A Resource Allocation Strategy for Edge Services Based on Intelligent Prediction

Artificial intelligence is one important technologies for industrial applications, but it requires a lot of computing resources and data to support it. With the development of edge computing and the Internet of Things, artiJicial intelligence is playing an increasingly important role in the field of edge services. Therefore, how to make intelligent algorithms to provide better services for the development of the Internet of Things has become an increasingly important topic. This paper focuses on the application of edge service distribution strategy and proposes a novel edge service distribution strategy based on intelligent prediction, which reduces the bandwidth consumption of edge service providers and minimizes the cost of edge service providers. In addition, this article uses the real data provided by the Wangsu Technology Company and an improved long short-term memory (LSTM) prediction method to dynamically change the bandwidth and achieves better optimization of resources allocation comparing with actual industrial applications. The simulation results show that our intelligent prediction can achieve good results, and the mechanism can achieve higher resource utilization.