Optimizing Allocation and Scheduling of Connected Vehicle Service Requests in Cloud/Edge Computing
Yecheng Zhao, BaekGyu Kim · 2020
Emerging connected vehicle services powered by artificial intelligence and data analytic are gaining increasing interest and attention with the advancement of cloud/edge computing technologies. Given the highly data- and computation-intensive characteristics of these applications, it is important that requests for these services be carefully allocated in cloud/edge computing systems to optimize performance, resource utilization and cost. Challenges arises when mobility of vehicles is taken into consideration. Specifically, as services become increasingly sophisticated and computation intensive, a vehicle may travel non-trivial amount of distance during queuing and processing of a request, which affect transmission of result data upon service fulfillment. In these cases, it is important that allocation of request be aware of the expected position of the vehicle at the time of request completion as oppose to submission. In the general scenario where there exists multiple cloud/edge devices and resource contention, it is important to simultaneously consider vehicle trajectories, workload and scheduling of requests to jointly optimize allocation. In this paper, we study the problem of cost minimization in allocation and scheduling of connected vehicle service requests on heterogeneous cloud/edge services. We consider the scenario where vehicles have non-trivial mobility during service delay and model its impact on data transmission. We introduce an optimal ILP formulation as well as an efficient and close to optimal heuristic algorithm for solving the optimization problem. Experiment result shows that the proposed technique is capable of achieving 10% to 30% of improvement comparing with straightforward approaches.