Machine-Learning-Based Performance Prediction for CDN Cache Groups in Meta Computing

Senmao Qi, Xulong Wang, Yifei Zou, Yuan Yuan, Yihong Ling, Guangzheng Lin, Ruomei Liu, Yijun Li, Dongxiao Yu

Abstract: Meta computing, as an innovative computing paradigm, aims to transform the Internet into a vast and distributed computing resource pool. This paradigm holds significant promise for the Industrial Internet of Things (IIoT), offering efficient, fault-tolerant, and personalized services while ensuring strong security and privacy. Nowadays, content delivery networks (CDNs) are integral to this vision, providing critical network support by reducing latency, alleviating network congestion, and enhancing service quality. Accurate prediction of CDN cache group performance, which involves heterogeneous edge servers handling diverse workloads, is essential for optimal resource utilization, dynamic load balancing, and efficient traffic management in IIoT. This article addresses the challenge of performance prediction in CDNs using machine learning techniques. By leveraging business request data, load information, and other relevant features, our approach aims to predict key performance indicators, such as CPU utilization, bandwidth usage, and I/O operations. We propose a comprehensive feature engineering method that aggregates input metrics across devices, categorizes business requests using clustering, and incorporates time series modeling to capture traffic patterns. Extensive experiments demonstrate the effectiveness of our approach, highlighting its potential to enhance resource management and service quality in CDNs, thereby supporting the deployment of meta computing in IIoT.

External IDs:dblp:journals/iotj/QiWZYLLLLY25