Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network

TL;DR

This paper investigates the energy efficiency and delay tradeoff in MEC-enabled IoT networks, proposing an online resource allocation algorithm that balances these factors under user mobility and stochastic demands.

Contribution

It introduces a novel stochastic optimization framework and an online algorithm for joint radio and computational resource allocation in MEC-IoT systems, considering mobility and non-stationary demands.

Findings

The proposed algorithm achieves an $O(1/V)$ EE improvement and $O(V)$ delay bound.

Simulation results show significant EE-delay performance gains over baseline methods.

Theoretical analysis confirms the tradeoff bounds between energy efficiency and delay.

Abstract

Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering non-stationary computation demands, user mobility, and wireless fading channels. This paper aims to study the tradeoff between energy efficiency (EE) and service delay for multi-user multi-server MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.