 5.2.2.
Figure 6. The double spending problem resulting from the Serverless trilemma [42].
5.2. A Review of the Architectural Evolution
The reader has seen in Section 5.1 that Cloud-native applications strived for a better resource
utilization mainly by applying more fine-grained deployment units in the shape of lightweight
containers (instead of virtual machines) or shape of functions in the case of FaaS approaches. Moreover,
these improvements of resource utilization rates had an impact on how architectures of cloud
applications evolved. Two major architectural trends of Cloud application architectures established
in the last decade. We will investigate Microservice architectures in Section 5.2.1 and Serverless
architectures in Section 5.2.2.
5.2.1. Microservice Architectures
Microservices form “an approach to software and systems architecture that builds on the well-established
concept of modularization but emphasize technical boundaries. Each module—each microservice—is implemented
and operated as a small yet independent system, offering access to its internal logic and data through a well-defined
network interface. This architectural style increases software agility because each microservice becomes an
independent unit of development, deployment, operations, versioning, and scaling [
]”. According to [
often mentioned benefits of microservice architectures are faster delivery, improved scalability, and greater
autonomy. Different services in a microservice architecture can be scaled independently from each other
according to their specific requirements and actual request stimuli. Furthermore, each service can be
developed and operated by different teams. Thus, microservices do not only have a technological but also
an organizational impact. These teams can make localized decisions per service regarding programming
languages, libraries, frameworks, and more. Thus, best-of-breed breaches are possible within each area of
responsibility on the one hand—on the other hand, this might increase the technological heterogeneity
naturally across the complete system, and corresponding long-term effects regarding maintainability of
such systems might be not even observed so far [4].
Alongside microservice architectures, we observed several other accompanying trends.
We already investigated containerization as such a trend in Section 5.1.2.
First generation
formed of individual services that were packed using container technologies (see
Figure 7). These services were then deployed and managed at runtime using container orchestration
tools, like Mesos. Each service was responsible for keeping track of other services, and invoking
them by specific communication protocols. Failure-handling was implemented directly in the services’
source code. With an increase of services per application, the reliable and fault-tolerant location and
invocation of appropriate service instances became a problem itself. If new services were implemented
Appl. Sci. 2018,8, 1368 13 of 26
using different programming languages, but made reusing existing discovery and failure-handling
code become increasingly difficult. Thus, freedom of choice and “polyglot programming” are an often
mentioned benefit of microservices but has its drawbacks that need to be managed.
Figure 7. Microservice architecture evolution—adapted from [31].
second generation microservice architectures
(see Figure 7) made use of discovery
services and reusable fault-tolerant communication libraries. Common discovery services (like Consul)
were used to register provided functionalities. During service invocation, all protocol-specific and
failure-handling features were delegated to an appropriate communication library, such as Finagle.
This simplified service implementation and reuse of boilerplate communication code across services.
third generation
(see Figure 7) introduced service proxies as transparent service intermediates
with the intent to improve software reusability. So-called sidecars encapsulate reusable service
discovery and communication features as self-contained services that can be accessed via existing
fault-tolerant communication libraries provided by almost every programming language nowadays.
Because of its network intermediary conception, sidecars are more than suited for monitoring the
behavior of all service interactions in a microservice application. This intermediary is precisely the
idea behind service mesh technologies such as Linkerd. These tools extend the notion of self-contained
sidecars to provide a more integrated service communication solution. Using service meshes, operators
have much more fine-grained control over the service-to-service communication including service
discovery, load balancing, fault tolerance, message routing, and even security. Thus, besides the pure
architectural point of view, the following tools, frameworks, services, and platforms (see Table 3) form
our current understanding of the term microservice:
Service discovery technologies let services communicate with each other without explicitly
referring to their network locations.
Container orchestration technologies automate container allocation and management tasks and
abstracting away the underlying physical or virtual infrastructure from service developers.
This is the reason that we see this technology as an essential part of any cloud-native application
stack (see Figure 3).
Appl. Sci. 2018,8, 1368 14 of 26
Monitoring technologies that are often based on time-series databases to enable runtime
monitoring and analysis of the behavior of microservice resources at different levels of detail.
Latency and fault-tolerant communication libraries let services communicate more efficiently
and reliably in permanently changing system configurations with plenty of service instances
permanently joining and leaving the system according to changing request stimuli.
