Open AccessProbabilistic analysis of QoS‐aware service composition with explicit environment models
Author(s) -
Zhou Yu,
Han Tingting,
Chen Taolue,
Zhou Shiqi
Publication year - 2020
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2019.0152
Subject(s) - computer science , probabilistic logic , quality of service , web service , service (business) , markov decision process , set (abstract data type) , parametric statistics , distributed computing , matching (statistics) , markov process , artificial intelligence , world wide web , computer network , programming language , statistics , economy , mathematics , economics
In service composition, quality‐of‐service (QoS) represents a crucial indicator for the policy adoption. Existing composition strategies rarely address the influence of the environment, which may influence QoS and thus lead to sub‐optimal composition policies in a dynamic environment. In this study, a model‐based service composition approach is proposed. Given the user request, it is possible to first find a set of matching abstract web services (AWSs), and then pull relevant concrete web services (CWSs) based on the AWSs. The set of CWSs can be modelled as a Markov decision process (MDP). In addition, the authors model the environment as a fully probabilistic system, capturing changes of environment probabilistically. The environment model can be further composed of the MDP from the service models, obtaining a monolithic MDP. They demonstrate how the probabilistic verification techniques can be used to find the optimal service selection strategy against their QoS and the environment change. A distinguishing feature of their approach is that the QoS, as well as the dynamic of environment change, is made parametric so that the formal analysis is adaptive to the environment which is of paramount importance for autonomous and self‐adaptive systems. Examples and experiments confirm the feasibility of their approach.