Learning Based Energy Efficient Task Offloading for Vehicular Collaborative Edge Computing

Abstract

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles' idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay.