Green cloud VM migration: Power use analysis

Aikema, David; Mirtchovski, Andrey; Kiddle, Cameron; Simmonds, Rob

doi:10.1109/igcc.2012.6322249

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…A live VM migration pattern guarantees continuous service provisioning to the hosted applications during the VM memory transfer process (Kapil et al, 2013;Shribman and Hudzia, 2013), whereas non-live VM migration (Kozuch and Satyanarayanan, 2002;Wang et al, 2010a) suspends application execution prior to memory image transfer (Aikema et al, 2012;Glazer and Tropper, 1993;Satyanarayanan, 2002, Kozuch et al, 2002;Milojičić et al, 2000). Live VM migration is classified into pre-copy or post-copy methods.…”

Section: Virtual Machine Migrationmentioning

confidence: 98%

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Ahmad

Gani

Hamid

et al. 2015

Journal of Network and Computer Applications

319

120

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Section: Virtual Machine Migrationmentioning

confidence: 98%

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Ahmad

Gani

Hamid

et al. 2015

Journal of Network and Computer Applications

319

120

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“…They gave detailed mathematical analysis but they never mention how the extra energy consumption impacts on the overall data center energy consumption. Aikema et al [4] compared how extra energy consumption of live migration changes depending on workload type and transport type. They concluded live migration is "not always be advisable", but did not evaluate how much impact migrations have on the overall energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Impact of Live Migration on Data Center Energy Saving

Akiyama

Hirofuchi

Honiden

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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Energy efficiency of cloud data centers is of great concern today and has been tackled by many researchers. Dynamic VM placement is a well-known strategy to improve energy efficiency of a data center. Virtual machines (VMs) under light load are consolidated into a small number of physical machines (PMs) to turn idle PMs into low-power states. Although live migration is essential for dynamic VM placement, former studies have not yet revealed how energy overhead of live migration has impact on energy efficiency of dynamic VM placement. To tackle this problem, we conducted integrated simulation of energy overhead of live migration and dynamic VM placement using SimGrid. We used three dynamic VM placement policies and two live migration mechanisms (existing pre-copy and an accelerated mechanism invented by us) to thoroughly evaluate the energy overhead. The results showed that in the worst case energy overhead of live migration occupies 5.8% of total energy consumption of a data center.

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“…The overall efficiency is optimized due to the fact that VM resource utilizations are effectively managed across all PMs. Nevertheless, a possible drawback of all above approaches is that they fail to consider the effort and energy consumed while performing VM migration in such large scales, such as the overall time and total power usage of those large volumes of VM migrations [11].…”

Section: Related Workmentioning

confidence: 99%

TARGO: Transition and Reallocation Based Green Optimization for Cloud VMs

Fang

Liu

et al. 2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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-Much research has been conducted focusing on improving resource utilization efficiency in data centers in the context of Green Cloud Computing (GCC). While virtualization enables better resource provision and utilization for various computational resources, different approaches are proposed based on virtual machine (VM) optimizations using either server or workload consolidation techniques. Nonetheless, these solutions can only be applied inside the Cloud. In fact, Infrastructure-as-a-Service (IaaS) users can hardly proactively achieve better VM resource utilization efficiency, as they typically have no control over any hypervisor or hardware in any Clouds. The issue gets more critical when workloads on VMs alter dramatically from time to time. This paper presents a novel approach namely Transition and Reallocation Based Green Optimization (TARGO) for such users. Through fully automated and intelligent VM optimization according to customizable optimization rules, TARGO guarantees that VMs or their successors being optimized will always run at their customizable green optimal states regardless how workloads vary. Experiments conducted on Amazon EC2 instances in the EU region show that, even under extreme random workloads, TARGO is still capable of selecting and retaining VM successors which run at an average CPU utilization of 50%-60%.

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Green cloud VM migration: Power use analysis

Cited by 14 publications

References 17 publications

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Evaluating Impact of Live Migration on Data Center Energy Saving

TARGO: Transition and Reallocation Based Green Optimization for Cloud VMs

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