ST-EUA: Spatio-temporal Edge User Allocation with Task Decomposition

Abstract

Recently, edge user allocation (EUA) problem has received much attentions. It aims to appropriately allocate edge users to their nearby edge servers. Existing EUA approaches suffer from a series of limitations. First, considering users' service requests only as a whole, they neglect the fact that a service request may be partitioned into multiple tasks to be performed by different servers. Second, the impact of the spatial distance between edge users and servers on users' quality of experience is not properly considered. Third, the temporal dynamics of users' service requests has not been fully investigated. To overcome these limitations systematically, this paper focuses on the problem of spatio-temporal edge user allocation with task decomposition (ST-EUA). We first formulate the ST-EUA problem. Then, we transform ST-EUA problem as a multi-objective optimization problem and prove its NP-hardness. To tackle the ST-EUA problem effectively and efficiently, we propose a novel genetic algorithm-based heuristic approach GA-ST, aiming to maximize usersoverall QoE while minimizing migration cost in different time slots. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of GA-ST. The results demonstrate that GA-ST significantly outperforms state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.