energy-efficient virtual machines placement - sbrc2014

Energy-Efficient VMs

Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work

ThepyCloudSimFramework

Experiments

Results andConclusions

References

Energy-Efficient Virtual Machines Placement

Albert De La Fuente VigliottiDaniel Macedo Batista

Department of Computer ScienceUniversity of Sao Paulo

albert at ime.usp.br

http://www.ime.usp.br/~albert — http://www.albertdelafuente.com

May 6th, 2014

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mailto:[email protected]

http://www.ime.usp.br/~albert

http://www.albertdelafuente.com


Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The problem

The current IT infrastructure contributes about 2% oftotal world wide power consumption and CO2 footprints[1].

This corresponds to the typical yearly electricityconsumption of 120 million households [1].

An energy consumption rise of 16-20% per year can beobserved in the last years on data centers and large-scalecomputing infrastructures, corresponding to a doublingevery 4-5 years [2].

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The objective

Question:

Is it possible to reduce the amount of consumed energy in adata center by using virtualization?

Question:

Is there reduction of energy consumption when keeping a samenumber of virtual machines in a lower number of physicalmachines?

Our approaches:

A Knapsack based algorithmAn Evolutionary Computation (EC) based algorithm

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Motivation

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Related work

CV Xavier et al. [3] analyzed performance, but they focusedonly on high performance computing environments (HPC).

CV Mehnert et al. [4] focused on memory incrementalcheckpointing (related on the EU-funded projectXtreemOS).

HV Beloglazov et al. [5] created OpenStack Neat which is anopen source software framework for distributed dynamicVM consolidation in cloud data centers based on theOpenStack platform.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Related work: CloudSim

HV Calheiros et al. [6] created a simulation toolkit calledCloudSim It abstracts the low level details related toCloud-based infrastructures and services, allowing to focuson specific system design. It supports modeling andsimulation of:

Large scale Cloud computing data centersVirtualized server hosts, with customizable policies forprovisioning host resources to virtual machinesEnergy-aware computational resourcesData center network topologies and message-passingapplicationsFederated clouds

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The core of the simulation framework ofpyCloudSim

The main algorithm of pyCloudSim 1 iterates over the available(unplaced) physical hosts and VMs to determine a placementusing a given strategy S.

1https://github.com/vonpupp/sbrc-2014-simulation

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https://github.com/vonpupp/sbrc-2014-simulation


Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The Knapsack (KSP) based strategy

A list of constraints is built for each resource, this includesassigning a weight on each VM which will be the criteria to bemaximized by the algorithm, equivalent to maximize thenumber of VMs.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The Evolutionary Computation (EC) based strategy

G generates possible solutions with 1% of chance to include aVM in a host. The evaluation function E calculates the fittingof the proposed solution. We used a population size of 50, atournament size of 25 and 2500 evaluations.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The evaluation function (a valid solution)

number of VMs = 4

[ 70 70 60 60 ]-100

[ -30 -30 -40 -40 ]max(0, [ -30 -30 -40 -40 ]

[ 0 0 0 0 ]sum([ 0 0 0 0 ]

0

4 - 0 = 4

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The evaluation function (an invalid solution)

number of VMs = 4

[ 130 70 60 60 ]-100

[ +30 -30 -40 -40 ]max(0, [ +30 -30 -40 -40 ]

[ +30 0 0 0 ]sum([ +30 0 0 0 ]

+30

4 - 30 = -26

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The trace analysis

We analyzed more than 11,776 real traces (24-hour long each)from the PlanetLab project. The Standard deviation range wasfrom 0.2634 to 43.5875, and the mean range was from 0.5173to 95.9756. These values represents percentage of use.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

The methodology

A simulation is made of:

trace-scenario

algorithm-scenario [Energy Unaware, Iterated-KSP,Iterated-EC]

physical machine-scenario [10, 100], increments by 10

VMs varying on the interval [16, 288], increments by 16

We repeated each simulation 30 times to check if there was aclear tendency, and later reduced the data to three cases:

best case

worst case

average case

The experiment took ∼180h (more than one week).

