Multi-objective task offloading strategy based on partial computing resource optimization in edge computing of Internet of Vehicles
Lijuan Li, yanqiang li, Yong Wang, Xinhao Lin, Zhibang Zhong · 2023
Mobile edge computing (MEC) is a promising paradigm for offloading compute-intensive services on vehicles to alleviate the problem of limited resources in the vehicles themselves. However, since the vehicle network involves multiple edge servers, MEC is facing the dilemma of how to fully utilize the edge resources to achieve the maximum benefit. In this paper, we aim to analyze MEC task offloading strategies from a multi-objective optimization perspective by considering independent partitionable computational tasks, and an MEC communication and computation offloading framework is constructed. A partial computational resource optimization (PCRO) algorithm is proposed, which jointly considers computational resource allocation and unit price adjustment to achieve minimum cost for vehicle users and maximum profit for edge servers. Extensive experimental results demonstrate the effectiveness of our proposed PCRO algorithm.