Dynamic Energy-Saving Offloading Strategy Guided by Lyapunov Optimization for IoT Devices

Abstract

In the Internet of Everything era, various Internet of Things (IoT) devices have become popular, and the number of computing-intensive applications has increased substantially. As an emerging technology, mobile-edge computing (MEC) gives network edge nodes stronger computing and storage capabilities, bringing users a good Quality of Experience (QoE). By offloading some computing tasks to the edge for processing, the burden on IoT devices can be effectively reduced. However, this approach exacerbates the computing and storage resource depletion of the MEC server and the bandwidth and transmission cost of the wireless link used to offload computing tasks. Additionally, making an offloading decision online without future system status information is a considerable challenge. Therefore, we should study and design a reasonable offloading strategy to reduce the additional overhead, which is of significance. We establish a virtual queue model to describe the workload offloading problem of IoT devices in a two-layer MEC network. This is a stochastic optimization problem. Based on Lyapunov optimization, we transform the research problem into a deterministic optimization problem. A Lyapunov online energy consumption optimization algorithm (LOECOA) is proposed to effectively balance the system’s queue backlog and energy consumption. Based on theoretical analysis and a large number of experimental and numerical results, our algorithm performs better on energy consumption while satisfying the system constraints under a dynamic task arrival rate.