Machine Learning-Based Task Clustering for Enhanced Virtual Machine Utilization in Edge Computing

Abstract

Edge computing provides cloud-like services at the network edge near mobile users. Due to the prosperity of smart applications that involve computing-intensive tasks, edge devices are intended to provide sufficient amounts of resources in order to accommodate the increasing computing demands. However, computing resources could also suffer being underutilized which leads to both resource and energy wastage. In this paper, heterogeneous virtual machine (VM) allocation in edge computing is considered to cope with the different computing demands at each edge device. To this end, an unsupervised machine learning technique, namely, the K-means is used to cluster incoming tasks into three different categories according to their processing requirements. Afterwards, tasks belonging to each cluster will be allocated the appropriate type of VMs to better utilize the computing resources. Results show the effectiveness of the proposed scheme in clustering computing tasks and improving resource utilization in edge devices.