Detecting Performance Antipatterns in Com-ponent Based Enterprise Systems.

Parsons, Trevor; Murphy, John

doi:10.5381/jot.2008.7.3.a1

Cited by 79 publications

(16 citation statements)

References 27 publications

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“…Based on [15], in another work Parsons et al [16] reconstruct design models as well as run-time paths from gathered monitoring data. Using pattern matching and data mining techniques these artifacts are analyzed to identify performance anti-patterns.…”

Section: Related Workmentioning

confidence: 99%

Automatic detection of performance anti-patterns in inter-component communications

Wert

Oehler²,

Heger

et al. 2014

Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures

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Performance problems such as high response times in software applications have a significant effect on the customer's satisfaction. In enterprise applications, performance problems are frequently manifested in inefficient or unnecessary communication patterns between software components originating from poor architectural design or implementation. Due to high manual effort, thorough performance analysis is often neglected, in practice. In order to overcome this problem, automated engineering approaches are required for the detection of performance problems. In this paper, we introduce several heuristics for measurement-based detection of well-known performance anti-patterns in inter-component communications. The detection heuristics comprise load and instrumentation descriptions for performance tests as well as corresponding detection rules. We integrate these heuristics with Dynamic Spotter, a framework for automatic detection of performance problems. We evaluate our heuristics on four evaluation scenarios based on an e-commerce benchmark (TPC-W) where the heuristics detect the expected communication performance anti-patterns and pinpoint their root causes.

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Section: Related Workmentioning

confidence: 99%

Automatic detection of performance anti-patterns in inter-component communications

Wert

Oehler²,

Heger

et al. 2014

Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures

View full text Add to dashboard Cite

show abstract

“…It is based on monitoring data from running systems, it extracts the runtime system design and detects EJB antipatterns by applying rules to it. However, the scope of [26] is restricted to such domain (i.e. EJB running applications), and the detection of the performance problems is not allowed earlier in the development process.…”

Section: Related Workmentioning

confidence: 99%

“…Parsons et al [26] present a framework for detecting performance antipatterns, where a rule-based performance diagnosis tool, named Performance Antipattern Detection (PAD), is presented. The approach deals with Component-Based Enterprise Systems, targeting Enterprise Java Bean (EJB) applications only.…”

Section: Related Workmentioning

confidence: 99%

“…Software performance antipatterns [28] have been used to automate the interpretation of performance analysis results and the generation of architectural feedback [26,31]. The rationale of using the performance antipatterns knowledge is two-fold: on one hand, an antipattern identifies a bad practice in the software architectural model that negatively affects the performance indices, and on the other hand, its definition also includes a solution description that lets the software architect devise refactoring actions.…”

Section: Introductionmentioning

confidence: 99%

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A model-driven approach to broaden the detection of software performance antipatterns at runtime

Marco

Trubiani

2014

Electron. Proc. Theor. Comput. Sci.

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Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that build on four different views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software elements to provide system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of software resources onto hardware platforms; (iv) the performance view that collects a set of specific performance indices. In this paper we present a lightweight infrastructure that enables the detection of software performance antipatterns at runtime through the monitoring of specific performance indices. The proposed approach precalculates the logical predicates of antipatterns and identifies the ones whose static, dynamic and deployment sub-predicates occur in the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates thus to support the detection of antipatterns at runtime.

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“…For performance prediction of the modeled scenarios, the approach suggests using existing simulation techniques, which are not part of the approach. Based on the COM-PAS framework, two further approaches have been proposed: AQUA, by Diaconescu and Murphy (2005), and PAD, by Parsons and Murphy (2008).…”

Section: Related Workmentioning

confidence: 99%

Architecture optimization: speed or accuracy? both!

et al. 2016

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Embedded systems are becoming more and more complex, thus demanding innovative means to tame their challenging development. Among others, early architecture optimization represents a crucial activity in the development of embedded systems to maximise the usage of their limited resources and to respect their real-time requirements. Typically, architecture optimization seeks good architecture candidates based on model-based analysis. Leveraging abstractions and estimates, this analysis usually produces approximations useful for comparing architecture candidates. Nonetheless, approximations do not provide enough accuracy in estimating crucial extra-functional properties. In this article, we provide an architecture optimization framework that profits from both the speed of model-based predictions and the accuracy of execution-based measurements. Model-based optimization rapidly finds a good architecture candidate, which is refined through optimization based on monitored executions of automatically generated code. Moreover, the framework enables the developer to leverage her optimization experience. More specifically, the developer can use runtime monitoring of generated code execution to manually adjust task allocation at modeling level, and commit the changes without halting execution. In the article, our architecture optimization mechanism is first described from a general point of view and then exploited for optimizing the allocation of software tasks to the processing cores of a multicore embedded system; we target extra-functional properties that can be

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Detecting Performance Antipatterns in Com-ponent Based Enterprise Systems.

Cited by 79 publications

References 27 publications

Automatic detection of performance anti-patterns in inter-component communications

Automatic detection of performance anti-patterns in inter-component communications

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Architecture optimization: speed or accuracy? both!

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