Migration energy-aware workload consolidation in enterprise clouds

Hossain, Mohammad Motaher; Huang, Jen-Cheng; Lee, Hsien-Hsin S.

doi:10.1109/cloudcom.2012.6427570

Cited by 18 publications

(8 citation statements)

References 6 publications

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“…In [34] authors discussed the consolidation through live VM migrations that takes into account both performance and energy costs. In [21] combination of consolidation (on low power or high performance nodes) is combined with switching unused nodes off is discussed.…”

Section: Workload and Resource Management Policies -Related Workmentioning

confidence: 99%

Energy and thermal models for simulation of workload and resource management in computing systems

Piątek

Oleksiak

Costa

2015

Simulation Modelling Practice and Theory

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Section: Workload and Resource Management Policies -Related Workmentioning

confidence: 99%

Energy and thermal models for simulation of workload and resource management in computing systems

Piątek

Oleksiak

Costa

2015

Simulation Modelling Practice and Theory

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“…The migration cost is depicted as the throughput of the migration. Hossain et al [29] also tried to minimize the total energy consumption by utilizing the VM placement, but they modeled the migration cost as the migration energy consumption at the destination and source servers.…”

Section: Related Workmentioning

confidence: 99%

PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Sun

Ansari

2016

2016 IEEE International Conference on Communications (ICC)

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We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, with lower end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations.Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.

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“…Their findings include: (1) Both the source and the destination PMs undergo large increase of power as shown in [2] and [3]. (2) Not only CPU load but also memory and network load contributes to extra energy consumption because they occupy non-negligible parts of energy consumption within a server [6].…”

Section: B Energy Consumption Of Live Migrationmentioning

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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Migration energy-aware workload consolidation in enterprise clouds

Cited by 18 publications

References 6 publications

Energy and thermal models for simulation of workload and resource management in computing systems

Energy and thermal models for simulation of workload and resource management in computing systems

PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Evaluating Impact of Live Migration on Data Center Energy Saving

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