Dynamic and Preemptive Task Offloading in Edge-cloud Computing Systems

Abstract

The edge-cloud computing systems are widely used to support various computation services. In this paper, we consider a dynamic task offloading problem in the edge-cloud computing system with multiple independent and stochastic arriving tasks. The system periodically schedules and offloads tasks to heterogenous resources in consideration of the required transmission delays and computation times. Our goal is to minimize the sum of weighted response times of all the tasks. A greedy local search based online offloading framework is proposed for the problem under study, which dynamically assigns tasks to the appropriate destination (edge servers or cloud servers) and preemptively allocates computing resources to each task according to its latency-sensitivity. Evaluation experiments are delicately designed on a number of testing instances with various parameter settings. Experimental results indicate that the proposal algorithm is more effective than the compared algorithms.