Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud

作     者：Huang Bonan Xia Weiwei Zhang Yueyue Zhang Jing Zou Qian Yan Feng Shen Lianfeng 黄博南;夏玮玮;章跃跃;张静;邹倩;燕锋;沈连丰

作者机构：National Mobile Communications Research LaboratorySoutheast UniversityNanjing 210096China 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2018年第34卷第4期

页      面：430-438页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：The National Natural Science Foundation of China(No.61741102,61471164,61601122) the Fundamental Research Funds for the Central Universities(No.SJLX_160040) 

主　　题：ad-hoc mobile cloud task assignment algorithm directed acyclic graph particle swarm optimization simulated annealing 

摘      要：In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all ***-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.