Edge service resource allocation strategy based on intelligent prediction

TL;DR

This paper proposes an intelligent prediction-based edge service resource allocation strategy that reduces bandwidth consumption and improves resource utilization using real industrial data and an improved LSTM model.

Contribution

It introduces a novel edge service distribution strategy leveraging intelligent prediction and an improved LSTM model for dynamic bandwidth management.

Findings

Achieves better resource allocation compared to existing methods.

Reduces bandwidth consumption for edge service providers.

Improves resource utilization efficiency.

Abstract

Artificial intelligence is one of the important technologies for industrial applications, but it requires a lot of computing resources and sensor data to support it. With the development of edge computing and the Internet of Things, artificial intelligence are playing an increasingly important role in the field of edge services. Therefore, how to make intelligent algorithms provide better services and 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 an 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 and short term memory 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.