Intelligent Resource Management Platform for Edge Computing Environments

Abstract

Edge computing has emerged as an important paradigm for supporting latency-sensitive applications. However, resource constraints and heterogeneous device capabilities pose challenges to efficient task scheduling and service deployment. This paper develops an intelligent resource management platform for edge computing environments. The platform adopts a layered control architecture and incorporates workload prediction, adaptive resource allocation, and service migration mechanisms. Through coordinated management of computing, storage, and network resources, the system improves overall utilization efficiency. Experimental results demonstrate that the proposed platform effectively reduces service latency and enhances system stability under dynamic workload conditions.