AMTS: Adaptive Multi-Objective Task Scheduling Strategy in Cloud Computing

作者：He Hua; Xu Guangquan^*; Pang Shanchen; Zhao Zenghua

来源：China Communications, 2016, 13(4): 162-171.

DOI：10.1109/cc.2016.7464133

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

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.

• 出版日期2016-4
• 单位天津大学; 中国石油大学（北京）