André Bauer

André Bauer
University of Wuerzburg | JMU · Department of Computer Science

Dr. rer. nat.

About

39
Publications
20,634
Reads
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475
Citations
Introduction
Postdoc and head of the Data Science Engineering Group at Professor Kounev’s Chair of Software Engineering at the University of Würzburg.
Additional affiliations
October 2016 - December 2020
University of Wuerzburg
Position
  • PhD Student
Education
October 2014 - September 2016
University of Wuerzburg
Field of study
  • Computer Science
October 2012 - September 2014
University of Wuerzburg
Field of study
  • Computer Science

Publications

Publications (39)
Conference Paper
Full-text available
Simple, threshold-based auto-scaling mechanisms as mainly used in practice bring no features to overcome resource provisioning delays and non-linear scalability of a software service. In this tutorial paper, we guide the reader step-by-step through the design and evaluation of a proactive and application-aware auto-scaling mechanism. First, we intr...
Conference Paper
Full-text available
Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the “No-Free-Lunch-Theorem” from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting metho...
Preprint
Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. Howev...
Article
Objectives: This study aims to improve early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms. Methods: Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modeling. Cardiac surgery-assoc...
Conference Paper
Full-text available
Performance regression testing is a foundation of modern DevOps processes and pipelines. Thus, the detection of change points, i.e., updates or commits that cause a significant change in the performance of the software, is of special importance. Typically, validating potential change points relies on humans, which is a considerable bottleneck and c...
Conference Paper
Full-text available
The research field of data analytics has grown significantly with the increase of gathered and available data. Accordingly, a large number of tools, metrics, and best practices have been proposed to make sense of this vast amount of data. To this end, benchmarking and standardization are needed to understand the proposed approaches better and conti...
Conference Paper
Full-text available
Autoscaling is a task of major importance in the cloud computing domain as it directly affects both operating costs and customer experience. Although there has been active research in this area for over ten years now, there is still a significant gap between the proposed methods in the literature and the deployed autoscalers in practice. Hence, man...
Conference Paper
Full-text available
Commits to the MongoDB software repository trigger a collection of automatically run tests. Here, the identification of commits responsible for performance regressions is paramount. Previously, the process relied on manual inspection of time series graphs to identify significant changes, later replaced with a threshold-based detection system. Howev...
Conference Paper
Full-text available
In the next few years, both the number of IoT devices and the performance of quantum computers will increase. Both technologies pose a challenge to our current crypto-strategies. Therefore, post-quantum n-ton communication encryption is a crucial field of research. Here, the development of new schemes and the analysis, and comparison of existing sc...
Article
Full-text available
Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact...
Conference Paper
Full-text available
In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case....
Thesis
Full-text available
These days, we are living in a digitalized world. Both our professional and private lives are pervaded by various IT services, which are typically operated using distributed computing systems (e.g., cloud environments). Due to the high level of digitalization, the operators of such systems are confronted with fast-paced and changing requirements. I...
Conference Paper
Full-text available
Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Ta...
Article
Full-text available
Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are...
Conference Paper
Full-text available
One central problem of machine learning is the inherent limitation to predict only what has been learned-stationarity. Any time series property that eludes stationarity poses a challenge for the proper model building. Furthermore, existing forecasting methods lack reliable forecast accuracy and time-to-result if not applied in their sweet spot. In...
Conference Paper
Full-text available
Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is...
Article
Full-text available
Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, Function-as-a-Service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage comp...
Article
Full-text available
The rapid adoption and the diversification of cloud computing technology exacerbate the importance of a sound experimental methodology for this domain. This work investigates how to measure and report performance in the cloud, and how well the cloud research community is already doing it. We propose a set of eight important methodological principle...
Conference Paper
Full-text available
Nowadays, in order to keep track of the fast-changing requirements of Internet applications, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources to the resource demand. The straightforward approach is to deploy a set of common and open-source single-service auto-scalers for each service independently. How...
Conference Paper
Full-text available
In a fast-paced world, software systems require autonomic management. To enable accurate and proactive au-tonomic systems, reliable time series forecasting methods are needed. In this tutorial paper, we guide the reader step-by-step through different forecasting steps. In each step, we highlight best practices and present available approaches. That...
Conference Paper
Full-text available
With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of th...
Conference Paper
Full-text available
Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based o...
Conference Paper
Full-text available
Proactive adaptation improves the system performance of Autonomic Computing systems as it recognizes adaptation concerns in advance and adapts or prepares adaptation accordingly. To support this, forecasting methods use historical data to predict future system states. According to the "No-Free-Lunch-Theorem", there is no general forecasting method...
Preprint
Full-text available
The growing popularity of workflows in the cloud domain promoted the development of sophisticated autoscaling policies that allow automatic allocation and deallocation of resources. However, many state-of-the-art autoscaling policies for workflows are mostly plan-based or designed for batches (ensembles) of workflows. This reduces their flexibility...
Chapter
Full-text available
In a fast-paced world, software systems require autonomic management. In order to enable accurate and proactive autonomic systems , reliable time series forecasting methods are required. However, most forecasting methods have either a high variance of accuracy and/or time-to-result. To this end, forecasting methods with robust performance are deman...
Article
Full-text available
Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of re...
Conference Paper
Performance engineering researchers propose and employ various methods to analyze, model, optimize and manage the performance of modern distributed applications. In order to evaluate these methods in realistic scenarios, researchers rely on reference applications. Existing testing and benchmarking applications are usually difficult to set up and ei...
Article
Full-text available
As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models...
Conference Paper
Full-text available
Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed cloud applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customizat...
Conference Paper
Full-text available
Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demons...
Article
Full-text available
In only a decade, cloud computing has emerged from a pursuit for a service-driven information and communication technology (ICT), becoming a significant fraction of the ICT market. Responding to the growth of the market, many alternative cloud services and their underlying systems are currently vying for the attention of cloud users and providers....
Article
Full-text available
Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However, in p...
Conference Paper
Full-text available
Nowadays, to keep track with the fast changing requirements of internet applications, auto-scaling is an essential mechanism for adapting the number of provisioned resources to the resource demand. In the context of public clouds, there exist different natures of cost-models for charging resources. However, the accounted resource units and charged...
Conference Paper
Full-text available
In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on h...

Questions

Questions (2)
Question
Hi,
I could require some input/hints.
The context:
I recommend methods based on some features of the input data. To this end, I calculate the performance of each method. That is, the recommendation system gets for each data point its properties and which method works best.
The problem:
There is one method that outperforms the other methods. I have 10 methods and on 1/5 of the data this method yields the best performance. On average, however, this method does not have the best performance.
My problem:
Is this the best method? How do I argue against or for it?
Thank you in advance,
André
Question
Hi,
I'm looking for a easy-to-setup FaaS platforam in order to conduct experiments in a controlled environment.
Thanks & Best,
André

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