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Performance-based selection of software and hardware features under parameter uncertainty

Published: 27 June 2014 Publication History

Abstract

Configurable software systems allow stakeholders to derive variants by selecting software and/or hardware features. Performance analysis of feature-based systems has been of large interest in the last few years, however a major research challenge is still to conduct such analysis before achieving full knowledge of the system, namely under a certain degree of uncertainty. In this paper we present an approach to analyze the correlation between selection of features embedding uncertain parameters and system performance. In particular, we provide best and worst case performance bounds on the basis of selected features and, in cases of wide gaps among these bounds, we carry on a sensitivity analysis process aimed at taming the uncertainty of parameters. The application of our approach to a case study in the e-health domain demonstrates how to support stakeholders in the identification of system variants that meet performance requirements.

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    cover image ACM Conferences
    QoSA '14: Proceedings of the 10th international ACM Sigsoft conference on Quality of software architectures
    June 2014
    158 pages
    ISBN:9781450325769
    DOI:10.1145/2602576
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    Publication History

    Published: 27 June 2014

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    Author Tags

    1. feature selection
    2. performance analysis
    3. software architectures
    4. uncertainty

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    • CRAFTERS ARTEMIS Project

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    QoSA '14 Paper Acceptance Rate 15 of 47 submissions, 32%;
    Overall Acceptance Rate 46 of 131 submissions, 35%

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    • (2024)Taming uncertainty with MDE: an historical perspectiveSoftware and Systems Modeling10.1007/s10270-024-01227-4Online publication date: 28-Oct-2024
    • (2022)Feature subset selection for learning huge configuration spacesProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A10.1145/3546932.3546997(85-96)Online publication date: 12-Sep-2022
    • (2022)Quantitative Verification for Monitoring Event-Streaming SystemsIEEE Transactions on Software Engineering10.1109/TSE.2020.299603348:2(538-550)Online publication date: 1-Feb-2022
    • (2021)Living with Uncertainty in Model-Based DevelopmentComposing Model-Based Analysis Tools10.1007/978-3-030-81915-6_8(159-185)Online publication date: 18-Jul-2021
    • (2020)Towards Using Probabilistic Models to Design Software Systems with Inherent UncertaintySoftware Architecture10.1007/978-3-030-58923-3_6(89-97)Online publication date: 8-Sep-2020
    • (2019)PLUSProceedings of the 41st International Conference on Software Engineering: New Ideas and Emerging Results10.1109/ICSE-NIER.2019.00028(77-80)Online publication date: 27-May-2019
    • (2018)Finding the Most Influential Parameters of Coalitions in a PSO-CO AlgorithmInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_24(284-296)Online publication date: 18-May-2018
    • (2018)Analyzing the Influence of LLVM Code Optimization Passes on Software PerformanceInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_23(272-283)Online publication date: 18-May-2018
    • (2018)QoS-Based Elasticity for Service Chains in Distributed Edge Cloud EnvironmentsAutonomous Control for a Reliable Internet of Services10.1007/978-3-319-90415-3_8(182-211)Online publication date: 25-May-2018
    • (2017)Software performance self-adaptation through efficient model predictive controlProceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering10.5555/3155562.3155624(485-496)Online publication date: 30-Oct-2017
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