标题：Cost-Efficient Dependent Task Offloading for Multiusers
作者：Fan, Yinuo; Zhai, Linbo; Wang, Hua
作者机构：[Fan, Yinuo; Zhai, Linbo] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China.; [Wang, Hua] Shandong Univ, Sch So 更多
通讯作者地址：[Zhai, LB]Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China.
关键词：Offoading; dependent tasks; mobile; cost
摘要：The extensive use of mobile intelligent devices, such as smart phones and tablets, induces new opportunity and challenge for computation offloading. Task offloading is an important issue in a system consisting of multiple types of devices, such as mobile intelligent devices, local edge hosts and a remote cloud server. In this paper, we study the offloading assignment of multiple applications, each one comprising several dependent tasks, in such a system. To evaluate the total cost in the offloading process, a new metric is introduced to take into account features of different devices. The remote server and local hosts are more concerned about their processors utilization, while mobile devices pay more attention to their energy. Therefore, this metric uses relative energy consumption to denote the cost of mobile devices, and evaluates the cost of the remote server and local hosts by the processor cycle number of task execution. We formulate the offloading problem to minimize the system cost of all applications within each application's completed time deadline. Since this problem is NP-hard, the heuristic algorithm is proposed to offload these dependent tasks. At first, our algorithm arranges all tasks from different applications in a priority queue considering both completed time deadline and task-dependency requirements. Then, based on the priority queue, all tasks are initially assigned to devices to protect mobile devices with low energy and make them survive in the assignment process as long as possible. At last, to obtain a better schedule realizing lower system cost, based on the relative remaining energy of mobile devices, we reassign tasks from high-cost devices to low-cost devices to minimize the system cost. Simulation results show that our proposed algorithm increases the successfully completed probability of whole applications and reduces the system cost effectively under time and energy constraints.