Energy Efficient Allocation of Virtual Machines in Cloud Data Centers

Beloglazov, Anton; Buyya, Rajkumar

doi:10.1109/ccgrid.2010.45

Cited by 388 publications

(320 citation statements)

References 2 publications

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“…SPC can be reduced by switching idle resources to sleep mode [2]. However, dynamic power consumption (DPC), P dynamic , is due to circuit activity and it depends on resource utilization.…”

Section: Power Modelmentioning

confidence: 99%

A Bio-inspired Approach for Power and Performance Aware Resource Allocation in Clouds

Kumar

Sharma

2016

MATEC Web of Conferences

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Abstract. In order to cope with increasing demand, cloud market players such as Amazon, Microsoft, Google, Gogrid, Flexiant, etc. have set up large sized data centers. Due to monotonically increasing size of data centers and heterogeneity of resources have made resource allocation a challenging task. A large percentage of total energy consumption of the data centers gets wasted because of under-utilization of resources. Thus, there is a need of resource allocation technique that improves the utilization of resources with effecting performance of services being delivered to end users. In this work, a bio-inspired resource allocation approach is proposed with the aim to improve utilization and hence the energy efficiency of the cloud infrastructure. The proposed approach makes use of Cuckoo search for power and performance aware allocation of resources to the services hired by the end users. The proposed approach is implemented in CloudSim. The simulation results have shown approximately 12% saving in energy consumption.

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“…SPC can be reduced by switching idle resources to sleep mode [2]. However, dynamic power consumption (DPC), P dynamic , is due to circuit activity and it depends on resource utilization.…”

Section: Power Modelmentioning

confidence: 99%

A Bio-inspired Approach for Power and Performance Aware Resource Allocation in Clouds

Kumar

Sharma

2016

MATEC Web of Conferences

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“…The green Cloud architecture proposed here, it is a power model that divides the energy level consumption in an idle server and running a server with the CPU utilization controlled by DVFS for different frequency and voltage utilization. This model proposes VM allocation divided in two parts: the first part performs VM provisioning and placing the VMs on hosts like a bin packing problem with different bin sizes and prices, and the second part performs the optimization of the current VM allocation similarly to [15] the authors use a modification of the Best Fit Decreasing (MBFD) algorithm [16]. To optimize the current VM allocation first selects VMs that need to be migrated, then the chosen VMs are placed on the hosts using an MBFD algorithm.…”

Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

175

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h i g h l i g h t s• Energy-aware run-time scheduler for task-based applications.• Model for estimating the application Energy consumption.• Methodology to automatically generate the required power consumption profile.• Multi-heuristic resource allocation algorithm to get solutions in polynomial time.• Energy saving/performance trade-off evaluation for different scenarios. a r t i c l e i n f o b s t r a c tGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently taskbased applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

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“…Beloglazov et al demonstrated with substantial empirical evidence that MM and MBFD perform well in practice and outperform other competing heuristics [5,6]. In this paper, our aim is to investigate whether any performance guarantees can be established rigorously for these heuristics, either in terms of optimality or approximation ratio.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

“…In their recent works, Beloglazov, Buyya and Abawajy proposed a combination of two heuristics for near-optimal VM consolidation [5,6]. The first heuristic, called MM (Minimization of Migrations), selects the VMs that should be migrated from a given host.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Mann

2015

Future Generation Computer Systems

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Dynamic consolidation of virtual machines (VMs) in a cloud data center can be used to minimize power consumption. Beloglazov et al. have proposed the MM (Minimization of Migrations) heuristic for selecting the VMs to migrate from under-or over-utilized hosts, as well as the MBFD (Modified Best Fit Decreasing) heuristic for deciding the placement of the migrated VMs. According to their simulation results, these heuristics work very well in practice. In this paper, we investigate what performance guarantees can be rigorously proven for the heuristics. In particular, we establish that MM is optimal with respect to the number of selected VMs of an over-utilized host and it is a 1.5-approximation with respect to the decrease in utilization. On the other hand, we show that the result of MBFD can be arbitrarily far from the optimum. Moreover, we show that even if both MM and MBFD deliver optimal results, their combination does not necessarily result in optimal VM consolidation, but approximation results can be proven under suitable technical conditions. To the best of our knowledge, these are the first rigorously proven results on the effectiveness of also practically useful heuristic algorithms for the VM consolidation problem.

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Energy Efficient Allocation of Virtual Machines in Cloud Data Centers

Cited by 388 publications

References 2 publications

A Bio-inspired Approach for Power and Performance Aware Resource Allocation in Clouds

A Bio-inspired Approach for Power and Performance Aware Resource Allocation in Clouds

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

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