Edge Computing and Caching Optimization Based on PPO for Task Offloading in RSU-Assisted IoV

Mobile Edge Computing (MEC) and caching at vehicular network edge have been recognized as promising technologies in the context of autonomous driving. Roadside Units (RSUs) deployed on both sides of road are regarded as computing nodes and caching nodes, catering to vehicles' requests. Existing edge computing and caching technologies face two challenges: 1) Vehicles' requests keep changing, making content popularity hard to predict. 2) Passive computing and caching technologies struggle to meet demands of computation-intensive and latency-sensitive requests when considering task offloading. To tackle these challenges, we propose a proactive edge computing and caching scheme to optimize task offloading. This scheme involves RSUs proactively sensing and identifying potential tasks that may be requested. Subsequently, it performs edge computing and caches the content based on its predicted popularity to respond to vehicle requests. The primary obstacle of our solution lies in selecting appropriate edge computing and caching nodes to minimize task computation delay and maximize caching benefits. To achieve this objective, we formulate a 0–1 mathematical model and transform it into a Markov Decision Process. Subsequently, we propose a solution based on deep reinforcement learning. Through extensive simulations, we demonstrate that our scheme effectively reduces long-term average computation delay and improves overall response ratio to vehicles' requests.