Optimal Task Offloading with Deep Q-Network for Edge-Cloud Computing Environment

• Published in: 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) pp. 406 - 411
• Main Authors: Ullah, Ihsan, Lim, Hyun-Kyo, Seok, Yeong-Jun, Han, Youn-Hee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.10.2022
• Subjects: 5G network; Cloud computing; Costs; Deep Q-Network; Dynamic scheduling; Edge cloud computing; IoT; Markov decision process (MDP); Performance evaluation; Reinforcement learning; Resource allocation; Resource management; Simulation
• ISSN: 2162-1241
• DOI: 10.1109/ICTC55196.2022.9952511

The edge-cloud computing paradigm has become a key component of fifth-generation (5G) networks and beyond. It becomes difficult to meet the computation-intensive and delay-sensitive service requirements of intelligent IoT applications by using only edge computing or cloud computing alone. Therefore, an intelligent resource management technique is required to efficiently distribute the task offloading to the edge and cloud computing systems. In this paper, we aim to optimize the task offloading under delay constraints to maximize resource utilization and minimize the offloading rejection in the edge-cloud computing system. Considering optimal decisions for task offloading and resource allocation deep reinforcement learning is applied. We formulate this optimization problem by the Markov decision process and then use Deep Q-Network (DQN) to find the optimal policy for task offloading. Thus, we design the DQN-edge-cloud (DQNEC) computing scheme to update the policy and optimally offload the task in a dynamic edge-cloud environment with considering resource utilization. The simulation results show that DQNEC achieves good performance than the heuristic approaches in terms of maximizing resource utilization with minimum cost and maximizing task offloading with minimum task rejection.