An Efficient Cloudlet Deployment Method Based on Approximate Graph Cut in Large-scale WMANs

Mobile edge computing provides a low-latency, high-bandwidth cloud computing environment for resource-constrained mobile devices by allowing mobile devices to offload tasks, but user task migration causes greater transmission delays. Cloudlets, a new component of mobile edge computing, can perform tasks offloaded by mobile users nearby to reduce the access latency and meet users' requirements for system response time. However, deploying cloudlets in large-scale wireless metropolitan area networks (WMANs) to improve the service quality of mobile applications is currently still difficult. To resolve this issue, we design a cloudlet deployment model based on approximate graph cut, which abstracts the wireless communication network into an undirected weighted graph, divides the graph according to the access point location attributes, and minimizes the user access delay of subgraphs to obtain optimal network area segmentation and cloudlet deployment locations. We also develop an efficient kernel method to optimize the objective function of graph cuts. The simulation experimental results demonstrate that our model has low time and space complexity; thus, it is suitable for large-scale cloudlet deployment and has valuable application prospects.