Stochastic Virtual Machine Placement for Cloud Data Centers Under Resource Requirement Variations

Zhou, Junlong; Zhang, Yi; Sun, Lulu; Zhuang, Sisi; Tang, Cheng; Sun, Jin

doi:10.1109/access.2019.2957340

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…Large data centers encounter more challenges, such as hardware and device glitches, contributing to inconsistency in system efficiency and ultimately reduction of quality of service. The most common solution is to combine VMs, a strategy that seeks to bundle as many VMs as possible into one single physical host, to increase the energy and resources performance of data centers [52]. In addition, the live VM migration enables a complex aggregation of VMs to maximize energy efficiency and better VM location by swapping idle hosts on low power modes.…”

Section: Resource Managementmentioning

confidence: 99%

Optimization of power consumption in data centers using machine learning based approaches: a review

Kumar

Khatri

Diván

2022

IJECE

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<span>Data center hosting is in higher demand to fulfill the computing and storage requirements of information technology (IT) and cloud services platforms which need more electricity to power on the IT devices and for data center cooling requirements. Because of the increased demand for data center facilities, optimizing power usage and ensuring that data center energy quality is not compromised has become a difficult task. As a result, various machine learning-based optimization approaches for enhancing overall power effectiveness have been outlined. This paper aims to identify and analyze the key ongoing research made between 2015 and 2021 to evaluate the types of approaches being used by researchers in data center energy consumption optimization using Machine Learning algorithms. It is discussed how machine learning can be used to optimize data center power. A potential future scope is proposed based on the findings of this review by combining a mixture of bioinspired optimization and neural network.</span>

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Section: Resource Managementmentioning

confidence: 99%

Optimization of power consumption in data centers using machine learning based approaches: a review

Kumar

Khatri

Diván

2022

IJECE

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“…The authors 21 included SLAs and statistical methods for VMP along with improving resource utilization. According to the available statistical data on resource utilization, a forecasting model was built using the neural networks.…”

Section: Related Workmentioning

confidence: 99%

“…According to the available statistical data on resource utilization, a forecasting model was built using the neural networks. A correlation aware VMP was proposed 21 to allocate resources for VM to match the future demand, thus improving the resource utilization.…”

Section: Related Workmentioning

confidence: 99%

“…VirtCo focuses on placing the VM well in advance to its launch based on the coflow information between the VM. The coflow information is determined based on Cui et al 20 The authors 21 included SLAs and statistical methods for VMP along with improving resource utilization. According to the available statistical data on resource utilization, a forecasting model was built using the neural networks.…”

Section: Related Workmentioning

confidence: 99%

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An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

Padmavathy

Anitha

2022

Int J Communication

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Summary With the rapid growth of the Internet, technological advancements, and the increasing number of cloud users, a paradigm transformation towards separate data and control planes with a centralized control point is taking place. As a result of this separation, software‐defined networks (SDNs) have distinct software controllers for routers and the capacity to manage network behavior. Virtual machine placement (VMP) and virtual machine migration (VMM) are two examples of SDN usage in data centers. The issue of VMP in cloud environments is a significant challenge that has been widely researched but not fully resolved. In this research, we used the single stage weapon target assignment (SSWTA) to model the virtual machine allocation (VMA) for jobs in order to maximize the resource utilization of the physical machines (PMs) by reducing idle time. Furthermore, this research proposes three algorithms: job assignment algorithms, PM allocation (PMA) using the least recently used (LRU) policy, and optimal VMA. The proposed VMA's performance is compared to other related VMA models such as first fit decreasing, and it is determined to be efficient in terms of resource utilization.

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“…Authors in [12] proposed a stochastic VMP approach to increase energy efficiency and resource utilization of the data center. Here resource requirements are modeled as random variables instead of taking deterministic values to denote resource requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm

Madhusudhan

T²,

Mustapha

et al. 2021

Scientific Programming

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In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.

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Stochastic Virtual Machine Placement for Cloud Data Centers Under Resource Requirement Variations

Cited by 12 publications

References 38 publications

Optimization of power consumption in data centers using machine learning based approaches: a review

Optimization of power consumption in data centers using machine learning based approaches: a review

An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm

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