Resource Scheduling and Optimization Strategy in Edge Computing Environment

With the rapid development of the Internet of Things (IoT), big data and artificial intelligence (AI) technologies, edge computing, as a new computing model, is gradually becoming a key technology for processing massive data and realizing low latency services. Edge computing emerged as a cutting-edge computing paradigm. By pushing computing resources and data storage to the edge of the network, it greatly shortens the data processing path and provides an efficient solution for dealing with large-scale data processing and low latency requirements. In order to further improve the utilization efficiency of computing resources and optimize system performance indicators, the optimization of resource allocation and task scheduling in edge computing has become the focus of academic and industrial circles. This paper innovatively proposes a resource scheduling and optimization model based on edge computing, which deeply integrates AI algorithm to achieve dynamic optimal allocation of resources and intelligent scheduling of tasks. Through simulation experiments, it has been verified that the model not only significantly improves resource utilization and reduces task completion time, but also effectively responds to network fluctuations and load changes, demonstrating strong robustness and adaptability.