Joint time delay and energy optimization with intelligent overclocking in edge computing  

作　　者：Kehao WANG Zhenhua XIONG Lin CHEN Pan ZHOU Hyundong SHIN 

机构地区：[1]School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China [2]Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China [3]School of Data and Computer Science,Sun Yat-Sen University,Guangzhou 510006,China [4]School of Cyber Science and Engineering,Huazhong University of Science and Technology,Wuhan 430000,China [5]Department of Electronic Engineering,Kyung Hee University,Yongin-si 17014,South Korea

出　　处：《Science China(Information Sciences)》2020年第4期150-165,共16页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61672395,61972448,61911540481);Fund of Hubei Key Laboratory of Inland Shipping Technology(Grant No.NHHY2019004);National Research Foundation of Korea(NRF)Grant Funded by the Korea Government(MSIT)(Grant No.2019K2A9A2A06024389)。

摘　　要：With the rapid growth of user equipment(UE),the amount of data transmitted over networks has become enormous,exerting immense pressure on backbone networks and central cloud infrastructures.Simultaneously,corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE.Mobile edge computing(MEC)has been proposed to support the offloading of UE tasks to edge clouds for execution.The implementation of MEC requires fast data transmission between UE and edge servers,and the emerging 5G network appears to render this technology possible.In this paper,considering a large number of UE,a fixed MEC server,and an advanced intelligent network,we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time.We jointly manage task offloading,server resource allocation,and overclocking to minimize the system-wide computation overhead and other risks.The proposed optimization problem is a mixedinteger nonlinear programming problem that is divided into three subproblems:offloading decision,resource allocation,and overclocking decision.We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem.Finally,simulation results show that the overclocked MEC server has lower system-wide computation overhead,faster task processing,and more offloaded UE as compared with the case without overclocking.With the rapid growth of user equipment(UE), the amount of data transmitted over networks has become enormous, exerting immense pressure on backbone networks and central cloud infrastructures.Simultaneously, corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE. Mobile edge computing(MEC) has been proposed to support the offloading of UE tasks to edge clouds for execution. The implementation of MEC requires fast data transmission between UE and edge servers, and the emerging 5G network appears to render this technology possible. In this paper,considering a large number of UE, a fixed MEC server, and an advanced intelligent network, we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time. We jointly manage task offloading, server resource allocation, and overclocking to minimize the system-wide computation overhead and other risks. The proposed optimization problem is a mixedinteger nonlinear programming problem that is divided into three subproblems: offloading decision, resource allocation, and overclocking decision. We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem. Finally, simulation results show that the overclocked MEC server has lower system-wide computation overhead, faster task processing, and more offloaded UE as compared with the case without overclocking.

关 键 词：OVERCLOCKING INTELLIGENT network mobile EDGE COMPUTING computation OFFLOADING resource allocation 

分 类 号：TN929.5[电子电信—通信与信息系统] O221.2[电子电信—信息与通信工程]