Symbolic performance adaptation

Incerto, Emilio; Tribastone, Mirco; Trubiani, Catia

doi:10.1145/2897053.2897060

Cited by 12 publications

(13 citation statements)

References 35 publications

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“…Only 11 of the primary studies (13%) made the evaluation results of their experiments publicly available (F17). Examples of studies that provide evaluation results are [30], [62], where results are made available with a replication package. Making experiment data public allows for verifying findings and experimental reuse, and lowers the barriers to meta-studies.…”

Section: F How Are Experiments Results Packaged?mentioning

confidence: 99%

“…The pattern for scalability applies a single method of a managing system on different variations of a managed system, and measures the scalability for time behavior of the managing system or functional appropriateness of both the managed and managing system (8 instances). For example, Incerto et al [30] evaluate the scalability of an SMT-backed planning approach in terms of computation time under increasing numbers of servers in the managed system. The pattern for time efficiency applies more than two methods of a managing system combined with constants, and measures the effect on the time behavior of both managing and managed system or just the managing system (4 instances).…”

Section: What Is the Experimental Design?mentioning

confidence: 99%

“…Twenty-one studies (38%) used a model to represent a managed system. For example, Incerto et al [30] represent a three-tier managed system as a Queuing Network to evaluate performance adaptation. A real implementation of a managed system was used in 21 studies (26%).…”

Section: How Are Experiments Operated?mentioning

confidence: 99%

“…Uncertainty in the system was used 35 times (27%). 14 For example, Incerto et al [30] address uncertainty of the system in terms of random faults of servers and network links. Only a few studies considered uncertainty of goals (20 studies, 15%) and humans (9 studies, 7%).…”

Section: How Are Experiments Operated?mentioning

confidence: 99%

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How do we Evaluate Self-adaptive Software Systems? [replication package]

Gerostathopoulos,

Vogel,

Weyns

et al. 2021

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With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge for the community. However, so far there is no clear overview of how evaluations are performed in self-adaptive systems. To address this gap, we conduct a mapping study. The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems-the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications. To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.

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Section: F How Are Experiments Results Packaged?mentioning

confidence: 99%

Section: What Is the Experimental Design?mentioning

confidence: 99%

Section: How Are Experiments Operated?mentioning

confidence: 99%

Section: How Are Experiments Operated?mentioning

confidence: 99%

See 2 more Smart Citations

How do we Evaluate Self-adaptive Software Systems? [replication package]

Gerostathopoulos,

Vogel,

Weyns

et al. 2021

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“…Kowal et al [42] leverage the commonalities across variants of software systems to analyse uncertainty in terms of both parametric changes (which affect the values of performance annotations) and structural changes (which may affect the topology of performance models). Incerto et al [43] express uncertainty as symbols in the specification of queuing network performance models and derive actual values of symbols by means of satisfiability modulo theory. Perez-Palacin et al [44] proposed a methodology to guide software engineers in the process of recognizing and managing the existence of uncertainty.…”

Section: Performance Uncertainty Analysismentioning

confidence: 99%

An efficient method for uncertainty propagation in robust software performance estimation

Aleti

Trubiani

Hoorn

et al. 2018

Journal of Systems and Software

Self Cite

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Software engineers often have to estimate the performance of a software system before having full knowledge of the system parameters, such as workload and operational profile. These uncertain parameters inevitably affect the accuracy of quality evaluations, and the ability to judge if the system can continue to fulfil performance requirements if parameter results are different from expected. Previous work has addressed this problem by modelling the potential values of uncertain parameters as probability distribution functions, and estimating the robustness of the system using Monte Carlo-based methods. These approaches require a large number of samples, which results in high computational cost and long waiting times.To address the computational inefficiency of existing approaches, we employ Polynomial Chaos Expansion (PCE) as a rigorous method for uncertainty propagation and further extend its use to robust performance estimation. The aim is to assess if the software system is robust, i.e., it can withstand possible changes in parameter values, and continue to meet performance requirements. PCE is a very efficient technique, and requires significantly less computations to accurately estimate the distribution of performance indices. Through three very different case studies from different phases of software development and heterogeneous application domains, we show that PCE can accurately (>97%) estimate the robustness of various performance indices, and saves up to 225 hours of performance evaluation time when compared to Monte Carlo Simulation.

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Towards Software Performance by Construction

Tribastone

2018

Leveraging Applications of Formal Methods, Verification and Validation. Modeling

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Symbolic performance adaptation

Cited by 12 publications

References 35 publications

How do we Evaluate Self-adaptive Software Systems? [replication package]

How do we Evaluate Self-adaptive Software Systems? [replication package]

An efficient method for uncertainty propagation in robust software performance estimation

Towards Software Performance by Construction

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