Title: Hybridisation of oppositional centre-based genetic algorithms for resource allocation in cloud

Authors: K.M. Uma Maheswari; S. Govindarajan

Addresses: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India ' Department of EDP, Faculty of Medical and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

Abstract: Cloud computing is an attractive computing model since it allows for the provision of resources on-demand. In cloud computing, resource allocation is one of the challenging problems; because when the clients want to allocate the resource to particular task while attaining minimum cost. To overcome the problem, in this work we introduce a novel technique for resource allocation in cloud computing using oppositional centre-based genetic algorithm. For optimisation, we hybridise the centre-based genetic algorithm with opposition-based learning (OBL), where OBL is improving the performance of the centre-based genetic algorithm while optimising the bi-objective function. The main aim is to assign the corresponding resources to each subtask within the minimum cost. The generated solution is competent to the quality of service (QoS) and enhances IaaS suppliers' believability. For experimentation, we compare our proposed hybrid algorithm with GA and CGA algorithm.

Keywords: resource allocation; cloud computing; HCFA; scheduling; deadline; hybridisation; bi-objective.

DOI: 10.1504/IJNVO.2019.103420

International Journal of Networking and Virtual Organisations, 2019 Vol.21 No.3, pp.307 - 325

Received: 17 Mar 2017
Accepted: 29 Aug 2017
Published online: 06 Nov 2019 *

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