Cloud service reliability modelling and optimal task scheduling

• Published in: IET communications Vol. 11; no. 2; pp. 161 - 167
• Main Authors: Cui, Hongyan, Li, Yang, Liu, Xiaofei, Ansari, Nirwan, Liu, Yunjie
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 26.01.2017
• Subjects: Algorithms; Cloud computing; cloud scheduling problem; cloud service reliability modelling; Clouds; computational complexity; configurable computing resources; GA‐CAS algorithm; genetic algorithms; genetic algorithm‐based chaotic ant swarm algorithm; heterogeneous environment; Markov processes; Markov‐based method; multiobjective optimisation problem; Multiple objective analysis; network access; Networks; NP‐hard problem; optimal task scheduling; optimisation; Optimization; Reliability; reliability analysis; resource allocation; scheduling; service sharing; software reliability; task analysis; Task scheduling; GA-CAS algorithm; multiobjective optimisation problem; software reliability; service sharing; genetic algorithms; genetic algorithm-based chaotic ant swarm algorithm; task analysis; cloud service reliability modelling; optimal task scheduling; optimisation; resource allocation; configurable computing resources; NP-hard problem; reliability analysis; Markov-based method; cloud scheduling problem; scheduling; Markov processes; heterogeneous environment; cloud computing; network access; computational complexity
• ISSN: 1751-8628; 1751-8636
• DOI: 10.1049/iet-com.2016.0417

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.