A Truthful Auction for Green Continuous Task Allocation and Pricing in Edge Computing

With the advent of edge computing, more and more tasks are offloaded to edge servers, but the computing and storage capabilities of edge servers are limited. Although some works propose efficient schemes for task allocation and pricing, they may ignore users' preferences for continuous tasks. However, the combinatorial preference causes high computational complexity. In this paper, we propose a dominant-strategy incentive compatibility (DSIC) and computationally efficient mechanism for green continuous task allocation based on the combinatorial auction. Besides, the activity on edge (AOE) network is introduced to describe the continuity of tasks. The proposed mechanism gives an approximate solution to the winner determination problem (WDP) in polynomial time and a pricing strategy that can guarantee the truthfulness and individual rationality of auction participants. We demonstrate the approximate ratio of the proposed algorithm through theoretical analysis. Experimental results show that the proposed mechanism achieves truthfulness, individual rationality, and high computational efficiency while considering green continuous task allocation.

I. Introduction

With the rapid development of mobile computing, mobile device traffic has grown dramatically [1]. Due to limited resources, mobile devices may face insufficient capabilities when processing compute-intensive and latency-sensitive tasks [2], [3]. The advent of edge computing (EC) brings solutions to this problem. In the EC environment, there are edge servers with certain storage and computing capabilities, mobile devices can offload tasks to edge servers to expand computing and storage capabilities [4]. However, the lightweight characteristic of edge servers makes allocating and pricing tasks reasonably an urgent problem to be solved.