Continuous-delivery technologies integrate solutions often into third-party services that
automate many of the DevOps practices typically used in a web-scale microservice production
environment [32].
Service proxy technologies encapsulate mainly communication-related features such as service
discovery and fault-tolerant communication and expose them over HTTP.
Finally, latest service mesh technologies built on sidecar technologies to provide a fully integrated
service-to-service communication monitoring and management environment.
Table 3. Some observable microservice engineering ecosystem components (adapted from [31]).
Example Tools, Frameworks, Services and Platforms (Last Access 07/11/2018)
Zookeeper (, Eureka (,
Consul (, etcd (, Synapse (https:
Kubernetes (,[
]), Mesos (,[
], Swarm
(, Nomad (
Graphite (, InfluxDB (,
Sensu (, cAdvisor (,
Prometheus (, Elastic Stack (
Fault tolerant
Finagle (, Hystrix (,
Proxygen (, Resilience4j (
delivery services
Ansible (, Circle CI (, Codeship (https://, Drone (, Spinnaker (, Travis CI
Service proxy Prana (, Envoy (
Service meshs Linkerd (, Istio (
Table 3shows that a complex tool-chain evolved to handle the continuous operation of
microservice-based cloud applications.
5.2.2. Serverless Architectures
Serverless computing is a cloud computing execution model in which the allocation of machine
resources is dynamically managed and intentionally out of control of the service customer. The ability
to scale to zero instances is one of the critical differentiators of serverless platforms compared with
container focused PaaS, or virtual machine focused IaaS services. Scale-to-zero enables avoiding billed
always-on components and therefore excludes the most expensive cloud usage pattern according to [
That might be one reason why the term “serverless” is getting more and more common since 2014 [
However, what is “serverless” exactly? Servers must still exist somewhere.
So-called serverless architectures replace server administration and operation mainly by using
Function-as-a-Service (FaaS) concepts [
] and integrating third-party backend services. Figure 4
showed the evolution of how resource utilization has been optimized over the last ten years ending
in the latest trend to make use of FaaS platforms. FaaS platforms apply time-sharing principles
and increase the utilization factor of computing infrastructures, and thus avoid expensive always-on
components. As already mentioned, at least one study showed that, due to this time-sharing, serverless
Appl. Sci. 2018,8, 1368 15 of 26
architectures can reduce costs by 70% [
]. A serverless platform is merely an event processing system
(see Figure 8). According to [
], serverless platforms take an event (sent over HTTP or received from
a further event source in the cloud). Then, these platforms determine which functions are registered to
process the event, find an existing instance of the function (or create a new one), send the event to the
function instance, wait for a response, gather execution logs, make the response available to the user,
and stop the function when it is no longer needed. Besides API composition and aggregation to reduce
API calls [42], event-based applications are especially very much suited for this approach [49].
Figure 8. Blueprint of a serverless platform architecture (adapted from [42]).
Serverless platform provision models can be grouped into the following categories:
Public (commercial) serverless services
of public cloud service providers provide computational
runtime-environments, also known as a function as a service (FaaS) platforms. Some well-known
type representatives include AWS Lambda, Google Cloud Functions, or Microsoft Azure
Functions. All of the mentioned commercial serverless computing models are prone to create
vendor lock-in (to some degree).
Open (source) serverless platforms
like Apache’s OpenWhisk or OpenLambda might be an
alternative with the downside that these platforms need infrastructure.
Provider agnostic serverless frameworks
provide a provider and platform agnostic way to
define and deploy serverless code on various serverless platforms or commercial serverless
services. Thus, these frameworks are an option to avoid (or reduce) vendor lock-in without the
necessity to operate an own infrastructure.
Thus, on the one hand, serverless computing provides some inherent benefits like resource
and cost efficiency, operation simplicity, and a possible increase of development speed and better
time-to-market [
]. However, serverless computing also comes along with some noteworthy
drawbacks, like runtime constraints, state constraints and still unsatisfactorily solved function
composition problems like the double spending problem (see Figure 6). Furthermore, resulting
serverless architectures have security implications. They increase attack surfaces and shift parts of the
application logic (service composing) to the client-side (which is not under complete control of the
service provider). Furthermore, FaaS increases vendor lock-in problems, client complexity, as well as
integration and testing complexity. Table 4summarizes some of the most mentioned benefits but also
drawbacks of FaaS from practitioner reportings [40].
