Workload prediction based on improved error correlation logistic regression algorithm and Cross‐TRCN of spatiotemporal neural network

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Network Management Pub Date : 2024-04-29 DOI:10.1002/nem.2272

Xin Wan, Xiang Huang, Fuzhi Wang

引用次数: 0

Abstract

In view of the randomness of user network usage behavior in data centers, which leads to a large randomness in power load, and considering that a single randomness processing method is usually difficult to fully characterize the uncertain characteristics of the system, this paper proposes a dual fusion prediction analysis model based on an improved error correlation logic regression algorithm and a novel spatiotemporal neural network structure called Cross‐TRCN. Two weight coefficients λ1 and λ2 are introduced to fuse the prediction results with different long‐term sequence prediction performance, thereby further eliminating the influence of random errors. The results show that it is feasible to predict the workload of data centers based on the improved error correlation logic regression algorithm and the innovative spatiotemporal neural network structure Cross‐TRCN.

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基于改进的误差相关逻辑回归算法和时空神经网络 Cross-TRCN 的工作量预测

鉴于数据中心用户网络使用行为的随机性导致电力负荷具有较大的随机性，同时考虑到单一的随机性处理方法通常难以完全表征系统的不确定性特征，本文提出了一种基于改进的误差相关逻辑回归算法和名为 Cross-TRCN 的新型时空神经网络结构的双重融合预测分析模型。引入两个权重系数 λ1 和 λ2 来融合不同长期序列预测性能的预测结果，从而进一步消除随机误差的影响。结果表明，基于改进的误差相关逻辑回归算法和创新的时空神经网络结构 Cross-TRCN 预测数据中心的工作量是可行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Network Management COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

5.10

自引率

6.70%

发文量

审稿时长

>12 weeks

期刊介绍： Modern computer networks and communication systems are increasing in size, scope, and heterogeneity. The promise of a single end-to-end technology has not been realized and likely never will occur. The decreasing cost of bandwidth is increasing the possible applications of computer networks and communication systems to entirely new domains. Problems in integrating heterogeneous wired and wireless technologies, ensuring security and quality of service, and reliably operating large-scale systems including the inclusion of cloud computing have all emerged as important topics. The one constant is the need for network management. Challenges in network management have never been greater than they are today. The International Journal of Network Management is the forum for researchers, developers, and practitioners in network management to present their work to an international audience. The journal is dedicated to the dissemination of information, which will enable improved management, operation, and maintenance of computer networks and communication systems. The journal is peer reviewed and publishes original papers (both theoretical and experimental) by leading researchers, practitioners, and consultants from universities, research laboratories, and companies around the world. Issues with thematic or guest-edited special topics typically occur several times per year. Topic areas for the journal are largely defined by the taxonomy for network and service management developed by IFIP WG6.6, together with IEEE-CNOM, the IRTF-NMRG and the Emanics Network of Excellence.