The Library
Robustness estimation and optimisation for semantic web service composition with stochastic service failures
Tools
Tools
Tools
Wang, Chen, Ma, Hui, Chen, Gang, Hartmann, Sven and Branke, Jürgen (2022) Robustness estimation and optimisation for semantic web service composition with stochastic service failures. IEEE Transactions on Emerging Topics in Computational Intelligence, 6 (1). pp. 77-92. doi:10.1109/TETCI.2020.3027870
|
PDF
WRAP-robustness-estimation-optimisation-semantic-web-service-composition-stochastic-service-failures-Branke-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1248Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TETCI.2020.3027870
Abstract
Service-oriented architecture (SOA) is a widely adopted software engineering paradigm that encourages modular and reusable applications. One popular application of SOA is web service composition, which aims to loosely couple web services to accommodate complex goals not achievable through any individual web service. Many approaches have been proposed to construct composite services with optimized Quality of Service (QoS), assuming that QoS of web services never changes. However, the constructed composite services may not perform well and may not be executable later due to its component services' failure. Therefore, it is important to build composite services that are robust to stochastic service failures. Two challenges of building robust composite services are to efficiently generate service composition with near-optimal quality in a large search space of available services and to accurately measure the robustness of composite services considering all possible failure scenarios. This article proposes a novel two-stage GA-based approach to robust web service composition with an adaptive evolutionary control and an efficient robustness measurement. This approach can generate robust composite service at the design phase, which can cope with stochastic service failures and maintain high quality at the time of execution. We have conducted experiments with benchmark datasets to evaluate the performance of our proposed approach. Our experiments show that our method can produce highly robust composite services, achieving outstanding performance consistently in the event of stochastic service failures, on service repositories with varying sizes.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School > Operations Management Faculty of Social Sciences > Warwick Business School |
||||||||
| Library of Congress Subject Headings (LCSH): | Service-oriented architecture (Computer science), Software engineering , Web services , Robust optimization , Combinatorial optimization , Genetic algorithms | ||||||||
| Journal or Publication Title: | IEEE Transactions on Emerging Topics in Computational Intelligence | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 2471-285X | ||||||||
| Official Date: | February 2022 | ||||||||
| Dates: |
|
||||||||
| Volume: | 6 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 77-92 | ||||||||
| DOI: | 10.1109/TETCI.2020.3027870 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 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. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