Appl. Sci. 2018,8, 1368 16 of 26
Table 4. Serverless architecture benefits and drawbacks (mainly compiled from [40]).
Benefits Drawbacks
RESOURCE EFFIENCY (service side)
- auto-scaling based on event stimulus - maximum function runtime is limited
- reduced operational costs - startup latencies of functions must be considered
- scale to zero capability (no always-on) - function runtime variations
- functions can not preserve a state across function calls
- external state (cache, key/value stores, etc.) can
compensate this but is a magnitude slower
- double spending problems (FaaS functions call other
FaaS functions)
OPERATION (service side)
- simplified deployment - increased attack surfaces
- simplified operation (see auto-scaling) - each endpoint introduces possible vulnerabilities
- missing protective barrier of a monolithic server
- parts of the application logic are shifted to the client-side
(that is not under control of the service provider)
- increased vendor lock-in (currently no FaaS standards
for API gateways and FaaS runtime environments)
DEVELOPMENT SPEED (service side)
- development speed - increased client complexity
- simplified unit testing of stateless FaaS functions - application logic is shifted to the client-side
- better time to market - code replication on client side across client platforms
- control of application workflow on client side to avoid
double-sending problems of FaaS computing
- increased integration testing complexity
- missing integration test tool-suites
Furthermore, Figure 9shows that serverless architectures (and microservice architectures
as well) require a cloud application architecture redesign, compared to traditional e-commerce
applications. Much more than microservice architectures, serverless architectures integrate third-party
backend services like authentication or database services intentionally. Functions on FaaS platforms
provide only very service specific, security relevant, or computing intensive functionality. In fact,
all functionality that would have been provided classically on a central application server is now
provided as a lot of isolated micro- or even nanoservices. The integration of all these isolated services
as meaningful end-user functionality is delegated to end devices (very often in the shape of native
mobile applications or progressive web applications). In summary, we can see the following observable
engineering decisions in serverless architectures:
Former cross-sectional but service-internal (or via a microservice provided) logic like
authentication or storage is sourced to external third party services.
Even nano- and microservice composition is shifted to end-user clients or edge devices. This means
that even service orchestration is not done anymore by the service provider itself but by the service
consumer via provided applications. This end-user orchestration has two interesting effects: (1) the
service consumer now provides resources needed for service orchestration; (2) because the service
composition is done outside the scope of the FaaS platform, still unsolved FaaS function composition
problems (like the double spending problem) are avoided.
Such client or edge devices are interfacing third party services directly.
Endpoints of very service specific functionality is provided via API gateways. Thus, HTTP- and
REST-based/REST-like communication protocols are generally preferred.
Only very domain or service specific functions are provided on FaaS platforms. Mainly, when
this functionality is security relevant and should be executed in a controlled runtime environment
by the service provider, or the functionality is too processing or data-intensive to be executed on
Appl. Sci. 2018,8, 1368 17 of 26
consumer clients or edge devices, or the functionality is so domain-, problem-, or service-specific
that simply no external third-party service exists.
Client' (Brow ser)
Application Server
Relationa l' Data base
Authentication$ Service
Purchase Database
Product Database
The authentication logic can be
replaced with a 3rd pa rty authenti cation
BaaS (like Auth0).
The clientis all owed dire ct
acces s to a subset of our
database . The database is fully
3rd party hosted.
Server application
logic now moves
to the client
making it of ten a
native mobile app
or a si ngle-page
web application.
Some functionalitymigh t be kept in the
server“. It might be compute intensive or
requires acc ess to a significant amo unt of
data like a search function.
Such functionalityis prov ided as FaaS
functionsth atof ten respond to HTTP
Some functionalitymigh t be kept
in the serve rforsec urity reasons
or for interfacing further3rd party
An API Gate way is basic ally a web server that re ceives
HTTP requests and routes them to su bsequent FaaS
functionsor othe r backen d service s.
Figure 9.
Serverless architectures result in a different and less centralized composition of application
components and backend services compared with classical tiered application architectures.
Finally, the reader might observe the trend in serverless architectures that this kind of architecture
is more decentralized and distributed, makes more intentional use of independently provided services,
and is therefore much more intangible (more cloudy) compared with microservice architectures.
