Preemption-Aware Energy Management in Virtualized Data Centers

Salehi, Mohsen Amini; Krishna, P. Radha; Deepak, Krishnamurty Sai; Buyya, Rajkumar

doi:10.1109/cloud.2012.147

Cited by 20 publications

(14 citation statements)

References 13 publications

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“…Considered VM set. Most works consider the placement of all VMs in the DC at once , . However, some also consider the placement of a single VM or a set of VMs belonging to the same application …”

Section: Related Workmentioning

confidence: 99%

“…New placement vs re‐optimization. For the given VM set (whether it is the set of all VMs of the DC, a single VM, or the VMs of an application), there are 2 different problem variants: Either the initial placement of the VMs needs to be determined or their existing placement is to be re‐optimized to adapt it to the changed resource requirements of the workload , . The 2 problems are similar; the main difference is that re‐optimization has to take into account the cost of migrating a VM from its old host to a new one, whereas this is no concern in the case of initial placement. Objective(s).…”

Section: Related Workmentioning

confidence: 99%

“…Some authors proposed proprietary heuristics. Some of them are simple greedy algorithms or straight‐forward selection policies, others are rather complex …”

Section: Related Workmentioning

confidence: 99%

“…Most works consider the placement of all VMs in the DC at once. 2,20,25,26,31,36,[40][41][42][43][44] However, some also consider the placement of a single VM 11,45 or a set of VMs belonging to the same application. 24,46,47 • New placement vs re-optimization.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Some authors proposed proprietary heuristics. Some of them are simple greedy algorithms or straight‐forward selection policies, others are rather complex …”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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“…Migrating virtual machine instances between physical servers may result in unacceptable service unavailability. Though explicitly allowing to preempt certain virtual machines for the sake of energy efficiency may be an option [10].…”

Section: B Schedulingmentioning

confidence: 99%

Energy-aware scheduling for infrastructure clouds

Knauth

Fetzer

2012

4th IEEE International Conference on Cloud Computing Technology and Science Proceedings

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More and more data centers are built, consuming ever more kilo watts of energy. Over the years, energy has become a dominant cost factor for data center operators. Utilizing lowpower idle modes is an immediate remedy to reduce data center power consumption. We use simulation to quantify the difference in energy consumption caused exclusively by virtual machine schedulers. Besides demonstrating the inefficiency of wide-spread default schedulers, we present our own optimized scheduler. Using a range of realistic simulation scenarios, our customized scheduler OptSched reduces cumulative machine uptime by up to 60.1%. We evaluate the effect of data center composition, run time distribution, virtual machine sizes, and batch requests on cumulative machine uptime. IaaS administrators can use our results to quickly assess possible reductions in machine uptime and, hence, untapped energy saving potential.

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Resource provisioning based on preempting virtual machines in distributed systems

Salehi

Javadi

Buyya

2013

Concurrency and Computation

Self Cite

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SUMMARYResource provisioning is one of the main challenges in large‐scale distributed systems such as federated Grids. Recently, many resource management systems in these environments have started to use the lease abstraction and virtual machines (VMs) for resource provisioning. In the large‐scale distributed systems, resource providers serve requests from external users along with their own local users. The problem arises when there is not sufficient resources for local users, who have higher priority than external ones, and need resources urgently. This problem could be solved by preempting VM‐based leases from external users and allocating them to the local ones. However, preempting VM‐based leases entails side effects in terms of overhead time as well as increasing makespan of external requests. In this paper, we model the overhead of preempting VMs. Then, to reduce the impact of these side effects, we propose and compare several policies that determine the proper set of lease(s) for preemption. We evaluate the proposed policies through simulation as well as real experimentation in the context of InterGrid under different working conditions. Evaluation results demonstrate that the proposed preemption policies serve up to 72% more local requests without increasing the rejection ratio of external requests. Copyright © 2013 John Wiley & Sons, Ltd.

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Preemption-Aware Energy Management in Virtualized Data Centers

Cited by 20 publications

References 13 publications

Which is the best algorithm for virtual machine placement optimization?

Which is the best algorithm for virtual machine placement optimization?

Energy-aware scheduling for infrastructure clouds

Resource provisioning based on preempting virtual machines in distributed systems

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