Deep reinforcement learning-based online task offloading in mobile edge computing networks

Mobile edge computing has been a promising paradigm to reduce the computation delay of tasks and extend the battery life of Internet of Things devices. Most existing task offloading methods in mobile edge computing networks are based on stable task arrivals and homogeneous users. However, in practical network environments, the frequency at which an Internet of Things device generates tasks is typically time-variant, and user requirements for security guarantees and response performance are always differential. Motivated by this, we investigate online task offloading in mobile edge computing networks with diverse users. By dividing edge nodes into public and private ones, we present a novel collaboration architecture. Considering correlated arrivals, we construct a system model comprising local computing and cloud-edge computing models. Under a matrix-geometric solution framework, we analyze the system model to derive the overall task latency. For mobile edge computing networks with variable task arrival intensity, we propose a deep reinforcement learning-based online task scheduling algorithm to realize online task offloading. Experimental results demonstrate that compared to the offline task scheduling algorithm, for the correlation coefficient of 0, 0.1, and 0.2, the proposed online task scheduling algorithm can reduce the overall task latency by about 1.09%, 2.65%, and 4.34%, respectively.