Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud
Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud
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DOI：
10.1007/978-3-030-23502-4_4
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
H. Kanemitsu;M. Hanada;H. Nakazato
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文献类型：
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作者：
H. Kanemitsu;M. Hanada;H. Nakazato
边缘计算可以实现云和用户之间的数据局部化，并可以应用于任务分流，即将移动终端上的部分工作负载移动到边缘或云系统，在降低能耗的同时最大限度地减少响应时间。由于移动终端计算能力的提高，移动工作流作业得到了广泛的应用。因此，如何对移动工作流中的每个任务进行卸载或调度是当前具有挑战性的问题之一。提出了一种基于任务卸载的任务调度算法，称为基于优先级的连续任务选择卸载(PCTSO)，以最小化调度长度并降低移动客户端的能量消耗。PCTSO试图选择依赖任务，以便卸载多个任务，从而利用边缘云中的多个vCPU；通过这种方式，可以保持并行度。仿真实验结果表明，PCTSO算法在调度长度上优于其他算法，且满足能量约束。
Edge computing can realize a data locality among a cloud and users, and it can be applied to task offloading, i.e., a part of workload on a mobile terminal is moved to an edge or a cloud system to minimize the response time with reducing energy consumption. Mobile workflow jobs have been widely used due to advance of computational power on a mobile terminal. Thus, how to offload or schedule each task in a mobile workflow is one of the current challenging issues.In this paper, we propose a task scheduling algorithm with task offloading, called priority-based continuous task selection for offloading (PCTSO), to minimize the schedule length with energy consumption at a mobile client being reduced. PCTSO tries to select dependent tasks such that many tasks are offloaded so as to utilize many vCPUs in the edge cloud; in this manner, the degree of parallelism can be maintained. Experimental results of the simulation demonstration that PCTSO outperforms other algorithms in the schedule length and satisfies the energy constraint.