Energy-latency tradeoff for task offloading and resource allocation in vehicular edge computing

Abstract

Vehicular edge computing (VEC) has emerged as a cutting-edge distributed computing paradigm capable of addressing network congestion and excessive energy use in vehicular systems. To enhance VEC performance, we examined the energy–latency tradeoff for partial tasks offloading in end-VEC-cloud orchestrated networks. We formulated a joint computation offloading and resource allocation problem aimed at minimizing latency and energy consumption. To address the underlined problem, we proposed a collaborative task splitting and resource allocation optimization (CTSRAO) algorithm. We initially decoupled the problem into two convex sub-problems and then applied the Lagrangian and simplex methods for joint optimization of computation resources and task splitting ratio. Furthermore, we investigated the criteria for determining whether a task should be offloaded to the VEC or cloud. Simulation results showed that our algorithm significantly enhances systems performance, achieving lower latency and energy consumption than the benchmark and state-of-the-art methods.