6. The Road Ahead
So far, we have identified and investigated two major trends. First, cloud computing and its related
application architecture evolution can be seen as a steady process to optimize resource utilization in
cloud computing. This was visualized in Figure 4and discussed in Section 5.1. Second, in Section 5.2,
it was emphasized that this resource utilization improvement results over time in an architectural
evolution of how cloud applications are being built and deployed. We ob s e r ve d a s h if t f r o m m o n o l i th i c
SOA, via independently deployable microservices towards so-called serverless architectures that are more
decentralized and distributed, and make more intentional use of independently provided services.
The question is whether and how are these trends continuing? To forecast the future is challenging,
but having current trends and the assumption that these trends will go on to some degree make it
a bit easier. This forecast is done in Section 6.1 for the optimization of resource utilization trend, and
Section 6.2 will take a look at how cloud application architectures may evolve in the future by merely
extrapolating the existing SOA-microservice-serverless path.
6.1. Unikernels—The Overlooked Deployment Unit?
Operating system virtualization based container technologies have massively influenced the
resource utilization optimization trend. However, containers are not about virtualization from a cloud
application deployment point of view. They are about a standardized and self-contained way to define
deployment units. However, are containers the only solution and the most resource efficient solution
already existing? The answer is no, and roads ahead might follow directions with the same intent to
define standardized and self-contained deployment units but with better resource utilization.
One option would be unikernels. A unikernel is a specialized, single address space machine
image constructed via library operating systems. The first such systems were Exokernel (MIT Parallel
and Distributed Operating Systems group) and Nemesis (the University of Cambridge, University of
Appl. Sci. 2018,8, 1368 18 of 26
Glasgow, Swedish Institute of Computer Science and Citrix Systems) in the late 1990s. The basic idea
is that a developer selects a minimal set of libraries which correspond to the OS constructs required for
their application to run. These libraries are then compiled with the application and configuration code
to build sealed, fixed-purpose images (unikernels) which run directly on a hypervisor or hardware
without an OS. Thus, unikernels are self-contained deployment units like containers we investigated in
Section 5.1.2 with the advantage to avoid a container overhead, a container runtime engine, and a host
operating system (see Figure 5). Thus, interesting aspects to investigate on the road ahead would be:
Because unikernels make operating systems and container runtime engines obsolete, this could
further increase resource utilization rates.
FaaS platforms workers are normally container based. However, unikernels are a deployment
option as well. Interesting research and engineering directions would be how to combine
unikernels with FaaS platforms to apply the same time-sharing principles.
However, although there is research following the long-term trend to improve resource
utilization [
], most cloud computing-related unikernel research [
] mainly investigates
unikernels as a security option to reduce attack surfaces (which are increased by serverless and
microservice architectures as we have seen in Section 5.2). However, the resource optimization effect
of unikernels might be still not aware to cloud engineers. Other than container technology, unikernel
technology is not hyped.
6.2. Overcoming Conceptual Centralized Approaches
This section investigates some long-term trends in cloud and service computing research through
the support of quantitative trend analysis. Scopus has been used to count the number of published
papers dealing with some relevant terms over the years. We searched for the following terms in titles,
abstracts, or keywords limited to the computer science domain:
Cloud computing—to collect the amount of cloud computing-related research in general.
SOA—to collect the service computing related research, which is still a major influencing concept
in cloud computing.
Microservices—to collect microservice related research (which is more modern and pragmatic
interpretation of SOA and very popular in cloud computing).
Serverless—to collect serverless architecture related research (which is the latest observable
architecture trend in cloud computing).
Peer-to-peer (P2P)—to collect P2P related research (because recently more decentralizing concepts
are entering cloud computing).
Blockchain—to collect blockchain related research (which is the latest observable P2P related
research trend/hype).
The presented architectural evolution can be seen as the perpetual fight of centralism and
decentralism. Centralized architectures are known since decades. This kind of architectures makes
system engineering easier. Centralized architectures have fewer problems with data synchronization
and data redundancy. They are easier to handle from a conceptual point of view. The client–server
architecture is still one of the most basic but dominant centralized architectural styles.
However, at various point in times, centralized approaches are challenged by more decentralized
approaches. Take the mainframe versus personal computer as one example dating back to the 1980s.
