Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach

Abstract

Abstract Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile devices to edge or cloud servers. However, Task offloading may result in increased energy consumption and delays, and the decision to offload the task is dependent on various factors such as time-varying radio channels, available computation resources, and the location of devices. As edge-cloud computing is a dynamic and resource-constrained environment, making optimal offloading decisions is a challenging task. This paper aims to optimize offloading and resource allocation to minimize delay and meet computation and communication needs in edge-cloud computing. The problem of optimizing task offloading in the edge-cloud computing environment is a multi-objective problem, for which we employ deep reinforcement learning to find the optimal solution. To accomplish this, we formulate the problem as a Markov decision process and use a Double Deep Q-Network (DDQN) algorithm. Our DDQN-edge-cloud (DDQNEC) scheme dynamically makes offloading decisions by analyzing resource utilization, task constraints, and the current status of the edge-cloud network. Simulation results demonstrate that DDQNEC outperforms heuristic approaches in terms of resource utilization, task offloading, and task rejection.

Keywords

• Edge-cloud computing
• Task offloading
• Resource allocation
• Deep Reinforcement learning
• Markov decision process (MDP)