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Power consumption comparison - Trace 1 (15.5204,25.0694)

Figure : Power consumption [100 hosts / Trace 1]

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References



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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Conclusions - Power consumption

Iterated-EC had power savings starting from 35.46% for aworkload of 288 VMs and up to 92.20% for a workload of16 VMs with 200 hosts.

The Iterated-KSP had power savings starting from40.33% for a workload of 288 VMs and up to 92.21%for a workload of 16 VMs.

We noticed that Iterated-KSP is 7.55% better than theIterated-EC (average case) which can be translated into adifference of 1.66 KW.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References



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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Conclusions - Hardware usage

The Iterated-KSP optimizes hardware by 6.20% to20.40% however it is not stable.

Iterated-EC ranges from 7.49% to 13.14% with a trendto be stable ≈11%.

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Suspended physical hosts comparison - Trace 2(15.9337, 34.7465)

Figure : Suspended physical hosts [100 hosts / Trace 2]

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Execution time comparison - Trace 3 (15.1083,44.7083)

Figure : Suspended physical hosts [100 hosts / Trace 3]

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Conclusions - Time

The Iterated-KSP is 11% to 15% faster thanIterated-EC. The execution time difference tend toincrease with the number of hosts and VMs at a rate of≈5 seconds per 100 hosts.

Iterated-EC is easier to be run in parallel thanIterated-KSP

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Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

Thanks

github.com/vonpupp/sbrc-2014-simulation

albert at ime.usp.br



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github.com/vonpupp/sbrc-2014-simulation

mailto:[email protected]




Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

References I

[1] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and surveyof energy-efficient data centers and cloud computing systems,” arXiv e-print1007.0066, Jul. 2010. [Online]. Available: http://arxiv.org/abs/1007.0066

[2] Rich Brown, “Report to congress on server and data center energyefficiency:Public law 109-431,” 2007. [Online]. Available:http://www.energystar.gov/ia/partners/prod development/downloads/EPA Datacenter Report Congress Final1.pdf

[3] M. Xavier, M. Neves, F. Rossi, T. Ferreto, T. Lange, and C. De Rose,“Performance evaluation of container-based virtualization for highperformance computing environments,” in 2013 21st Euromicro InternationalConference on Parallel, Distributed and Network-Based Processing (PDP),2013, pp. 233–240.

[4] J. Mehnert-Spahn, E. Feller, and M. Schoettner, “Incremental checkpointingfor grids,” in Linux Symposium, vol. 120, 2009. [Online]. Available:https://www.kernel.org/doc/ols/2009/ols2009-pages-201-208.pdf

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http://arxiv.org/abs/1007.0066

http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf

http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf

https://www.kernel.org/doc/ols/2009/ols2009-pages-201-208.pdf


Placement

Albert De LaFuente

VigliottiDaniel

MacedoBatista

The Problem

The objective

Motivation

Related Work


Experiments


References

References II

[5] A. Beloglazov and R. Buyya, “OpenStack neat: A framework for dynamicconsolidation of virtual machines in OpenStack clouds–A blueprint,”Technical Report CLOUDS-TR-2012-4, Cloud Computing and DistributedSystems Laboratory, The University of Melbourne, Tech. Rep., 2012. [Online].Available:http://www.cloudbus.org/reports/OpenStack-neat-Blueprint-Aug2012.pdf

[6] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya,“CloudSim: a toolkit for modeling and simulation of cloud computingenvironments and evaluation of resource provisioning algorithms,” Software:Practice and Experience, vol. 41, no. 1, pp. 23–50, Jan. 2011. [Online].Available: http://onlinelibrary.wiley.com/doi/10.1002/spe.995/abstract

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http://www.cloudbus.org/reports/OpenStack-neat-Blueprint-Aug2012.pdf

http://onlinelibrary.wiley.com/doi/10.1002/spe.995/abstract

energy-efficient virtual machines placement - sbrc2014

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