<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.</p></abstract>

The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

School of Computer Science & Engineering, Research Scholar, VIT University, Vellore, India

School of Advanced Sciences, VIT University, Vellore, India

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and...

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Online veröffentlicht: 22. Juni 2016

Seitenbereich: 97 - 112

DOI: https://doi.org/10.1515/cait-2016-0023

Schlüsselwörter
Energy efficient cloud computing, virtual machine consolidation, adaptive genetic algorithm, minimum migration time, dynamic threshold, service level agreement violation

© by Perla Ravi Theja

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Online veröffentlicht: 22. Juni 2016

Seitenbereich: 97 - 112

DOI: https://doi.org/10.1515/cait-2016-0023

SchlüsselwörterEnergy efficient cloud computing, virtual machine consolidation, adaptive genetic algorithm, minimum migration time, dynamic threshold, service level agreement violation

© by Perla Ravi Theja

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Energy efficient cloud computing, virtual machine consolidation, adaptive genetic algorithm, minimum migration time, dynamic threshold, service level agreement violation