Maximize Potential Reserved Task Scheduling for URLLC Transmission and Edge Computing

Emerging Internet of vehicles systems brings interesting new applications, such as VR entertainment systems in a car. These applications frequently generate data processing requirements and require a rapid response to ensure user experience. The combination of edge computing mode and ultra-low-latency communications (URLLC) traffic can better meet the requirements of the above scenarios. All requests for signal transmission and data processing can be considered a latency-limited task. We study scheduling strategies of these tasks intending to maximize overall utility for all users. We show that finding an optimal schedule for at least N tasks is NP-hard in the utility-maximizing issue. We propose a heuristic algorithm to maximize the overall utility of all users from the perspective of residual utility. To simulate the tolerance of delay for different tasks, we designed three utility curves: exponential, linear, and step. Simulation results show that the proposed algorithm outperforms the benchmark.