LAUSR

As the 5th generation (5G) network develops and rolls out rapidly, user requests can be offloaded to nearby edge servers for processing. This alleviates the pressure on the network backhaul and the remote cloud. Nevertheless, the edge user allocation (EUA) problem, as one of the main research challenges in the 5G era, has become a major obstacle to ensuring users’ Quality of Experience (QoE) in the edge computing environment. Conventional EUA approaches, ranging from static global allocation model to online decision-making model, have ignored the long-term impact of the changes in users’ expectations on user-perceived Quality of Service (QoS). Additionally, most existing approaches have not taken into account the distance between an edge user and an edge server, which impacts the user’s data rate profoundly. In this paper, we tackle these challenges by formulating EUA problem as a spatial-temporal one (ST-EUA), which models distance-aware QoS based on the wireless transmission attenuation and models users’ QoE based on the Expectation Confirmation Theory (ECT). To find an appropriate solution for ST-EUA problem, we develop two fuzzy control-based approaches, namely FC and BFC, for on demand scenarios and batch processing scenarios, respectively. They can balance effectively the user consolidation and server load. We conduct extensive experiments based on two widely-used real-world datasets. The results demonstrate the superiority of our FC and BFC in effectiveness and efficiency over the baselines and state-of-the-art.