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AMTS:Adaptive multi-objective task scheduling Strategy in Cloud Computing

China Communications 2016年第4期13卷 162-171页

作者： HE Hua XU Guangquan PANG Shanchen ZHAO Zenghua School of Computer Science and Technology Tianjin University College of Computer and Communication Engineering China University of Petroleum

task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive multi-objective task scheduling(AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.

关键词： quality of service cloud computing multi-objective task scheduling particle swarm optimization(PSO) small position value(SPV)

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multi-objective Optimization for Cloud task scheduling Based on the ANP Model

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Chinese Journal of Electronics 2017年第5期26卷 889-898页

作者： LI Kunlun WANG Jun Electronic Information Engeering College Hebei University

We propose a multi-objective optimization algorithm for cloud task scheduling based on the Analytic network process(ANP) model to solve the problems in cloud task scheduling,such as the deficiencies of mathematical description,limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal ***,we present the mathematical description of cloud task scheduling using matrix ***,the improved Nondominated sorting genetic algorithm II(NSGA-II) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming(GEP)algorithm has been introduced into the cloud task scheduling field to search the Pareto set among ***,ANP model has been combined with the improved NSGA-II to solve the selection problems of Pareto *** with the multi-objective optimization algorithm based on the weighted polynomial,the proposed algorithm can optimize multiple goals at the same time,and can avoid the additional iterations due to the change of users preferences *** simulation results indicate that the proposed algorithm is effective.

关键词： Cloud computing multi-objective task scheduling NSGA-Ⅱ algorithm ANP model GEP algorithm

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