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Trustworthy stigmergic service composition and adaptation in decentralized environments

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posted on 2023-05-20, 17:24 authored by Moustafa, A, Zhang, M, Quan BaiQuan Bai
The widespread use of web services in forming complex online applications requires service composition to cope with highly dynamic and heterogeneous environments. Traditional centralized service composition techniques are not sufficient to address the needs of applications in decentralized environments. In this paper, a stigmergic-based approach is proposed to model the decentralized service interactions and handle service composition in highly dynamic open environments. In the proposed approach, web services and resources are modeled as multiple agents. Stigmergic-based self-organization mechanisms among agents are deployed to facilitate adapting service composition. In addition, to overcome the limitations of traditional QoS-based approaches, trust measurements are deployed as a criterion for service selection. To improve the performance of the proposed stigmergic-based approach under dynamic scale-free environments, we investigate the hybridization with local search operators to consolidate adaptation, and diversity schemes are introduced to facilitate continual service adaptation. Extensive experiments show the efficiency of the proposed approach in dealing with incomplete information and dynamic factors in composing and adapting web services in open environments. The experiment results also show that the proposed approach achieves a better performance than other traditional approaches.

History

Publication title

IEEE Transactions on Services Computing

Volume

9

Pagination

317-329

ISSN

1939-1374

Department/School

School of Information and Communication Technology

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

United States

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Copyright 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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