Online Auction Based Resource Allocation for Soft-Deadline Tasks in Edge Computing

With the development of edge computing (EC), more and more tasks are offloaded to edge servers (ESs). However, faced with a huge number of users offloading tasks to ESs, how to allocate resources reasonably and reduce the response time of the system are problems worth studying. In this paper, we design an online auction algorithm to deal with those two issues at the same time. We first introduce four task value functions to model the sensitivity to the delay of different tasks. Then, we construct a three-layer EC model. Based on it, we define the resources allocation problem as a social welfare (SW) maximization problem, which is NP-hard. To solve this problem, we utilize the master-dual technique to transform it into an online auction problem. Finally, an algorithm considering task classification is proposed, which realizes both resource allocation and latency reduction in a polynomial time. Experiment results show that our approach reduces the scheduling latency by an average of 38% while maintains SW at the same time.