Title: Novel algorithms and equivalence optimisation for resource allocation in cloud computing

Authors: Weiwei Lin; Chaoyue Zhu; Jin Li; Bo Liu; Huiqiong Lian

Addresses: School of Computer Engineering and Science, South China University of Technology, Panyu, Guangzhou 510006, China ' School of Computer Engineering and Science, South China University of Technology, Panyu, Guangzhou 510006, China ' School of Computer Science, Guangzhou University, Panyu, Guangzhou 510006, China ' School of Computer, South China Normal University, Guangzhou 510631, China ' School of Computer Engineering and Science, South China University of Technology, Panyu, Guangzhou 510006, China

Abstract: In this paper, we model the optimisation of the resource allocation in cloud computing as a constraint satisfaction problem considering three types of resources (CPU, RAM and bandwidth) and design a Choco-Based algorithm (CB) for VM resource allocation in virtualised cloud data centres. We also propose an Improved First-Fit Decreasing Algorithm (IFFD) and an Improved Best-Fit Decreasing Algorithm (IBFD) and conduct performance evaluation experiments using Choco. The experimental results show that CB has better results, whereas its solution time is longer than IFFD and IBFD in resource allocation. Moreover, to reduce the complexity of solving the problem of CSP-based resource allocation, we propose an equivalence optimisation which can greatly reduce the search space for resource allocation by making tree pruning with resource equivalence. Then, a resource allocation algorithm based on Equivalent Optimisation (EO) is designed. Experimental results also show that compared with CB, EO greatly reduces the time of allocating resource of cloud computing.

Keywords: cloud computing; resource allocation; Choco; equivalence optimisation; constraint satisfaction; CPU; RAM; bandwidth; cloud data centres; tree pruning.

DOI: 10.1504/IJWGS.2015.068899

International Journal of Web and Grid Services, 2015 Vol.11 No.2, pp.193 - 210

Received: 11 Jun 2014
Accepted: 13 Jul 2014
Published online: 19 Apr 2015 *

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