This paper addresses the challenge of optimizing communication, computation, and storage I/O caching in Vehicular Edge Computing (VEC) platforms for autonomous vehicles. The exponential data generated by the autonomous vehicles demands low-latency connectivity with nearby edge servers. However, the existing VEC platforms struggle to meet the performance requirements, especially in real-time applications like collision avoidance. This work proposes a novel algorithm for joint allocation of computing resources, storage I/O cache, and communication resources, considering the diverse priorities and demands of key vehicular services. Our approach integrates application-specific optimizations, prioritization, and joint latency reduction considering communication, computation, as well as storage. Accounting for distinct priorities and data access characteristics of various vehicular services, our proposed feasible solution, employing dual decomposition and Lagrangian relaxation, significantly reduces service latency by up to 64% compared to the current state-of-the-art resource allocation in vehicular edge computing.
Reducing Computation, Communication, and Storage Latency in Vehicular Edge Computing
2024-06-24
556891 byte
Conference paper
Electronic Resource
English