SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host States Naive Bayesian Prediction Model

Li, Lianpeng; Dong, Jian; Zuo, Decheng; Liu, JIaxi

doi:10.1109/bdcloud.2018.00025

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…We also do some simulation experiments with random In order to compare with the researches in recent years, we choose ACS-VM [11], PCM [12], MadMCHD [13], and our two previous studies HSNBP [14], RobustSLR [15] as the benchmark algorithms, and calculate the Energy, SLAV, and number of VM migrations improvement percentages comparing to LR-MMT-1.2 [8] policy using the PlanetLab data set of "20110322". For the RobustSLR model, we choose the MAE(10) strategy with the best overall performance [15]. The results are shown in Table 7.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

Li¹,

Dong

Zuo

et al. 2019

IEICE Trans. Inf. & Syst.

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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.

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Section: Simulation Results and Analysismentioning

confidence: 99%

“…Then we use the HUBDTree for host underloading detection (line 14). Finally, if the host is underloaded, it will return true, otherwise it will return false(line [15][16][17][18][19]. for i = 0 to n − 1 do 7: for i = 0 to n − 1 do 10:…”

Section: Host Underloading Detectionmentioning

confidence: 99%

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

Li¹,

Dong

Zuo

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

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“…Among the four subproblems decomposed from the VM consolidation proposed by Neat, load detection is the most concerned research field. Li et al [19,20] proposed a three-order Markov chain model and a binary decision tree prediction model to predict the host load state, all of them aiming to simultaneously minimize the SLA violation and power consumption of data centers. Farahnakian et al [21] assumed that host Central Processing Unit (CPU) utilization can be fitted by a linear function and proposed a linear regression model to predict the next CPU utilization.…”

Section: Related Workmentioning

confidence: 99%

I-Neat: An intelligent framework for adaptive virtual machine consolidation

Liu

Zhao

Dong

et al. 2022

Tsinghua Sci. Technol.

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With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine (VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation.However, the performance of different algorithms is related with the characteristics of the workload and system status; some algorithms are suitable for Central Processing Unit (CPU)-intensive workload and some for web application workload. Therefore, an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms. Neat is an open-source dynamic VM consolidation framework, which is well integrated into OpenStack.However, it cannot conduct dynamic algorithm scheduling, and VM consolidation algorithms in Neat are few and basic, which results in low performance for energy saving and Service-Level Agreement (SLA) avoidance. In this paper, an Intelligent Neat framework (I-Neat) is proposed, which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system. The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms. The algorithm library is designed based on a template, and in addition to the algorithms of Neat, I-Neat adds six new algorithms to the algorithm library. Furthermore, the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code. Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.

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“…Lianpeng Li,Jian Dong,Decheng Zuo & JIaxi Liu [21] proposed a prediction model based on the Simple Bayesian Classifier to detect the future host state. They used the mean function to convert the CPU utilization history for the last hour, and then used them as the Bayes features and the host states (overloaded or not overloaded) as Bayesian Classifier labels.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Algorithm Naive Bayesian Classifier and Random Key Cuckoo Search for Virtual Machine Consolidation Problem

Moaly¹,

A.Youssef²

2020

IJACSA

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The trade-off between Energy consumption and SLA violation presents a serious challenge in cloud computing environments. A non-aggressive virtual machine consolidation algorithm is a good approach to reduce the consumed energy as well as SLA violation. A well-known strategy to deal with the virtual machine consolidation problem consists of four steps: host overloading detection, host under-loading detection, virtual machine selection and virtual machine placement. In this paper, the previous strategy is modified by merging the last two steps virtual machine selection and virtual machine placement, to avoid any poor solutions caused by solving both steps separately. In the host overloading/under-loading detection steps, we classified host status into five classes: Over-Utilized, Nearly Over-Utilized, Normal Utilized, Under-Utilized and Switched Off, then an algorithm, based on the Naive Bayesian Classifier, was introduced in order to detect the future host state for minimizing the number of virtual machine migrations; as a result, the energy consumption and performance degradation due to migrations will be minimized. In the virtual machine selection and placement steps, we introduced an algorithm based on the Random Key Cuckoo Search to reduce the energy consumption and enhance the SLA violation. To assess the algorithm, real data traces for 10 days, were used to verify the proposed algorithms. The experimental results proved that the proposed algorithms can significantly reduce the consumed energy as well as the SLA violation in data centers.

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SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host States Naive Bayesian Prediction Model

Cited by 7 publications

References 23 publications

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

I-Neat: An intelligent framework for adaptive virtual machine consolidation

Hybrid Algorithm Naive Bayesian Classifier and Random Key Cuckoo Search for Virtual Machine Consolidation Problem

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