Machine Learning for Edge Device Management: Utilizing Machine Learning Algorithms to Optimize Management Tasks for Edge Devices

Abstract

Machine learning (ML) has emerged as a promising approach for optimizing management tasks for edge devices in distributed computing environments. Edge devices, such as sensors, actuators, and embedded systems, are becoming increasingly prevalent in various domains, including IoT, industrial automation, and smart cities. However, managing these devices efficiently poses significant challenges due to their resource constraints, dynamic environments, and heterogeneous nature. This paper presents a comprehensive review of the recent advancements in utilizing ML for edge device management. We discuss various ML algorithms, including supervised, unsupervised, and reinforcement learning, and their applications in optimizing tasks such as resource allocation, energy management, and fault detection in edge devices. Furthermore, we analyze the key challenges and future research directions in this field to provide insights for researchers and practitioners aiming to enhance the management of edge devices.