Scatter-Gather Live Migration of Virtual Machines

Deshpande, Umesh; Chan, Danny; Chan, Steven M.; Gopalan, Kartik; Bila, Nilton

doi:10.1109/tcc.2015.2481424

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…VM migration is one of the ways to manage numerous VMs running in cloud centers. In the VM migration method, the VM is migrated to another node when the resource usage of the physical server exceeds a threshold to minimize the performance degradation and resource contention of the VMs . Besides GPU resources, there are various technologies that allow the VM to be paused and reexecuted as soon as it is migrated, for example, context switching, dirty pages, buffer copying.…”

Section: Related Workmentioning

confidence: 99%

Partial migration technique for GPGPU tasks to Prevent GPU Memory Starvation in RPC‐based GPU Virtualization

Kang

Lim

2020

Softw Pract Exp

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Graphics processing unit (GPU) virtualization technology enables a single GPU to be shared among multiple virtual machines (VMs), thereby allowing multiple VMs to perform GPU operations simultaneously with a single GPU. Because GPUs exhibit lower resource scalability than central processing units (CPUs), memory, and storage, many VMs encounter resource shortages while running GPU operations concurrently, implying that the VM performing the GPU operation must wait to use the GPU. In this paper, we propose a partial migration technique for general-purpose graphics processing unit (GPGPU) tasks to prevent the GPU resource shortage in a remote procedure call-based GPU virtualization environment. The proposed method allows a GPGPU task to be migrated to another physical server's GPU based on the available resources of the target's GPU device, thereby reducing the wait time of the VM to use the GPU. With this approach, we prevent resource shortages and minimize performance degradation for GPGPU operations running on multiple VMs. Our proposed method can prevent GPU memory shortage, improve GPGPU task performance by up to 14%, and improve GPU computational performance by up to 82%. In addition, experiments show that the migration of GPGPU tasks minimizes the impact on other VMs.

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Section: Related Workmentioning

confidence: 99%

Partial migration technique for GPGPU tasks to Prevent GPU Memory Starvation in RPC‐based GPU Virtualization

Kang

Lim

2020

Softw Pract Exp

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“…Slowing the operations of VMs will, of course, subsequently decrease the performance of the applications hosted by them. In [12], Deshpande et al introduce a novel non-standard migration method called scatter-gather VM migration. The migration is done via an intermediate node.…”

Section: Related Workmentioning

confidence: 99%

Affinity Aware-Scheduling of Live Migration of Virtual Machines Under Maintenance Scenarios

Yazidi

Ung

Haugerud

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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During maintenance and disaster recovery scenarios, Virtual Machine (VM) inter-site migrations usually take place over limited bandwidth-typically Wide Area Network (WAN)which is highly affected by the amount of inter-VM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of intercommunicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise an affinity-based scheduling algorithm for controlling the order of the migration groups by considering their inter-group traffic. Comprehensive real-life experimental results and simulations show that our approach in some cases is able to decrease the volume of separated traffic by a factor larger than 60%.

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“…In [4], Deshpande et al introduce a novel non-standard migration method called scatter-gather VM migration. The migration is done via an intermediate node.…”

Section: Related Workmentioning

confidence: 99%

Minimum-Impact First

Yazidi

Haugerud

Ung

et al. 2019

Proceedings of the 11th International Conference on Management of Digital EcoSystems

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Virtual Machine (VM) migration is an important feature for ensuring smooth operations during maintenance and disaster recovery scenarios. The migration might be inter-site and in such a case the inter-site bandwidth which is typically Wide Area Network (WAN) might be a bottleneck. In such a case, the bandwidth is affected by the amount of inter-VM traffic that becomes separated during the migration process. The amount of separated traffic might not only cause degradation of the of the Quality of Service (QoS) of inter-communicating VMs but can also delay the migration process due to the congestion of the migration link. The state-of-the-art algorithm due to Yazidi et al. is an affinity aware algorithm that does not consider the completion time of the migration. The first stage of our algorithm is identical to Yazidi et al. where we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise a greedy algorithm for controlling the order of the migration groups by considering their inter-group traffic that greedily selects groups with the lowest impact in terms of volume of separated traffic which we denominate Minimum-Impact First (MIF). We also design a latency-aware algorithm that only schedules the quickest migration first. The latter simple heuristic interestingly outperforms legacy works in the case of migration over a non-dedicated link. We find that our MIF algorithm consistently outperforms the state-of-the-art algorithms by a clear margin using real-traffic traces by a margin larger than 40%. We show that the MIF algorithm ensures the lowest amount of separated traffic in both dedicated-link and non-dedicated-link scenarios.

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Scatter-Gather Live Migration of Virtual Machines

Cited by 23 publications

References 27 publications

Partial migration technique for GPGPU tasks to Prevent GPU Memory Starvation in RPC‐based GPU Virtualization

Partial migration technique for GPGPU tasks to Prevent GPU Memory Starvation in RPC‐based GPU Virtualization

Affinity Aware-Scheduling of Live Migration of Virtual Machines Under Maintenance Scenarios

Minimum-Impact First

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