Distributed Task Offloading and Workload Balancing in IoV

MEC is an emerging paradigm to offload computation from the cloud in vehicular networks, aiming at better supporting computation-intensive services with low-latency and real-time requirements. In this chapter, we investigate a new service scenario of task offloading and workload balancing in MEC-empowered vehicular networks, where the computational resources of MEC/cloud servers are cooperatively utilized. Then, we formulate a Distributed Task Offloading (DTO) problem by considering heterogeneous computation resources, high mobility of vehicles, and uneven distribution of workloads, targeting at optimizing task offloading among MEC/cloud servers and minimizing task completion time. We prove that the DTO is NP-hard. Further, we propose a multi-armed bandit learning algorithm called utility-table-based learning. For workload balancing among MEC servers, a utility table is established to determine the optimal solution, which is updated based on the feedback signal from the offloaded server. For optimal task offloading, a theoretical bound is derived to determine the ratio of workload assigned to the cloud. Lastly, we build the simulation model and conduct an extensive experiment, which demonstrates the superiority of the proposed algorithm.