Currently, such decentralizing trends often correlate with terms like mobile cloud computing [
the Internet of Things (IoT) and edge computing. Edge computing is a method for cloud computing
systems to hand over control of services from some central nodes (the “core”) to the “edge” of the
Internet which makes contact with the physical world. Data comes in from the physical world
via various sensors, and actions are taken to change physical state via various forms of actuators.
By performing analytics and knowledge generation at the edge, communications bandwidth between
Appl. Sci. 2018,8, 1368 19 of 26
systems and central data processing can be reduced. Edge computing takes advantage of proximity to
the physical items of interest also exploiting relationships those items may have to each other but needs
more decentralized processing approaches. Thus, the question arises how conceptually centralized
service-oriented solutions can be adapted for such more decentralized problems [57,58].
Figure 10 shows the number of papers per year for research that is dealing with cloud computing
in general and relates it with serverless architectures, P2P based related research (including blockchains
as a latest significant P2P trend). We see a rise in interest in research about peer-to-peer (that means
decentralized) approaches starting in 2000 that reached its peak in 2010. What is interesting to us is that
peer-to-peer based research decreased with the beginning starting increase of cloud computing related
research in 2008. Thus, cloud computing (mainly a concept to provide services in a conceptually
centralized manner) decreased the interest in peer-to-peer related research. P2P computing is
a distributed application architecture that partitions tasks or workloads between peers. Peers are
equally privileged and equipotent participants in the application. Peers make a portion of their
resources, such as processing power, disk storage or network bandwidth, directly available to other
network participants, without the need for central coordination by servers or stable hosts. Thus, peers
are both suppliers and consumers of resources, in contrast to the cloud computing consumer-service
model. However, leveraging P2P technologies for service provisioning has been identified of particular
interest for research, and one of the challenges is how to enable the service providers to adapt
themselves in response to changing service demand [59].
Figure 10.
Trends of papers dealing with the terms cloud-computing, serverless, P2P, and blockchain
(as the latest P2P based trend). Retrieved from Scopus (limited to computer science), 2018 extrapolated.
One astonishing curve in Figure 10 is the research interest in serverless solutions. Although
on a substantially lower absolute level, a constant research interest in serverless solutions can be
observed since 1995. To have “serverless” solutions seems to be a long-standing dream in computer
science. The reader should be aware that the notion of serverless changed over time. Serverless has
been used until 2000 very often in file storage research contexts. With the rise of P2P based solutions,
the term serverless has been mainly used alongside P2P based approaches. In addition, since 2015,
much momentum has been given alongside cloud-native application architectures (see Figure 11).
Thus, nowadays, it is mainly used in the notion described in Sections 5.1.3 and 5.2.2.
Appl. Sci. 2018,8, 1368 20 of 26
Figure 11.
Trends of papers dealing with cloud-computing, SOA, microservices and serverless.
Retrieved from Scopus (limited to computer science), 2018 extrapolated.
Figure 11 shows some further interesting correlation. With the rise of cloud computing in 2008,
there is a steady decline in SOA related research. Thus, to deploy monolithic SOA applications in the
cloud was not seen useful from the very beginning of cloud computing. However, it took almost five
years in research to investigate further and more cloud suited application architectures (microservice
and serverless architectures).
If we look at Figures 10 and 11, we see a decline of classical architecture approaches like SOA
and a rising interest in new architecture styles like microservice and serverless architectures. Again,
especially serverless architectures come along with some decentralizing philosophy that is observable
in P2P based research as well. The author does not think that those cloud application architectures
will strive for the same level of decentralizing and distribution like peer-to-peer based approaches.
However, a more distributed service-to-service trend is observable in cloud application architecture
research [
]. Thus, the cloud computing trend started a decline in SOA (see Figure 11) and P2P (see
Figure 10). However, if we compare SOA and P2P (including blockchain related research), we see an
increasing interest in decentralized solutions again (see Figure 12).
If we are taking all this together to forecast the road ahead, we could assume that new architecture
styles like microservices and serverless architectures will dominate service computing. Moreover,
SOA seems to die. However, we see a resurgence of interest in decentralized approaches known
from P2P related research. Therefore, the author assumes that especially serverless architectures will
more and more evolve into cloud application architectures that follow distributed service-to-service
principles (much more in the notion of peer-to-peer).
