Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆ edo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Energy-Efficient Virtual Machines Placement Albert De La Fuente Vigliotti Daniel Macˆ edo Batista Department of Computer Science University of S˜ ao Paulo albert at ime.usp.br http://www.ime.usp.br/ ~ albert — http://www.albertdelafuente.com May 6th, 2014 1 / 24
My presentation at the 32nd Brazilian Symposium on Computer Networks and Distributed Systems. Held at Florianopolis - Brazil, May 5-9, 2014
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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
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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Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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
3 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
References
Motivation
4 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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.
5 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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
6 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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.
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.
8 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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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Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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
10 / 24
Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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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Energy-Efficient VMs
Placement
Albert De LaFuente
VigliottiDaniel
MacedoBatista
The Problem
The objective
Motivation
Related Work
ThepyCloudSimFramework
Experiments
Results andConclusions
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.
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
[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
[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