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

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 fus...

Full description

Saved in:
Bibliographic Details
Published in:International journal of network management Vol. 35; no. 1
Main Authors: Wan, Xin, Huang, Xiang, Wang, Fuzhi
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.01.2025
Subjects:
Algorithms
Correlation
cross TRCN
Data centers
Error analysis
improved error correlation logistic regression
Neural networks
Random errors
Randomness
Regression models
Spatiotemporal data
Uncertainty analysis
Workload
workload prediction
ISSN:1055-7148, 1099-1190
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. This prediction method combines an improved error related logistic regression algorithm and an innovative spatiotemporal neural network Cross‐TRCN to predict and analyze the workload of data centers. And the workload prediction of data centers is obtained by combining the prediction results of two methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1055-7148
1099-1190
DOI:10.1002/nem.2272