Figure 12.
Trends of papers dealing with SOA, and P2P (including blockchain). Retrieved from Scopus
(limited to computer science), 2018 extrapolated.
Appl. Sci. 2018,8, 1368 21 of 26
7. Related Work
As far as the author knows, no survey focused intentionally observable trends in cloud
applications’ architectures over the last decade from a “big picture” architectural evolution point
of view. This paper grouped that evolution mainly into the following point of views:
Resource utilization optimization approaches like
approaches have
been investigated in Section 5.1.
The architectural evolution of cloud applications that is dominated by
evolving into
serverless architectures
. Both architectural styles have been investigated in
Section 5.2.
For all of these four specific aspects (containerization, FaaS, microservices, serverless
architectures), there exist surveys that should be considered by the reader. The studies and
surveys [
] deal mainly with containerization and its accompanying resource efficiency.
Although FaaS is quite young and could be only little reflected in research so far, there exist first survey
papers [
] dealing with FaaS approaches deriving some open research questions regarding tool
support, performance, patterns for serverless solutions, enterprise suitability and whether serverless
architectures will extend beyond traditional cloud platforms and architectures.
Service composition provides value-adding and higher-order services by composing basic
services that can be even pervasively provided by various organizations [
]. Furthermore, service
computing is quite established, and there are several surveys on SOA related aspects [
]. However,
more recent studies focus mainly on microservices. Refs. [
] focus especially on the architectural
point of view and the relationship between SOA and microservices. All of these papers are great to
understand the current microservice “hype” better. It is highly recommended to study these papers.
However, these papers are somehow bound to microservices and do not take the “big picture” of
general cloud application architecture evolution into account. Ref. [
] provides a great overview
of microservices and even serverless architectures, but serverless architectures are subsumed as
a part of microservices to some degree. The author is not quite sure whether serverless architectures
do not introduce fundamental new aspects into cloud application architectures that evolve from
the “scale-to-zero” capability on the one hand and the unsolved function composition aspects (like
the double spending problem) on the other hand. Resulting serverless architectures push former
conceptually centralized service composing logic to end user and edge devices out of direct control of
the service provider.
8. Conclusions
Two major trends in cloud application architecture have been identified and investigated. First,
cloud computing and its related application architecture evolution can be seen as a steady process to
optimize resource utilization in cloud computing. Unikernels—a technology from late 1990s—might
be one option for future improvements. Like containers, they are self-contained but avoid a container
overhead, a container runtime engine, and even a host operating system. However, astonishing little
research is conducted in that field. Second, each resource utilization improvement resulted in an
architectural evolution of how cloud applications are being built and deployed. We observed a shift
from monolithic SOA (machine virtualization), via independently deployable microservices (container)
towards so-called serverless architectures (FaaS function). Especially serverless architectures are more
decentralized and distributed and make more intentional use of independently provided services.
Furthermore, service orchestration logic is shifted to end devices outside the direct scope of the service
provisioning system.
Thus, new architecture styles like microservice and serverless architectures might dominate
service computing. Furthermore, a resurgence of interest in decentralized approaches known from
P2P related research is observable. That is astonishing because, with the rise of cloud computing (and
its centralized service provisioning concept), the research interest in peer-to-peer based approaches
Appl. Sci. 2018,8, 1368 22 of 26
(and its decentralization philosophy) decreased. However, this seems to change and might be an
indicator where cloud computing could be heading in the future. Baldini et al. [
] asked the interesting
question, whether serverless extend beyond traditional cloud platforms. If we are looking at the trends
investigated in Section 6.2, this seems likely. Modern cloud applications might lose clear boundaries
and could evolve into something that could be named service-meshes. Such service-meshes would
be composed of small and fine-grained services provided by different and independent providers.
Moreover, mobile and edge devices not explicitly belonging to the service provisioning system anymore
do the service composition and orchestration. This path might have already started with FaaS and
serverless architectures. This forecast might sound astonishing familiar. In the 1960s, the Internet was
designed to be decentralized and distributed.
This research was funded by the German Federal Ministry of Education and Research under Grant No.
13FH021PX4 (Project CloudTRANSIT).
