research-article

On the effects of seeding strategies: a case for search-based multi-objective service composition

Authors:

Xin YaoAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 1419 - 1426

https://doi.org/10.1145/3205455.3205513

Published: 02 July 2018 Publication History

Abstract

Service composition aims to search a composition plan of candidate services that produces the optimal results with respect to multiple and possibly conflicting Quality-of-Service (QoS) attributes, e.g., latency, throughput and cost. This leads to a multi-objective optimization problem for which evolutionary algorithm is a promising solution. In this paper, we investigate different ways of injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Specifically, we propose four alternative seeding strategies to strengthen the quality of the initial population for the MOEA to start working with. By using the real-world WS-DREAM dataset, we conduced experimental evaluations based on 9 different workflows of service composition problems and several metrics. The results confirm the effectiveness and efficiency of those seeding strategies. We also observed that, unlike the discoveries for other problem domains, the implication of the number of seeds on the service composition problems is minimal, for which we investigated and discussed the possible reasons.

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Cited By

Gong JChen T(2024)Deep Configuration Performance Learning: A Systematic Survey and TaxonomyACM Transactions on Software Engineering and Methodology10.1145/370298634:1(1-62)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702986
Chen PChen TLi M(2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3388910
Da Silva AKnissel TWeiss G(2024)Self-Adaptive Optimization Techniques for Matrix Production Systems2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)10.1109/CASE59546.2024.10711799(1163-1168)Online publication date: 28-Aug-2024
https://doi.org/10.1109/CASE59546.2024.10711799
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Index Terms

On the effects of seeding strategies: a case for search-based multi-objective service composition
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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cover image ACM Conferences

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

July 2018

1578 pages

ISBN:9781450356183

DOI:10.1145/3205455

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Copyright © 2018 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 02 July 2018

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GECCO '18

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Gong JChen T(2024)Deep Configuration Performance Learning: A Systematic Survey and TaxonomyACM Transactions on Software Engineering and Methodology10.1145/370298634:1(1-62)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702986
Chen PChen TLi M(2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3388910
Da Silva AKnissel TWeiss G(2024)Self-Adaptive Optimization Techniques for Matrix Production Systems2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)10.1109/CASE59546.2024.10711799(1163-1168)Online publication date: 28-Aug-2024
https://doi.org/10.1109/CASE59546.2024.10711799
Arrieta AValle PAgirre JSagardui G(2023)Some Seeds Are Strong: Seeding Strategies for Search-based Test Case SelectionACM Transactions on Software Engineering and Methodology10.1145/353218232:1(1-47)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3532182
Chen TLi M(2023)The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software EngineeringACM Transactions on Software Engineering and Methodology10.1145/351423332:1(1-40)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3514233
Kumar SChen TBahsoon RBuyya R(2023)DebtCom: Technical Debt-Aware Service Recomposition in SaaS CloudIEEE Transactions on Services Computing10.1109/TSC.2023.323704316:4(2545-2558)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3237043
Li MChen TGrundy J(2023)Methodology and Guidelines for Evaluating Multi-Objective Search-Based Software EngineeringProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00096(338-339)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE-Companion58688.2023.00096
Chen TSchmerl BMaggio MCámara J(2022)Planning landscape analysis for self-adaptive systemsProceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3524844.3528060(84-90)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.1145/3524844.3528060
Li MChen TYao X(2022)How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological GuidanceIEEE Transactions on Software Engineering10.1109/TSE.2020.303610848:5(1771-1799)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1109/TSE.2020.3036108
Chen T(2022)Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00022(78-89)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00022
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