User allocation‐aware edge cloud placement in mobile edge computing

TLDR

Mobile edge computing is emerging as a ubiquitous platform to overcome mobile device resource limits and core network bandwidth bottlenecks, and placing edge clouds as small data centers at the network edge is key for cost reduction and QoS improvement. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. We formulate the problem as a multi‑objective optimization balancing workload among edge clouds and minimizing user communication delay, and propose an approximate solution using K‑means clustering and mixed‑integer quadratic programming, evaluated on Shanghai Telecom base‑station data against representative baselines. Our experiments show that the proposed approach improves workload balance and reduces communication delay compared to other representative methods.

Abstract

Summary Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K‐means and mixed‐integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.