Automated Transformation of Descartes Modeling Language to Palladio Component Model

ABSTRACT Model-based performance predictions and reconfigurations enable optimizing resource efficiency while ensuring that Quality-of-Service demands are met in today's complex ITsystems. The Descartes Modeling Language (DML) and the Palladio Component Model (PCM) are two architectural performance modeling formalisms applied in this context. This paper compares DML to PCM concerning similarities, differences and semantic gaps. Based on this, we propose a mapping from DML to PCM for which we implemented a tool realizing an automated transformation

Diffusion maps for changing data

Graph Laplacians and related nonlinear mappings into low dimensional spaces have been shown to be powerful tools for organizing high dimensional data. Here we consider a data set X in which the graph associated with it changes depending on some set of parameters. We analyze this type of data in terms of the diffusion distance and the corresponding diffusion map. As the data changes over the parameter space, the low dimensional embedding changes as well. We give a way to go between these embeddings, and furthermore, map them all into a common space, allowing one to track the evolution of X in its intrinsic geometry. A global diffusion distance is also defined, which gives a measure of the global behavior of the data over the parameter space. Approximation theorems in terms of randomly sampled data are presented, as are potential applications.Comment: 38 pages. 9 figures. To appear in Applied and Computational Harmonic Analysis. v2: Several minor changes beyond just typos. v3: Minor typo corrected, added DO

Resource management for cloud functions with memory tracing, profiling and autotuning

Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base

A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.Comment: Status: Accepted at Empirical Software Engineerin

Performanz Engineering von Serverless Anwendungen und Plattformen

Serverless computing is an emerging cloud computing paradigm that offers a highlevel application programming model with utilization-based billing. It enables the deployment of cloud applications without managing the underlying resources or worrying about other operational aspects. Function-as-a-Service (FaaS) platforms implement serverless computing by allowing developers to execute code on-demand in response to events with continuous scaling while having to pay only for the time used with sub-second metering. Cloud providers have further introduced many fully managed services for databases, messaging buses, and storage that also implement a serverless computing model. Applications composed of these fully managed services and FaaS functions are quickly gaining popularity in both industry and in academia. However, due to this rapid adoption, much information surrounding serverless computing is inconsistent and often outdated as the serverless paradigm evolves. This makes the performance engineering of serverless applications and platforms challenging, as there are many open questions, such as: What types of applications is serverless computing well suited for, and what are its limitations? How should serverless applications be designed, configured, and implemented? Which design decisions impact the performance properties of serverless platforms and how can they be optimized? These and many other open questions can be traced back to an inconsistent understanding of serverless applications and platforms, which could present a major roadblock in the adoption of serverless computing. In this thesis, we address the lack of performance knowledge surrounding serverless applications and platforms from multiple angles: we conduct empirical studies to further the understanding of serverless applications and platforms, we introduce automated optimization methods that simplify the operation of serverless applications, and we enable the analysis of design tradeoffs of serverless platforms by extending white-box performance modeling.Serverless Computing ist ein neues Cloud-Computing-Paradigma, das ein High-Level-Anwendungsprogrammiermodell mit nutzungsbasierter Abrechnung bietet. Es ermöglicht die Bereitstellung von Cloud-Anwendungen, ohne dass die zugrunde liegenden Ressourcen verwaltet werden müssen oder man sich um andere betriebliche Aspekte kümmern muss. FaaS-Plattformen implementieren Serverless Computing, indem sie Entwicklern die Möglichkeit geben, Code nach Bedarf als Reaktion auf Ereignisse mit kontinuierlicher Skalierung auszuführen, während sie nur für die genutzte Zeit mit sekundengenauer Abrechnung zahlen müssen. Cloud-Anbieter haben darüber hinaus viele vollständig verwaltete Dienste für Datenbanken, Messaging-Busse und Orchestrierung eingeführt, die ebenfalls ein Serverless Computing-Modell implementieren. Anwendungen, die aus diesen vollständig verwalteten Diensten und FaaS-Funktionen bestehen, werden sowohl in der Industrie als auch in der Wissenschaft immer beliebter. Aufgrund dieser schnellen Verbreitung sind jedoch viele Informationen zum Serverless Computing inkonsistent und oft veraltet, da sich das Serverless Paradigma weiterentwickelt. Dies macht das Performanz-Engineering von Serverless Anwendungen und Plattformen zu einer Herausforderung, da es viele offene Fragen gibt, wie zum Beispiel: Für welche Arten von Anwendungen ist Serverless Computing gut geeignet und wo liegen seine Grenzen? Wie sollten Serverless Anwendungen konzipiert, konfiguriert und implementiert werden? Welche Designentscheidungen wirken sich auf die Performanzeigenschaften von Serverless Plattformen aus und wie können sie optimiert werden? Diese und viele andere offene Fragen lassen sich auf ein uneinheitliches Verständnis von Serverless Anwendungen und Plattformen zurückführen, was ein großes Hindernis für die Einführung von Serverless Computing darstellen könnte. In dieser Arbeit adressieren wir den Mangel an Performanzwissen zu Serverless Anwendungen und Plattformen aus mehreren Blickwinkeln: Wir führen empirische Studien durch, um das Verständnis von Serverless Anwendungen und Plattformen zu fördern, wir stellen automatisierte Optimierungsmethoden vor, die das benötigte Wissen für den Betrieb von Serverless Anwendungen reduzieren, und wir erweitern die White-Box-Performanzmodellierungerung für die Analyse von Designkompromissen von Serverless Plattformen