Research on Heterogeneous Computation Resource Allocation Based on Data-Driven Method

The rapid development of the mobile Internet and the Internet of Things is leading to a diversification of user devices and the emergence of new mobile applications on a regular basis. Such applications include those that are computationally intensive, such as pattern recognition, interactive gaming, virtual reality, and augmented reality. However, the computing and energy resources available on the user's equipment are limited, which presents a challenge in effectively supporting such demanding applications. In this work, we propose a heterogeneous computing resource allocation model based on a data-driven approach. The model first collects and analyzes historical workload data at scale, extracts key features, and builds a detailed data set. Then, a data-driven deep neural network is used to predict future resource requirements. Based on the prediction results, the model adopts a dynamic adjustment and optimization resource allocation strategy. This strategy not only fully considers the characteristics of different computing resources, but also accurately matches the requirements of various tasks, and realizes dynamic and flexible resource allocation, thereby greatly improving the overall performance and resource utilization of the system. Experimental results show that the proposed method is significantly better than the traditional resource allocation method in a variety of scenarios, demonstrating its excellent accuracy and adaptability.

1. Introduction

The rapid development of the Fifth Generation (5G) and the Internet of Things (10 T) has resulted in a significant increase in the number of user devices connected to the Internet. This has been accompanied by a considerable rise in the volume of data and a proliferation of sophisticated mobile applications and services. Among these applications, those that are computationally intensive, such as pattern recognition, interactive games, and virtual/augmented reality, not only consume a considerable amount of computing resources and energy, but also require ultra-low response latency [1]. However, the limitations of hardware technology result in the computing, storage and energy resources of mobile devices being constrained to varying degrees. As a result, it is difficult to provide effective support for the deployment of latency-sensitive and computationally intensive applications.