Geographic Clustering Based Mobile Edge Computing Resource Allocation Optimization Mechanism

Song Kang, Linna Ruan, Shaoyong Guo, Wencui Li, Xue-song Qiu
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引用次数: 7

Abstract

With the development of Internet of Things (IoT), a large number of terminals and devices are connected to the network. Mobile edge computing (MEC) is proposed to assist cloud computing, to relieve the pressure of network and satisfy the requirements of delay-sensitive applications. Considering reasonable allocation of computing resources is the most important aspect corresponding to delay, this paper designs geographic clustering and collaborative scheduling (GC-CS) mechanism. This mechanism can be divided into two parts, which are the decentralized deployment of MEC servers and the resource allocation optimization in MEC. For the first part, this paper designs the load balancing based geographic clustering (LBGC) algorithm which combines the idea of greedy algorithm to realize the initial allocation of computing resources. For the second part, delay minimization oriented collaborative scheduling (DMCS) algorithm is designed to decrease the response delay without increasing system overhead. Finally, the effectiveness of the mechanism is verified by simulation in the IoT scene.
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基于地理聚类的移动边缘计算资源分配优化机制
随着物联网(IoT)的发展,大量的终端和设备被连接到网络中。移动边缘计算(MEC)是为了辅助云计算,缓解网络压力,满足延迟敏感型应用的需求而提出的。考虑到计算资源的合理分配是与延迟相对应的最重要的方面,本文设计了地理集群和协同调度机制。该机制可分为MEC服务器的分散部署和MEC中的资源分配优化两部分。第一部分设计了基于负载均衡的地理聚类(LBGC)算法,该算法结合贪婪算法的思想,实现了计算资源的初始分配。第二部分设计了面向延迟最小化的协同调度算法,在不增加系统开销的前提下降低响应延迟。最后,通过物联网场景仿真验证了该机制的有效性。
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