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A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud

Published: 01 May 2013 Publication History

Abstract

Cloud computing, with its promise of (almost) unlimited computation, storage, and bandwidth, is increasingly becoming the infrastructure of choice for many organizations. As cloud offerings mature, service-based applications need to dynamically recompose themselves to self-adapt to changing QoS requirements. In this paper, we present a decentralized mechanism for such self-adaptation, using market-based heuristics. We use a continuous double-auction to allow applications to decide which services to choose, among the many on offer. We view an application as a multi-agent system and the cloud as a marketplace where many such applications self-adapt. We show through a simulation study that our mechanism is effective for the individual application as well as from the collective perspective of all applications adapting at the same time.

Cited By

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  • (2022)Systematic Scalability Modeling of QoS-aware Dynamic Service CompositionACM Transactions on Autonomous and Adaptive Systems10.1145/352916216:3-4(1-39)Online publication date: 2-Nov-2022
  • (2020)Dynamic Security Exchange Scheduling Model for Business Workflow Based on Queuing Theory in Cloud ComputingSecurity and Communication Networks10.1155/2020/88866402020Online publication date: 1-Jan-2020
  • (2020)Overwhelming Uncertainty in Self-adaptation: An Empirical Study on PLA and CobRAProceedings of the 12th Asia-Pacific Symposium on Internetware10.1145/3457913.3457943(250-259)Online publication date: 1-Nov-2020
  • Show More Cited By
  1. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud

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    Information & Contributors

    Information

    Published In

    cover image IEEE Transactions on Software Engineering
    IEEE Transactions on Software Engineering  Volume 39, Issue 5
    May 2013
    154 pages

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    IEEE Press

    Publication History

    Published: 01 May 2013

    Author Tags

    1. Adaptation models
    2. Cloud computing
    3. Measurement
    4. Pricing
    5. Quality of service
    6. Reliability
    7. Resource management
    8. Self-adaptation
    9. market based
    10. multi-agent systems

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    Cited By

    View all
    • (2022)Systematic Scalability Modeling of QoS-aware Dynamic Service CompositionACM Transactions on Autonomous and Adaptive Systems10.1145/352916216:3-4(1-39)Online publication date: 2-Nov-2022
    • (2020)Dynamic Security Exchange Scheduling Model for Business Workflow Based on Queuing Theory in Cloud ComputingSecurity and Communication Networks10.1155/2020/88866402020Online publication date: 1-Jan-2020
    • (2020)Overwhelming Uncertainty in Self-adaptation: An Empirical Study on PLA and CobRAProceedings of the 12th Asia-Pacific Symposium on Internetware10.1145/3457913.3457943(250-259)Online publication date: 1-Nov-2020
    • (2019)Replaceability and negotiation in a cloud service ecosystemJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-019-0137-88:1(1-14)Online publication date: 1-Dec-2019
    • (2019)Service selection mechanisms in the Internet of Things (IoT): a systematic and comprehensive studyCluster Computing10.1007/s10586-019-02984-423:2(1163-1183)Online publication date: 19-Sep-2019
    • (2018)SSMProceedings of the 2018 2nd International Conference on Cloud and Big Data Computing10.1145/3264560.3264568(11-15)Online publication date: 3-Aug-2018
    • (2018)Formulating Criticality-Based Cost-Effective Fault Tolerance Strategies for Multi-Tenant Service-Based SystemsIEEE Transactions on Software Engineering10.1109/TSE.2017.268166744:3(291-307)Online publication date: 1-Mar-2018
    • (2018)Notes on ensembles of IoT, network functions and clouds for service-oriented computing and applicationsService Oriented Computing and Applications10.1007/s11761-018-0228-212:1(1-10)Online publication date: 1-Mar-2018
    • (2017)Self-evolvable knowledge-enhanced IoT data mobility for smart environmentProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109789(1-14)Online publication date: 17-Oct-2017
    • (2017)Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix FactorizationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.270079628:10(2911-2924)Online publication date: 1-Oct-2017
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