VM Consolidation for Cloud Data Center Using Median Based Threshold Approach

Sharma, Oshin; Saini, Hemraj

doi:10.1016/j.procs.2016.06.005

Cited by 24 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the efficiency of the network, Chang et al [ 25 ] have predicted the workload on the servers with the help of recurrent neural networks. To minimize the violations of SLA and performance degradation while maintaining the optimal level of energy consumption, Sharma and Saini [ 26 ] have introduced a novel technique for the consolidation of virtual machines in the cloud environment. In 2017, Farahnakian et al [ 27 ] have proposed a self-adaptive algorithm for threshold, detection while considering CPU and memory as resources for calculating the utilization.…”

Section: State Of Art For Energy-efficient Vm Consolidationmentioning

confidence: 99%

“…of HS No. of VMM Beloglazov et al [ 19 ] * * Hongjian Li et al [ 20 ] * * * * Yahya et al [ 21 ] * * * Chinnaiah et al [ 22 ] * * Xiao et al [ 23 ] * * * Beloglazov et al [ 24 ] * * * Sharma and Saini [ 26 ] * * Farahnakian et al [ 27 ] * * * Minarolli et al [ 28 ] * * …”

Section: Consolidation Based Solutions For Energy Efficiency In CC En...mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Magotra

Malhotra

Dogra

2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO 2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

show abstract

Section: State Of Art For Energy-efficient Vm Consolidationmentioning

confidence: 99%

Section: Consolidation Based Solutions For Energy Efficiency In CC En...mentioning

confidence: 99%

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Magotra

Malhotra

Dogra

2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…The state is referred to as the balance state of host resource utilization. Although it is difficult to predict accurately, studies ( Yu, Chen & Cai, 2016 ; Zheng, Li & Dong, 2016 ; Sharma & Saini, 2016 ) have shown that the resource utilization of VMs follows the Gaussian distribution. Accordingly, the Gaussian distribution is adopted in this paper to estimate the probability of resource utilization of VMs, to further calculate the probability of PMs being in the state.…”

Section: Adaptive Estimation Of the Probability Of Dynamic Workloadmentioning

confidence: 99%

Multi-resource collaborative optimization for adaptive virtual machine placement

Pan

2022

PeerJ Computer Science

View full text Add to dashboard Cite

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

show abstract

“…In an attempt to deal with the dynamic workload, Sharma and Saini [30] has suggested a median based adaptive threshold scheme. Though the scheme achieved reduction in the violations of SLA but the issue of energy consumption by the data center remained unattended.…”

Section: Migrationmentioning

confidence: 99%

An adaptive threshold policy for host overload detection in cloud data centre

2021

IJATEE

View full text Add to dashboard Cite

VM Consolidation for Cloud Data Center Using Median Based Threshold Approach

Cited by 24 publications

References 10 publications

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Multi-resource collaborative optimization for adaptive virtual machine placement

An adaptive threshold policy for host overload detection in cloud data centre

Contact Info

Product

Resources

About