Joint Service Caching and Computation Offloading to Maximize System Profits in Mobile Edge-Cloud Computing

Qingyang Fan, Junyu Lin, Guangsheng Feng, Zihan Gao, Huiqiang Wang, Yafei Li
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引用次数: 7

Abstract

Considering the advantages of mobile edge computing (MEC), such as low latency, high bandwidth, etc., more and more mobile services are cached to mobile edge servers. However, due to limited computing resources and storage capacity of mobile edge servers, it is hard to guarantee that all services are cached and all computation offloading requests are satisfied. In this paper, we jointly optimize service caching and computation offloading to maximize system profits in mobile edge-cloud computing (MECC). The problem is formalized as a nonconvex optimization problem with discrete variables. We propose a Dynamic Joint computation Offloading and Service Caching algorithm (DJOSC) to solve the problem. Specifically, a regularization technique and Lyapunov optimization theory are used to transform the problem into two subproblems, which are solved by convex optimization techniques. Numerical evaluations show that the maximum system profits can be achieved under different computing resources, storage capacities and bandwidth capacities.
移动边缘云计算中联合服务缓存和计算卸载最大化系统利润
考虑到移动边缘计算(MEC)的低延迟、高带宽等优势,越来越多的移动服务被缓存在移动边缘服务器上。然而,由于移动边缘服务器的计算资源和存储容量有限,很难保证所有的服务都被缓存,所有的计算卸载请求都得到满足。在移动边缘云计算(MECC)中,我们共同优化服务缓存和计算卸载,以实现系统利润最大化。将该问题形式化为具有离散变量的非凸优化问题。我们提出了一种动态联合计算卸载和服务缓存算法(DJOSC)来解决这个问题。具体而言,利用正则化技术和李雅普诺夫优化理论将该问题转化为两个子问题,并利用凸优化技术进行求解。数值计算结果表明,在不同的计算资源、存储容量和带宽容量条件下,系统收益最大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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