Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm

Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-...

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Published in:Journal of experimental & theoretical artificial intelligence Vol. 33; no. 2; pp. 179 - 202
Main Author: Hosseini Shirvani, Mirsaeid
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 04.03.2021
Taylor & Francis Ltd
Subjects:
Algorithms
Business
Cloud computing
Combinatorial analysis
Composition
Cost function
Cybersecurity
Economies of scale
Genetic algorithms
multi-cloud market
Multi-objective optimisation
Multiple objective analysis
Particle swarm optimization
Simulated annealing
web service composition
Web services
ISSN:0952-813X, 1362-3079
Online Access:Get full text
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Summary:Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant features of a single cloud. One of the biggest challenges is to know which cloud is commensurate with user's business process with regards to security objectives. To this end, the new method is presented to quantify the amount of cloud security risk (CSR) in regards to user's business process. Therefore, in this paper, the web service composition problem is formulated to bi-objective optimisation problem with service cost and multi-cloud risk viewpoints in ever-increasing multi-cloud environment (MCE) in which each provider has its variable pricing policy and different security level. It is obviously an NP-Hard problem. To solve the combinatorial problem, we develop a bi-objective time-varying particle swarm optimisation (BOTV-PSO) algorithm. The parameters are tuned based on elapsed time so a good balance between exploration and exploitation is achieved. To illustrate the effectiveness of proposed algorithm, we defined several scenarios and compared the performance of proposed algorithm with multi-objective GA-based (MOGA) optimiser, a single objective genetic algorithm (SOGA) that only optimises cost function and neglects CSR, and multi-objective simulated annealing algorithm (MOSA). The experimental results showed the superiority of proposed BOTV-PSO against other approaches in terms of convergence, diversity, fitness, performance, and even scalability.
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ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2020.1725652