Cloud-Edge Collaborative Computing for Consumer Electronics via Deep Reinforcement Learning

With the explosive growth of consumer electronic devices, edge computing has emerged as a promising paradigm to process large-scale data in real-time and enhance data privacy. However, consumer electronic devices’ limited computing power and energy pose significant challenges to efficiently executing computation-intensive tasks. To tackle this problem, we present a cloud-edge collaborative computing offloading method that relies on deep reinforcement learning. More precisely, we construct an optimization problem with the goal of minimizing the combined delay in task execution and energy consumption, taking into account the computing resources, bandwidth, and offloading policies. We then develop an asynchronous cloud-edge collaborative deep reinforcement learning (CEC-DRL) algorithm to solve the optimization problem. The CEC-DRL algorithm leverages the computing capabilities of both cloud and consumer electronic devices to satisfy the demand for efficient data processing in large-scale consumer electronics scenarios. Moreover, it can adaptively adjust the offloading policy to minimize the system cost under various and dynamic environments. Simulation results demonstrate that the CEC-DRL algorithm provides fast convergence, high resilience, and almost ideal offloading policies while incurring the lowest computation cost.