I would like to thank Peter-Christian Quint, (Lübeck University of Applied Sciences,
Germany), Dirk Reimers ( GmbH, Lübeck, Germany), Derek Palme (fat IT solutions GmbH,
Kiel, Germany), Thomas Finnern (wilhelm.Tel GmbH, Stadtwerke Norderstedt, Germany), René Peinl (Hof
University of Applied Sciences, Germany), Bob Duncan (University of Aberdeen, UK), Magnus Westerlund
(Arcada University of Applied Sciences, Helsinki, Finland), and Josef Adersberger (QAWare GmbH, Munich,
Germany) for their direct or indirect contributions to our research. Without their hard work, their inspiring ideas,
their practitioner awareness, or their outside-the-box-thinking, this contribution would not have been possible.
Conflicts of Interest:
The author declares no conflict of interest. The founding sponsor had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
The following abbreviations are used in this manuscript:
AMQP Advanced Message Queuing Protocol
API Application Programming Interface
GCE Google Compute Engine
CDMI Cloud Data Management Interface
CIMI Cloud Infrastructure Management Interface
CNA Cloud-Native Application
DLT Distributed Ledger Technology (aka blockchain)
IaaS Infrastructure as a Service
IoT Internet of Things
FaaS Function as a Service
HTTP Hypertext Transfer Protocol
NIST National Institute of Standards and Technology
OCI Open Container Initiative
OCCI Open Cloud Computing Interface
OVF Open Virtualization Format
OS Operating System
P2P Peer-to-Peer
PaaS Platform as a Service
QoS Quality of Service
REST Representational State Transfer
SaaS Software as a Service
SOA Service-Oriented Architecture
SOC Service-Oriented Computing
TOSCA Topology and Orchestration Specification for Cloud Applications
UCAML Unified Cloud Application Modeling Language
VM Virtual Machine
WS-BPEL Web Service-Business Process Execution Language
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[sangyoon]As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution[sangyoon] that is encountered due to resource provisioning. [sangyoon]In serverless computing, functions can be composed as workflows to process a complex task, and the cold start problem has a significant influence on workflow response time because the cold start can occur in each function.The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.
Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.
In modern societies, the rampant growth of data management technologies—that have access to data sources from a plethora of heterogeneous systems—enables data analysts to leverage their advantages to new areas and critical infrastructures. However, there is no global reference standard for data platform technology. Data platforms scenarios are characterized by a high degree of heterogeneity at all levels (middleware, application service, data/semantics, scalability, and governance), preventing deployment, federation, and interoperability of existing solutions. Although many initiatives are dealing with developing data platform architectures in diversified application domains, not many projects have addressed integration in port environments with the possibility of including cognitive services. Unlike other cases, port environment is a complex system that consists of multiple heterogeneous critical infrastructures, which are connected and dependent on each other. The key pillar is to define the design of a secure interoperable system facilitating the exchange of data through standardized data models, based on common semantics, and offering advanced interconnection capabilities leading to cooperation between different IT/IoT/Objects platforms. This contribution deals with scalability, interoperability, and standardization features of data platforms from a business point of view in a smart and cognitive port case study. The main goal is to design an innovative platform, named DataPorts, which will overcome these obstacles and provide an ecosystem where port authorities, external data platforms, transportation, and logistics companies can cooperate and create the basis to offer cognitive services. The chapter relates to knowledge and learning as well as to systems, methodologies, hardware, and tools cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation & Deployment Agenda (Milano et al., Strategic research, innovation and deployment agenda - AI, data and robotics partnership. Third release. Big Data Value Association, 2020).
Nowadays the advancement of technologies such as the Internet of Things and Machine Learning has allowed the development of increasingly complex systems to simplify and automate countless different processes. On the other hand, the affirmation of Cloud Computing, and therefore the delivery of different services through the Internet as tools and applications like data storage, servers, databases, networking, and software has made it possible to reach even higher levels in terms of cost savings, increased productivity, speed and efficiency, performance, and security. This paper illustrates the practical example of the possible On-Cloud migration of an On-premise application, using the services and patterns offered by Amazon. The application in exam has the purpose of automatically managing access and booking of a room, and possibly a parking lot, in a tourist complex. Since the original project did not foresee mechanisms of autoscaling of the computational resources, there is no monitoring mechanism or database redundancy, the usage of a Cloud approach was necessary to make possible the automatic scaling of resources based on the unpredictable load of requests from the users of the platform and to achieve performance, security and availability increase of the whole system.
