Performance Metrics of Virtual Machine Live Migration

Galloway, Michael; Loewen, Gabriel; Vrbsky, Susan V.

doi:10.1109/cloud.2015.90

Cited by 9 publications

(5 citation statements)

References 9 publications

(12 reference statements)

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“…Rather, they assume that, as long as VMs obtain the resources they request, SLOs will be satisfied. Migration overhead. Although live migration minimizes the period in which the VM is unresponsive to the sub‐second range, the migration takes actually significantly longer and incurs non‐negligible overhead during this time in terms of both network traffic and additional PM load, also resulting in extra energy consumption . Therefore, a good VM placement optimization algorithm has to minimize the number of migrations, ie, the number of VMs whose placement must be changed.…”

Section: Introductionmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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Summary One of the key problems for Infrastructure‐as‐a‐Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real‐world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

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Section: Introductionmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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“…The chosen key performance indicators are the Migration time, Downtime, UE service recovery time, and Migration data size [47]- [49]. These performance indicators are evaluated while migrating the CN components and keeping one UE connected.…”

Section: B Performance Indicatorsmentioning

confidence: 99%

Live Migration of Virtual Machine and Container Based Mobile Core Network Components: A Comprehensive Study

et al. 2021

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With the increasing demand for openness, flexibility, and monetization, the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) -the core network (CN) component of 4G LTE and 5G non-standalone mobile networks -reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration of these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. This paper's first contribution is the proposal of a framework that enables migration of containerised virtual EPC components using an open-source migration solution which does not fully support the mobile network protocol stack yet. The second contribution is an experimental-based comprehensive analysis of live migration in two virtualization technologies -VM and Container -with the additional scrutinization on the container migration approach. The presented experimental comparison accounts for several system parameters and configurations: flavor (image) size, network characteristics, processor hardware architecture model, and the CPU load of the backhaul network components. The comparison reveals that the live migration completion time and also the end-user service interruption time of the virtualized EPC components is reduced approximately by 70% in the container platform when using the proposed framework.

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“…Public cloud computing: It mainly depends on third individual to suggest services by paying them on regular basis according to the procedure. Public Cloud environment is made available to all unrestricted consumers who can subscribe the needed services [3].…”

Section: A Deployment Modelmentioning

confidence: 99%

An Enhanced Load Balancing Mechanism in Cloud computing

Kumar¹,

Luthra²,

Singh³

2017

International Journal of Advanced Research in Computer and Comm

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Cloud computing is the means of accessing a shared pool of configurable computing resources (including hardware, software, networks, servers, storage applications and services) that can be rapidly provided, used and released with minimal effort on the part of users or service providers. But it has some of the main concerns like load management and fault tolerance. In this paper we have put forward a load balancing approach in cloud computing. Load balancing is helped to distribute the workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improves the performance of the system.

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Performance Metrics of Virtual Machine Live Migration

Cited by 9 publications

References 9 publications

Which is the best algorithm for virtual machine placement optimization?

Which is the best algorithm for virtual machine placement optimization?

Live Migration of Virtual Machine and Container Based Mobile Core Network Components: A Comprehensive Study

An Enhanced Load Balancing Mechanism in Cloud computing

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