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A process partitioning technique for constructing decentralized web service compositions
Author(s) -
Xue Gang,
Liu Di,
Liu Junsong,
Yao Shaowen
Publication year - 2019
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2735
Subject(s) - computer science , distributed computing , process (computing) , service (business) , web service , component (thermodynamics) , ranking (information retrieval) , graph , service composition , transformation (genetics) , decentralised system , database , theoretical computer science , control (management) , artificial intelligence , operating system , world wide web , biochemistry , chemistry , physics , economy , gene , economics , thermodynamics
Summary Web service compositions have been widely applied in different applications. A service composition is usually implemented in either a centralized or decentralized manner. Compared with the centralized service composition, the decentralized composition has no central control component, and components interact with each other directly, thereby achieving better performance. Process partitioning is a technique to divide a process into multiple parts and has been shown that it can be successfully applied to decentralizing process‐driven service compositions. This paper proposes a new process partitioning technique for constructing decentralized service compositions. The proposed technique, which is based on typed digraphs and a graph transformation technique, is used for exploring available process partitioning solutions. For applications, this paper discusses the topology and interaction features about the partitioning solutions and summarizes a ranking method for them. Three experiments are conducted to evaluate the proposed methods in this paper. Experimental results show that the proposed methods can be applied in constructing decentralized service compositions effectively. In addition, the results also show that the decentralized compositions can have lower average response times and higher throughputs than the corresponding centralized compositions in the experiments.

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