Workload Prediction in Cloud Data Centers Using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized With Gazelle Optimization Algorithm

IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-11 DOI:10.1002/ett.70078
R. Karthikeyan, Saleem Raja Abdul Samad, V. Balamurugan, Sundaravadivazhagan Balasubaramanian, Robin Cyriac
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Abstract

Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. In this manuscript, Workload Prediction in Cloud Data Centers using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm (CVSTGCN-WLP-CDC) is proposed. Initially, the input data is collected from two standard datasets such as NASA and Saskatchewan HTTP traces dataset. Then, preprocessing using Multi-Window Savitzky–Golay Filter (MWSGF) is used to remove noise and redundant the data. The preprocessed data is fed to CVSTGCN for workload prediction in a dynamic cloud environment. In this work, proposed Gazelle Optimization Approach (GOA) used to enhance the CVSTGCN weight and bias parameters. The proposed CVSTGCN-WLP-CDC technique is executed and efficacy based on workload prediction structure is evaluated using several performances metrics such as accuracy, recall, precision, energy consumption correlation coefficient, sum of elasticity index (SEI), root mean square error (RMSE), mean squared prediction error (MPE), and percentage prediction error (PER). The proposed CVSTGCN-WLP-CDC provides 23.32%, 28.53% and 24.65% higher accuracy; 22.34%, 25.62%, and 22.84% lower energy consumption when comparing to the existing methods using Artificial Intelligence augmented evolutionary approach espoused cloud data centres workload prediction architecture (TCNN-CDC-WLP), Performance analysis of machine learning centered workload prediction techniques for cloud (PA-BPNN-CWPC), Machine learning methods for effectual energy utilization in cloud data centers (ARNN-EU-CDC) methods respectively.

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工作负载预测是云数据中心保持资源弹性和可扩展性的必要因素。然而,由于云数据中心工作负载预测的冗余性、噪声和低准确性,工作负载预测的准确性非常低。本文提出了使用瞪羚优化算法优化的复值时空图卷积神经网络(CVSTGCN-WLP-CDC)进行云数据中心工作量预测。首先,从 NASA 和萨斯喀彻温 HTTP 跟踪数据集等两个标准数据集中收集输入数据。然后,使用多窗口萨维茨基-戈莱滤波器(MWSGF)进行预处理,以去除噪声和冗余数据。预处理后的数据被输送到 CVSTGCN,用于动态云环境中的工作量预测。在这项工作中,提出的瞪羚优化方法(GOA)用于增强 CVSTGCN 的权重和偏置参数。基于工作负载预测结构的 CVSTGCN-WLP-CDC 技术通过准确度、召回率、精确度、能耗相关系数、弹性指数总和(SEI)、均方根误差(RMSE)、均方预测误差(MPE)和预测误差百分比(PER)等性能指标进行了评估。提出的 CVSTGCN-WLP-CDC 准确率分别提高了 23.32%、28.53% 和 24.65%;能耗分别降低了 22.34%、25.62% 和 22.84%。与采用人工智能增强进化方法的云数据中心工作量预测架构(TCNN-CDC-WLP)、以机器学习为中心的云计算工作量预测技术性能分析(PA-BPNN-CWPC)、云数据中心有效能源利用的机器学习方法(ARNN-EU-CDC)等现有方法相比,能耗分别降低了 84%。
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来源期刊
CiteScore
8.90
自引率
13.90%
发文量
249
期刊介绍: ransactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT), has the following aims: - to attract cutting-edge publications from leading researchers and research groups around the world - to become a highly cited source of timely research findings in emerging fields of telecommunications - to limit revision and publication cycles to a few months and thus significantly increase attractiveness to publish - to become the leading journal for publishing the latest developments in telecommunications
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