An Innovative Task Offloading Algorithm Based on Deep Reinforcement Learning in Computation Resource Network

• Published in: 2024 International Wireless Communications and Mobile Computing (IWCMC) pp. 1 - 7
• Main Authors: Long, Yufei, Zeng, Qimiao, Zhuang, Yirong, Pan, Qing, Xiao, Han
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27-05-2024
• Subjects: Actor Critic; Computation Resource Network; Computational modeling; Costs; Deep reinforcement learning; Delay effects; Energy consumption; Resource management; Servers; Task Offloading
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC61514.2024.10592363

With the proliferation of Internet of Things (IoT) devices and the exponential growth of data generated at the network edge, there is a pressing need for efficient task offloading strategies in edge-cloud collaborative systems. In this study, we address the optimization of task offloading decisions and computation resource allocation in a multi-user computation resource network comprising edge servers and a centralized cloud server interconnected. Our objective is to minimize both time delay and energy consumption. We formulate the problem as an optimization task aiming to minimize the integrated cost of latency and energy consumption while satisfying the delay and computation resource requirements, resulting in a non-convex, NP-hard problem. To tackle this challenge, we propose a deep reinforcement learning approach, specifically the Actor-Critic based Task Offloading Optimization Network (ACTOON). Extensive simulations are conducted to demonstrate the superiority of ACTOON over other baseline methods.