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SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model
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Summary :
For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.10 pp.1942-1951
- Publication Date
- 2019/10/01
- Publicized
- 2019/07/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7441
- Type of Manuscript
- PAPER
- Category
- Computer System
Authors
Lianpeng LI
Harbin Institute Of Technology
Jian DONG
Harbin Institute Of Technology
Decheng ZUO
Harbin Institute Of Technology
Yao ZHAO
Harbin Institute Of Technology
Tianyang LI
Harbin Institute Of Technology
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Lianpeng LI, Jian DONG, Decheng ZUO, Yao ZHAO, Tianyang LI, "SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 10, pp. 1942-1951, October 2019, doi: 10.1587/transinf.2018EDP7441.
Abstract: For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7441/_p
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@ARTICLE{e102-d_10_1942,
author={Lianpeng LI, Jian DONG, Decheng ZUO, Yao ZHAO, Tianyang LI, },
journal={IEICE TRANSACTIONS on Information},
title={SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model},
year={2019},
volume={E102-D},
number={10},
pages={1942-1951},
abstract={For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.},
keywords={},
doi={10.1587/transinf.2018EDP7441},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model
T2 - IEICE TRANSACTIONS on Information
SP - 1942
EP - 1951
AU - Lianpeng LI
AU - Jian DONG
AU - Decheng ZUO
AU - Yao ZHAO
AU - Tianyang LI
PY - 2019
DO - 10.1587/transinf.2018EDP7441
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.
ER -
English
