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

Abstract

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.

A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

“…However, the consolidation of VMs will inevitably lead to a decline in service quality, resulting in Service Level Agreement (SLA) violations. Therefore, when formulating a consolidation strategy, it is necessary to ensure excellent user service quality [7][8][9][10] while minimizing energy consumption.…”

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

“…If the ability of a provider is relatively high as well as the provider often acquires the suggested overbooking strategy, optimal profit may be obtained and its SLA breach rejected. Li et al (2019) suggested a VM positioning algorithm as well as a host overload / underload identification algorithm for saving energy and SLA-aware virtual machine consolidation focused on their suggested stable, easy linear prediction model of regression in cloud data centres. In contrast to the native linear regression, our suggested approaches modify an estimate and squint at over-prediction while applying the error to the forecast.…”

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

“…Li, Dong, Zuo and Wu [16] proposed host overloading or underloading detection method and linear regression prediction model for SLA-aware and energy efficient VM consolidation. Eight methods were used to calculate the error of the model.…”