Auction-Based Dependent Task Offloading for IoT Users in Edge Clouds

Abstract

The rapid proliferation of latency-sensitive Internet of Things (IoT) applications boosts the frequency of offloading compute-intensive tasks from IoT users to mobile edge computing (MEC) due to the limitation resources of IoT devices. It is inevitably for IoT users to compete for the computing resources of the MEC, especially when the computation tasks are dependent and have hard deadline constraints. However, most existing dependent task offloading schemes may not well consider the resource competition issues among IoT users, and possibly lead to limited system performance in multiuser scenario. To address this issue, we intend to design an auction-based dependent task-offloading mechanism to improve the efficiency of task offloading for multiple IoT users. First, we formulate the dependent task offloading as a valuation maximization problem in the trade of computing resources satisfying users’ latency requirements, which has been proved to be NP-hard. Then, by jointly considering the task graph structure and the current status of the MEC, we propose a truthful auction mechanism, named greedy winner selection strategy, in which a heuristic dependent task assignment for winners is designed to improve the efficiency of the task offloading. By conducting extensive simulations, we validate that the performance of the proposed dependent task offloading strategy is superior to existing competition algorithms, in terms of total valuations, average makespans, and success rates.