Connected Vehicles Computation Task Offloading Based on Opportunism in Cooperative Edge Computing  

作　　者：Duan Xue Yan Guo Ning Li Xiaoxiang Song 

机构地区：[1]College of Communications Engineering,Army Engineering University of PLA,Nanjing,210007,China [2]College of Computer Science,Liupanshui Normal University,Liupanshui,553000,China

出　　处：《Computers, Materials & Continua》2023年第4期609-631,共23页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China (61871400);Natural Science Foundation of Jiangsu Province (BK20211227);Scientific Research Project of Liupanshui Normal University (LPSSYYBZK202207).

摘　　要：The traditional multi-access edge computing (MEC) capacity isoverwhelmed by the increasing demand for vehicles, leading to acute degradationin task offloading performance. There is a tremendous number ofresource-rich and idle mobile connected vehicles (CVs) in the traffic network,and vehicles are created as opportunistic ad-hoc edge clouds to alleviatethe resource limitation of MEC by providing opportunistic computing services.On this basis, a novel scalable system framework is proposed in thispaper for computation task offloading in opportunistic CV-assisted MEC.In this framework, opportunistic ad-hoc edge cloud and fixed edge cloudcooperate to form a novel hybrid cloud. Meanwhile, offloading decision andresource allocation of the user CVs must be ascertained. Furthermore, thejoint offloading decision and resource allocation problem is described asa Mixed Integer Nonlinear Programming (MINLP) problem, which optimizesthe task response latency of user CVs under various constraints. Theoriginal problem is decomposed into two subproblems. First, the Lagrangedual method is used to acquire the best resource allocation with the fixedoffloading decision. Then, the satisfaction-driven method based on trial anderror (TE) learning is adopted to optimize the offloading decision. Finally, acomprehensive series of experiments are conducted to demonstrate that oursuggested scheme is more effective than other comparison schemes.

关 键 词：Multi-access edge computing opportunistic ad-hoc edge cloud offloading decision resource allocation TE learning 

分 类 号：TN929.5[电子电信—通信与信息系统]