Online Task Offloading Decision Algorithm for High-speed Vehicles
When and where to offloading tasks are the main problems to be solved in the task offloading decision in vehicular edge computing.High speed driving of the vehicle causes frequent changes of offloading access devices,and the offloading communica-tion between the vehicle and the offloading access device may break at any time.This requires that the offloading decision should be made immediately once the vehicle obtains an offloading opportunity.The existing offloading decision research focuses on how to maximize the offloading gain,without fully considering the impact of the timeliness of offloading decision on offloading strate-gy.As a result,the proposed offloading decision methods have high time and space complexity,and cannot be used for online task offloading decisions of high-speed vehicles.In order to solve the above problems,this paper first comprehensively considers the in-fluence of offloading decision-making timeliness and offloading gain factors,establishes a task offloading decision model for high-speed vehicles,and transforms it into a variation of the secretary problem.Then,an online vehicle task offloading decision algo-rithm OODA based on weighted bipartite graph matching is proposed to assist the vehicle to make real-time task offloading deci-sions when passing through multiple heterogeneous edge servers sequentially,and maximize the overall offloading gain.Finally,theoretical analysis shows that the competitive ratio of OODA algorithm is analyzed theoretically.Extensive simulation results show that OODA is feasible and effective.
万方数据
维普
