Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

The “E3D+VET” Erasmus+ project: Interdisciplinary teaching and learning in VET centres through 3D printing

The "E3D+VET" (Erasmus+ for the immersion in 3D printing of VET centres) is an Erasmus+ KA2 project aimed at developing educational resources for the VET system, providing new competences to both teachers and students and serving as important means of innovation and acquisition of effective knowledge on interdisciplinary STEAM topics. The project started on October 2017 and it will last up to the end of March 2020. In this work, we present the main outcomes from the project activities carried out so far. In particular, after a description of the general objectives of the project, we introduce the methodology developed for making 3D-printing a valuable resource for supporting physics teaching in a highly motivating learning environment and three didactical exercises as examples of 3D-printing based tools that can support teachers in their physics class. As a part of the project plan, here we finally present the preliminary training program specifically designed for the teacher professional development about the knowledge of 3D-printing potential for an effective teaching of physics contents and, at the same time, for improving student transversal abilities

Incremental Calibration of Architectural Performance Models with Parametric Dependencies

Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.Comment: Manar Mazkatli is supported by the German Academic Exchange Service (DAAD

Open-Source Software as Catalyzer for Technology Transfer: Kieker’s Development and Lessons Learned

The monitoring framework Kieker commenced as a joint diploma thesis of the University of Oldenburg and a telecommunication provider in 2006,and grew toward a high-quality open-source project during the last years. Meanwhile, Kieker has been and is employed in various projects.Several research groups constitute the open-source community to advance the Kieker framework. In this paper, we review Kieker's history,development, and impact as catalyzer for technology transfer

“May I Help You?”: Exploring the Effect of Individuals’ Self-Efficacy on the Use of Conversational Agents

Conversational agents (CAs) increasingly permeate our lives and offer us assistance for a myriad of tasks. Despite promising measurable benefits, CA use remains below expectations. To complement prior technology-focused research, this study takes a user-centric perspective and explores an individual’s characteristics and dispositions as a factor influencing CA use. In particular, we investigate how individuals’ self-efficacy, i.e., their belief in their own skills and abilities, affects their decision to seek assistance from a CA. We present the research model and study design for a laboratory experiment. In the experiment, participants complete two tasks embedded in realistic scenarios including websites with integrated CAs – that they might use for assistance. Initial results confirm the influence of individuals’ self-efficacy beliefs on their decision to use CAs. By taking a human-centric perspective and observing actual behavior, we expect to contribute to CA research by exploring a factor likely to drive CA use

Continuous Integration of Architectural Performance Models with Parametric Dependencies – The CIPM Approach

Explicitly considering the software architecture supports efficient assessments of quality attributes. In particular, Architecture-based Performance Prediction (AbPP) supports performance assessment for future scenarios (e.g., alternative workload, design, deployment, etc.) without expensive measurements for all such alternatives. However, accurate AbPP requires an up-to-date architectural Performance Model (aPM) that is parameterized over factors impacting performance like input data characteristics. Especially in agile development, keeping such a parametric aPM consistent with software artifacts is challenging due to frequent evolutionary, adaptive and usage-related changes. The shortcoming of existing approaches is the scope of consistency maintenance since they do not address the impact of all aforementioned changes. Besides, extracting aPM by static and/or dynamic analysis after each impacting change would cause unnecessary monitoring overhead and may overwrite previous manual adjustments. In this article, we present our Continuous Integration of architectural Performance Model (CIPM) approach, which automatically updates the parametric aPM after each evolutionary, adaptive or usage change. To reduce the monitoring overhead, CIPM calibrates just the affected performance parameters (e.g., resource demand), using adaptive monitoring. Moreover, CIPM proposes a self-validation process that validates the accuracy, manages the monitoring and recalibrates the inaccurate parts. As a result, CIPM will automatically keep the aPM up-to-date throughout the development time and operation time, which enables AbPP for a proactive identification of upcoming performance problems and evaluating alternatives at low costs. CIPM is evaluated using three case studies, considering (1) the accuracy of the updated aPMs and associated AbPP and (2) the applicability of CIPM in terms of the scalability and the required monitoring overhead

Variability in Behavior of Application Service Workload in a Utility Cloud

Using the elasticity feature of a utility cloud, users can acquire and release resources as required and pay for what they use. Applications with time-varying workloads can request for variable resources over time that makes cloud a convenient option for such applications. The elasticity in current IaaS cloud provides mainly two options to the users: horizontal and vertical scaling. In both ways of scaling the basic resource allocation unit is fixed-sized VM, it forces the cloud users to characterize their workload based on VM size, which might lead to under-utilization or over-allocation of resources. This turns out to be an inefficient model for both cloud users and providers. In this paper we discuss and calculate the variability in different kinds of application service workload. We also discuss different dynamic provisioning approaches proposed by researchers. We conclude with a brief introduction to the issues or limitations in existing solutions and our approach to resolve them in a way that is suitable and economic for both cloud user and provider