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

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.