Load Prediction Energy Efficient VM Consolidation Policy in Multimedia Cloud

2020 ◽  
pp. 893-903
Author(s):  
K. P. N. Jayasena ◽  
G. K. Suren W. de Chickera
Keyword(s):  
Energy Efficient ◽  
Load Prediction ◽  
Multimedia Cloud ◽  
Vm Consolidation

Failure-aware energy-efficient VM consolidation in cloud computing systems

2019 ◽  
Vol 94 ◽  
pp. 620-633 ◽  
Author(s):  
Yogesh Sharma ◽  
Weisheng Si ◽  
Daniel Sun ◽  
Bahman Javadi
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Computing Systems ◽  
Vm Consolidation

scholarly journals Energy-efficient and quality-aware VM consolidation method

2020 ◽  
Vol 102 ◽  
pp. 789-809 ◽  
Author(s):  
Zhihua Li ◽  
Xinrong Yu ◽  
Lei Yu ◽  
Shujie Guo ◽  
Victor Chang
Keyword(s):  
Energy Efficient ◽  
Vm Consolidation

An Energy Efficient and Adaptive Threshold VM Consolidation Framework for Cloud Environment

2020 ◽  
Vol 113 (1) ◽  
pp. 349-367
Author(s):  
Nagma Khattar ◽  
Jaiteg Singh ◽  
Jagpreet Sidhu
Keyword(s):  
Energy Efficient ◽  
Adaptive Threshold ◽  
Cloud Environment ◽  
Vm Consolidation

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

2016 ◽  
Vol 16 (2) ◽  
pp. 97-112 ◽  
Author(s):  
Perla Ravi Theja ◽  
S. K. Khadar Babu
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Large Scale ◽  
Robust Regression ◽  
Evolutionary Computing ◽  
Adaptive Genetic Algorithm ◽  
Local Regression ◽  
Absolute Deviation ◽  
Cloud Computing System ◽  
Vm Consolidation

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.

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