A survey of live Virtual Machine migration techniques

Le, Tuan Thanh

doi:10.1016/j.cosrev.2020.100304

Cited by 29 publications

(14 citation statements)

References 39 publications

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“…Migrate VM i to the server with higher number of N(0) 10: else if (VM i status is 1 and VM i = VM_TBM then: Migrate VM i to the S RCS 12: end if 13: end for 14: End Energy Consumption: Energy Consumption (EC) is defined as the power consumption at time t which is expressed as follows in Eqs. (11) and (12),…”

Section: Migration Metricsmentioning

confidence: 99%

“…Live VM migration approach provides an energy efficient cloud environment by integrating normal loads of multiple servers on a few physical machines and put in an energy saving mode for the idle servers [9,10]. While performing live VM migration, CDC workload parameters such as CPU, Disk and memory utilization plays a vital role and envisaging those parameters for the upcoming workload, still remains an open challenge [11], as the CDC workload parameters are highly dynamic and non-linear in nature [12,13]. Thus, determining the workload parameters for live VM migration can be viewed as a prediction problem as it predicts the upcoming workload parameters in advance [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Subramanian¹,

Sriram²

2023

Intelligent Automation &Amp; Soft Computing

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Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers' (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory (CDB-LSTM) model is proposed. The CDB-LSTM adopts Helly property of Hypergraph and Savitzky-Golay (SG) filter to select informative samples and exclude noisy inference & outliers. The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computational complexity during live VM migration. Further, the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration. A Hypergraph based CDB-LSTM was validated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

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Section: Migration Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Subramanian¹,

Sriram²

2023

Intelligent Automation &Amp; Soft Computing

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show abstract

“…The difference between dynamic server provisioning and workload dispatching is that the former turns off some unnecessary servers, whereas the latter just simply adjusts server utilization. Besides workload dispatching, workload can also be migrated from one server to another at a low cost using the live VM migration techniques [102] in a virtualized environment. A running task can be migrated to another server using such techniques in the event of a thermal emergency or in the pursuit of total energy reduction.…”

Section: A Enabling Techniquesmentioning

confidence: 99%

Toward a Systematic Survey for Carbon Neutral Data Centers

Cao

Zhou

et al. 2022

IEEE Commun. Surv. Tutorials

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“…In [5] a detailed comparison between pre-copy and post-cop migration is presented. The comparison in [5] shows that pre-copy technique is a more safe and reliable and so it is the commonly used technique by the hypervisors Xen, VMware, Microsoft Hyper-V, Oracle VM server, KVM, Virtuozzo, OVirt and Google Compute Engine. Post-copy migration is used also in KVM, Virtuozzo, OVirt and Google Compute Engine.…”

Section: Live Migration Typesmentioning

confidence: 99%

Virtual machines pre-copy live migration cost modeling and prediction: a survey

Elsaid

Abbas

Meinel

2021

Distrib Parallel Databases

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Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.

show abstract

A survey of live Virtual Machine migration techniques

Cited by 29 publications

References 39 publications

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Toward a Systematic Survey for Carbon Neutral Data Centers

Virtual machines pre-copy live migration cost modeling and prediction: a survey

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