Cloud service reliability modelling and optimal task scheduling. Issue 2 (1st January 2017)

• Record Type: Journal Article
• Authors: Cui, Hongyan; Li, Yang; Liu, Xiaofei; Ansari, Nirwan; Liu, Yunjie
• Is Part Of: IET communications. Volume 11:Issue 2(2017)
• Page Start: 161
• Page End: 167
• Publication Date: 2017-01-01
• Subjects: cloud computing -- software reliability -- scheduling -- task analysis -- resource allocation -- computational complexity -- genetic algorithms -- Markov processes -- optimisation; cloud service reliability modelling -- optimal task scheduling -- cloud computing -- service sharing -- network access -- configurable computing resources -- heterogeneous environment -- resource allocation -- NP‐hard problem -- reliability analysis -- Markov‐based method -- cloud scheduling problem -- multiobjective optimisation problem -- genetic algorithm‐based chaotic ant swarm algorithm -- GA‐CAS algorithm; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2016.0417
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP‐hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov‐based method. They formulate the cloud scheduling problem as a multi‐objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm‐based chaotic ant swarm (GA‐CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi‐objective optimisation problem. Simulation results have demonstrated that GA‐CAS generally speeds up convergence and outperforms other meta‐heuristic approaches.