Paper Info
Title
Self-adaptation and distributed knowledge-based service ecosystem evolution
Abstract
Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self-adaptation technologies for the service-based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self-adaptation and distributed knowledge-based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self-adaptation mechanism. We adopt the "survival of the fittest" principle for crucial elements in the ecosystem during evolution so that ultimately, service-based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service-based process are automatically repaired.
Year
DOI
Venue
2021
10.1002/cpe.6469
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
distributed knowledge, runtime self-adaptation, service ecosystem, service evolution, stability evaluation
Journal
33
Issue
ISSN
Citations 
24
1532-0626
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xianghui Wang133.11
Zhiyong Feng200.34
Keman Huang300.34
Shizhan Chen401.35