A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

作     者：Mohammad Aldossary 

作者机构：Department of Computer ScienceCollege of Arts and SciencePrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3531-3562页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Prince Sattam Bin Abdulaziz University, Saudi Arabia Deanship of Scientific Research, King Saud University 

主　　题：Cloud computing energy efficiency auto-scaling live migration workload prediction energy prediction cost estimation 

摘      要：With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’***,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are ***,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)***,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous *** approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total ***,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective *** testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.