Mobile Edge Computing Network Control: Tradeoff Between Delay and Cost

TL;DR

This paper studies multi-hop mobile edge computing networks, proposing a distributed control algorithm that balances delay and cost for multi-task offloading, scheduling, and resource allocation, validated through simulations.

Contribution

It introduces a Lyapunov-based distributed algorithm for dynamic MEC network control that optimizes throughput, delay, and cost in multi-task, multi-hop scenarios.

Findings

The algorithm achieves throughput-optimal performance.

Simulation results validate delay and cost guarantees.

Guidelines on resource interplay in MEC networks are provided.

Abstract

As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on singletask offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.