Multi-Granularity Decomposition based Task Scheduling for Migration Cost Minimization

Ziliang Wang, Tingting Zhang, Y. Li, Sheng Wang, F. Zhou, Lei Feng, Wenjing Li
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Abstract

With the development of mobile communication, network technology, and the continuous emergence of intelligent network applications, users' demand for network computing power has increased explosively, which promoted the formation of a multi-level computing power system composed of the end devices, mobile network edge cloud, and center clouds. The terminal and edge computing power resources are limited. The cloud computing power is rich, but the delay is high, so the computing power at all levels needs effective cooperation to meet the quality of service requirements of various ubiquitous computing services. In this trend, cloud computing and edge computing begin to evolve into networked collaborative computing. In this paper, a task scheduling heuristic algorithm based on task cost minimization is proposed for network computing services with a large amount of communication and computation and high delay cost. This method divides the computing tasks of network applications into multiple granularities and schedules the divided sub-tasks, which can improve the utilization of the distributed computing resources and enhance the collaborative scheduling capability of computing and network resources.
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基于多粒度分解的迁移成本最小化任务调度
随着移动通信、网络技术的发展,以及智能网络应用的不断涌现,用户对网络计算能力的需求呈爆炸式增长,推动了由终端设备、移动网络边缘云和中心云组成的多层次计算能力体系的形成。终端和边缘计算能力资源有限。云计算能力丰富,但时延高,需要各级计算能力的有效协同,才能满足各种普适计算服务的服务质量要求。在这种趋势下,云计算和边缘计算开始向网络化协同计算发展。针对通信量大、计算量大、时延代价高的网络计算业务,提出了一种基于任务代价最小化的任务调度启发式算法。该方法将网络应用的计算任务划分为多个粒度,并对划分的子任务进行调度,提高了分布式计算资源的利用率,增强了计算资源和网络资源的协同调度能力。
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