Open AccessBook
Model-Based Software Performance Analysis
TLDR
This book provides the cross-knowledge that allows developers to tackle software performance issues from the very early phases of software development and explains the basic concepts of performance analysis and describes the most representative methodologies to annotate and transform software models into performance models.Abstract:
Poor performance is one of the main quality-related shortcomings that cause software projects to fail. Thus, the need to address performance concerns early during the software development process is fully acknowledged, and there is a growing interest in the research and software industry communities towards techniques, methods and tools that permit to manage system performance concerns as an integral part of software engineering. Model-based software performance analysis introduces performance concerns in the scope of software modeling, thus allowing the developer to carry on performance analysis throughout the software lifecycle. With this book, Cortellessa, Di Marco and Inverardi provide the cross-knowledge that allows developers to tackle software performance issues from the very early phases of software development. They explain the basic concepts of performance analysis and describe the most representative methodologies used to annotate and transform software models into performance models. To this end, they go all the way from performance primers through software and performance modeling notations to the latest transformation-based methodologies. As a result, their book is a self-contained reference text on software performance engineering, from which different target groups will benefit: professional software engineers and graduate students in software engineering will learn both basic concepts of performance modeling and new methodologies; while performance specialists will find out how to investigate software performance model building.read more
Citations
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Proceedings ArticleDOI
Software performance self-adaptation through efficient model predictive control
TL;DR: This paper presents an approach that allows performance self-adaptation using a system model based on queuing networks (QNs), a well-assessed formalism for software performance engineering that is efficient since it is based on mixed integer programming, which uses a compact representation of a QN with ordinary differential equations.
Proceedings ArticleDOI
Stratus ML: A Layered Cloud Modeling Framework
TL;DR: Stratus ML provides an intuitive user interface that allows the cloud stakeholders to define their application services, configure them, specify the applications' behaviour at runtime through a set of adaptation rules, and estimate cost under diverse cloud platforms and configurations.
Journal ArticleDOI
Software model refactoring based on performance analysis: better working on software or performance side?
TL;DR: This paper compares two approaches that have recently been introduced for interpreting model-based performance analysis results and translating them into architectural feedback: one based on the detection and solution of performance antipatterns, and another based on bidirectional model transformations between software and performance models.
Book ChapterDOI
Software performance modeling
TL;DR: A model transformation chain named PUMA (Performance by Unified Model Analysis) is described that enables the integration of performance analysis in a UML-based software development process, by automating the derivation of performance models from UML+MARTE software models, and by facilitating the interoperability of UML tools and performance tools.
References
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Journal ArticleDOI
A performance model interchange format
TL;DR: The definition of a PMIF is presented by describing a meta-model of the information requirements and the transfer format derived from it, which describes how tool developers can implement the PMIF, how the model interchange via export and import works in practice, and how thePMIF can be extended.