Multi-User Offloading for Edge Computing Networks: A Dependency-Aware and Latency-Optimal Approach

Abstract

Driven by the tremendous application demands, the Internet of Things (IoT) systems are expected to fulfill computation-intensive and latency-sensitive sensing and computational tasks, which pose a significant challenge for the IoT devices with limited computational ability and battery capacity. To address this problem, edge computing is a promising architecture where the IoT devices can offload their tasks to the edge servers. Current works on task offloading often overlook the unique task topologies and schedules from the IoT devices, leading to degraded performance and underutilization of the edge resources. In this article, we investigate the problem of fine-grained task offloading in edge computing for low-power IoT systems. By explicitly considering: 1) the topology/schedules of the IoT tasks; 2) the heterogeneous resources on edge servers; and 3) the wireless interference in the multiaccess edge networks, we propose a lightweight yet efficient offloading scheme for multiuser edge systems, which offloads the most appropriate IoT tasks/subtasks to edge servers such that the expected execution time is minimized. To support the multiuser offloading, we also propose a distributed consensus algorithm for low-power IoT devices. We conduct extensive simulation experiments and the results show that the proposed offloading algorithms can effectively reduce the end-to-end task execution time and improve the resource utilization of the edge servers.