SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Robust Linear Regression Prediction Model

Virtual machine (VM) consolidation provides a promising approach to save energy and to improve resource utilization in data centers. However, the aggressive consolidation of virtual machines may lead to service-level agreements (SLA) violation, which is essential for data centers and their users. Th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.9490-9500
Main Authors: Li, Lianpeng, Dong, Jian, Zuo, Decheng, Wu, Jin
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
cloud data centers
Computer centers
Consolidation
Data centers
Energy consumption
energy-efficient
Linear regression
Prediction algorithms
Prediction models
Predictive models
Regression analysis
Regression models
Resource management
Resource utilization
Robustness (mathematics)
SLA-aware
Virtual environments
VM consolidation
Workload
Workloads
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Virtual machine (VM) consolidation provides a promising approach to save energy and to improve resource utilization in data centers. However, the aggressive consolidation of virtual machines may lead to service-level agreements (SLA) violation, which is essential for data centers and their users. Therefore, it is very meaningful to strike a tradeoff between power efficient and reduction of SLA violation level. In this paper, we propose a host overloading/underloading detection algorithm and a new VM placement algorithm based on our proposed robust simple linear regression prediction model for SLA-aware and energy-efficient consolidation of virtual machines in cloud data centers. Different from the native linear regression, our proposed methods amend the prediction and squint toward over-prediction by adding the error to the prediction; in this paper, we propose eight methods to calculate the error. We evaluate our proposed algorithms by extended the CloudSim simulator using PlanetLab workload and random workload. The experimental results show that our proposed model can reduce SLA violation rates by at most 99.16% and energy consumption by at most 25.43% for the real-world workload.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2891567