Mobility Prediction Based Computation Offloading Handoff Strategy for Vehicular Edge Computing
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摘要: 车载云计算环境中的计算卸载存在回程网络延迟高、远程云端负载大等问题，车载边缘计算利用边缘服务器靠近车载终端，就近提供云计算服务的特点，在一定程度上解决了上述问题。但由于汽车运动造成的通信环境动态变化进而导致任务完成时间增加，为此该文提出一种基于移动路径可预测的计算卸载切换策略MPOHS，即在车辆移动路径可预测情况下，引入基于最小完成时间的计算切换策略，以降低车辆移动性对计算卸载的影响。实验结果表明，相对于现有研究，该文所提算法能够在减少平均任务完成时间的同时，减少切换次数和切换时间开销，有效降低汽车运动对计算卸载的影响。Abstract: In the vehicular cloud computing environments, computation offloading faces the problems such as high network delay and large load of the remote cloud. The vehicular edge computing takes advantage of the edge servers to be close to the vehicular terminals, and provides the cloud computing service to solve the problem mentioned above. However, due to the dynamic change of communication environment caused by vehicle movement, the task completion time will increase. For this reason, this paper proposes a Mobility Prediction-based computation Offloading Handoff Strategy (MPOHS), which tries to minimize the average completion time of offloaded tasks by migrating tasks among edge servers according to the prediction of vehicle movement. The experimental results show that, compared with the existing research, the proposed strategy can reduce the average task completion time, cut down the handoff times and handoff time overhead, and effectively reduce the impact of vehicle movement on the performance of computation offloading.
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表 1 仿真参数表
参数名 参数值 参数名 参数值 基站覆盖范围 100% V计算能力(MIPS) 82335×0.75 RSU半径(m) U[100 150] ES计算能力(MIPS) 82335×1.5 Cloud数目(个) 1 Cloud计算能力(MIPS) 82335×2 BS数目(个) 1 V2R单跳带宽(Mbps) 15 RSU数目(个) 64 V2B单跳带宽(Mbps) 2 ES数目(个) 16 E2E单跳带宽(Mbps) U[15 20] V数目(个) 10 V2V端到端延时(ms) U[5 15] 任务计算量(MI) U[7 9]×106 V2R端到端延时(ms) U[20 30] 任务数据量(M) U[30 50] V2B端到端延时(ms) U[450 550] 
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