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With serverless-based applications are increasing their popularity, little is known on testing practices and tools available to test serverless functions. This work aims to identify testing approaches for serverless functions built for the Amazon Web Services cloud platform, and to demonstrate how to implement them to a full-stack application. For this purpose, we implemented unit, integration and system tests to an existing open source application providing insights of the testing practices and and tools applicable. Results shows that all the testing practices are applicable, even if there is a lack of tools to support end-to-end tests, especially for debugging.
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In the late-1950s, leasing time on an IBM 704 cost hundreds of dollars per minute. Today, cloud computing, that is, using IT as a service, on-demand and pay-per-use, is a widely used computing paradigm that offers large economies of scale. Born from a need to make platform as a service (PaaS) more accessible, fine-grained, and affordable, serverless computing has garnered interest from both industry and academia. This article aims to give an understanding of these early days of serverless computing: what it is, where it comes from, what is the current status of serverless technology, and what are its main obstacles and opportunities.
Technical Report
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The project CloudTRANSIT dealt with the question of how to transfer cloud applications and services at runtime without downtime across cloud infrastructures from different public and private cloud service providers. This technical report summarizes the outcomes of approximately 20 research papers that have been published throughout the project. This report intends to provide an integrated birds-eye view on these-so far-isolated papers. The report references the original papers where ever possible. This project also systematically investigated practitioner initiated cloud application engineering trends of the last three years that provide several promising technical opportunities to avoid cloud vendor lock-in pragmatically. Especially European cloud service providers should track such kind of research because of the technical opportunities to bring cloud application workloads back home to Europe. Such workloads are currently often deployed and inherently bound to U.S. providers. Intensified EU General Data Protection (GDPR) policies, European Cloud Initiatives, or "America First" policies might even make this imperative. So, technical solutions needed for these scenarios that are manageable not only by large but also by small and medium-sized enterprises. Therefore, this project systematically analyzed commonalities of cloud infrastructures and cloud applications. Latest evolutions of cloud standards and cloud engineering trends (like containerization) were used to derive a cloud-native reference model (ClouNS) that guided the development of a pragmatic cloud-transferability solution. This solution intentionally separated the infrastructure-agnostic operation of elastic container platforms (like Swarm, Kubernetes, Mesos/Marathon, etc.) via a multi-cloud-scaler and the platform-agnostic definition of cloud-native applications and services via an unified cloud application modeling language. Both components are independent but complementary. Because of their independence, they can even contribute (although not intended) to other fields like moving target based cloud security-but also distributed ledger technologies (block-chains) made provide options here. The report summarizes the main outcomes and insights of a proof-of-concept solution to realize transferability for cloud applications and services at runtime without downtime.
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In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO2 emissions and the daily energy cost.
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Microservices are an architectural approach emerging out of service-oriented architecture, emphasizing self-management and lightweightness as the means to improve software agility, scalability, and autonomy. This article examines microservice evolution from the technological and architectural perspectives and discusses key challenges facing future microservice developments.
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As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.
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Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style. We aim at characterizing different microservice architectural style patterns and the principles that guide their definition. We conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles. We present two key contributions. Firstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified. Secondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. We can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.
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Client-side data deduplication enables cloud storage services (e.g., Dropbox) to achieve both storage and bandwidth savings, resulting in reduced operating cost and high level of user satisfaction. However, the deduplication checks (i.e., the corresponding essential message exchange) create a side channel, exposing the privacy of file existence status to the attacker. In particular, the binary response from the deduplication check reveals the information about the existence of a copy of the file in the cloud storage. This behavior can be exploited to launch further attacks such as learning the sensitive file content and establishing a covert channel. While current solutions provide only weaker privacy or rely on unreasonable assumptions, we propose RAndom REsponse (RARE) approach to achieve stronger privacy. The idea behind our proposed RARE solution is that the uploading user sends the deduplication request for two chunks at once. The cloud receiving the deduplication request returns the randomized deduplication response with the careful design so as to preserve the deduplication gain and at the same time minimize the privacy leakage. Our analytical results confirm privacy guarantee and results show that both deduplication benefit and privacy of RARE can be preserved.