Xu, Xiaolong, Cao, Lingling, Wang, Xinheng ORCID: https://orcid.org/0000-0001-8771-8901, Xu, Xiaolong, Cao, Lingling and Wang, Xinheng (2016) Resource pre-allocation algorithms for low-energy task scheduling of cloud computing. Journal of Systems Engineering and Electronics, 27 (2). pp. 457-469. ISSN 1004-4132

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Abstract

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the "shut down the redundant, turn on the demanded" strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted: a cubic exponential smoothing algorithm based on the conservative control (CESCC) strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching (RA-PM) are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing (RA-ISA) is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Item Type:	Article
Identifier:	10.1109/JSEE.2016.00047
Keywords:	simulated annealing, green cloud computing, power consumption, prediction, resource allocation, probabilistic matching, simulated annealing, green cloud computing, power consumption, prediction, resource allocation, probabilistic matching
Subjects:	Computing > Systems > Computer networking
Depositing User:	Henry Wang
Date Deposited:	01 Feb 2018 11:40
Last Modified:	06 Feb 2024 15:55
URI:	https://repository.uwl.ac.uk/id/eprint/4363

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