Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

TLDR

IoT applications are computation‑intensive, and the limited capacity of end devices degrades performance, so tasks are offloaded to Mobile Edge Computing, though this incurs significant energy for transmission and processing. The study investigates energy‑efficient task offloading strategies in Mobile Edge Computing. The authors formulate a stochastic optimization problem to minimize offloading energy while bounding average queue length, transform it into a deterministic problem, and propose the online EEDOA algorithm with polynomial‑time complexity. Theoretical analysis demonstrates that EEDOA approximates minimal transmission energy while bounding queue length, and experiments confirm its effectiveness.

Abstract

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA's effectiveness.