A Storage Optimization Model for Cloud Servers in Integrated Communication, Sensing, and Computation System

Abstract

The massive amount of sensing and communication data that needs to be processed during the production process of complex heavy equipment generates heavy storage pressure on the cloud server-side, thus limiting the convergence of sensing, communication, and computing in intelligent factories. To solve the problem, based on machine learning techniques, a storage optimization model is proposed in this paper for reducing the storage pressure on the cloud server and enhancing the coupling between communication and sensing data. At first, based on the operation rules of the distributed file system on the cloud server, the proposed model screens and organizes the system logs. With the filtered logs, the model sets feature labels, constructs feature vectors, and builds sample sets. Then, based on the ID3 decision tree, a file elimination model is trained to analyze the files stored in the cloud server and predict their reusability. In practice, the proposed model is applied in the Hadoop Distributed File System and helps the system delete underutilized and low-value files and save storage space. Experiments show that the proposed model can effectively reduce the storage load on the cloud server and improve the integration efficiency of multisource heterogeneous data during complex heavy equipment production.

Predicting Frequent-Infrequent Access of Objects in Tiered Cloud Storage Services

“…The study by Wang et al [13] introduces a groundbreaking model for optimizing cloud storage, employing machine learning techniques to enhance storage efficiency across cloud servers. This research aligns closely with our work on leveraging predictive analytics for cloud storage management.…”