![Energy-
Efficient VMs
Placement
Albert De La
Fuente
Vigliotti
Daniel
Macˆedo
Batista
The Problem
The objective
Motivation
Related Work
The
pyCloudSim
Framework
Experiments
Results and
Conclusions
References
The problem
The current IT infrastructure contributes about 2% of
total world wide power consumption and CO2 footprints
[1].
This corresponds to the typical yearly electricity
consumption of 120 million households [1].
An energy consumption rise of 16-20% per year can be
observed in the last years on data centers and large-scale
computing infrastructures, corresponding to a doubling
every 4-5 years [2].
2 / 24](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Energy-Efficient Virtual Machines Placement - SBRC2014
- 1. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo 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 Macˆedo 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
- 2. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The problem The current IT infrastructure contributes about 2% of total world wide power consumption and CO2 footprints [1]. This corresponds to the typical yearly electricity consumption of 120 million households [1]. An energy consumption rise of 16-20% per year can be observed in the last years on data centers and large-scale computing infrastructures, corresponding to a doubling every 4-5 years [2]. 2 / 24
- 3. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The objective Question: Is it possible to reduce the amount of consumed energy in a data center by using virtualization? Question: Is there reduction of energy consumption when keeping a same number of virtual machines in a lower number of physical machines? Our approaches: A Knapsack based algorithm An Evolutionary Computation (EC) based algorithm 3 / 24
- 4. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Motivation 4 / 24
- 5. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Related work CV Xavier et al. [3] analyzed performance, but they focused only on high performance computing environments (HPC). CV Mehnert et al. [4] focused on memory incremental checkpointing (related on the EU-funded project XtreemOS). HV Beloglazov et al. [5] created OpenStack Neat which is an open source software framework for distributed dynamic VM consolidation in cloud data centers based on the OpenStack platform. 5 / 24
- 6. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Related work: CloudSim HV Calheiros et al. [6] created a simulation toolkit called CloudSim It abstracts the low level details related to Cloud-based infrastructures and services, allowing to focus on specific system design. It supports modeling and simulation of: Large scale Cloud computing data centers Virtualized server hosts, with customizable policies for provisioning host resources to virtual machines Energy-aware computational resources Data center network topologies and message-passing applications Federated clouds 6 / 24
- 7. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The core of the simulation framework of pyCloudSim The main algorithm of pyCloudSim 1 iterates over the available (unplaced) physical hosts and VMs to determine a placement using a given strategy S. 1 https://github.com/vonpupp/sbrc-2014-simulation 7 / 24
- 8. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The Knapsack (KSP) based strategy A list of constraints is built for each resource, this includes assigning a weight on each VM which will be the criteria to be maximized by the algorithm, equivalent to maximize the number of VMs. 8 / 24
- 9. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The Evolutionary Computation (EC) based strategy G generates possible solutions with 1% of chance to include a VM in a host. The evaluation function E calculates the fitting of the proposed solution. We used a population size of 50, a tournament size of 25 and 2500 evaluations. 9 / 24
- 10. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions 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
- 11. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions 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 11 / 24
- 12. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References The trace analysis We analyzed more than 11,776 real traces (24-hour long each) from the PlanetLab project. The Standard deviation range was from 0.2634 to 43.5875, and the mean range was from 0.5173 to 95.9756. These values represents percentage of use. 12 / 24
- 13. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions 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 a clear tendency, and later reduced the data to three cases: best case worst case average case The experiment took ∼180h (more than one week). 13 / 24
- 14. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Power consumption comparison - Trace 1 (15.5204, 25.0694) Figure : Power consumption [100 hosts / Trace 1] 14 / 24
- 15. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Power consumption comparison - Trace 1 (15.5204, 25.0694) Figure : Power consumption [200 hosts / Trace 1] 15 / 24
- 16. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Conclusions - Power consumption Iterated-EC had power savings starting from 35.46% for a workload of 288 VMs and up to 92.20% for a workload of 16 VMs with 200 hosts. The Iterated-KSP had power savings starting from 40.33% 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 the Iterated-EC (average case) which can be translated into a difference of 1.66 KW. 16 / 24
- 17. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Power consumption comparison - Trace 2 (15.9337, 34.7465) Figure : Power consumption [100 hosts / Trace 2] 17 / 24
- 18. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Conclusions - Hardware usage The Iterated-KSP optimizes hardware by 6.20% to 20.40% however it is not stable. Iterated-EC ranges from 7.49% to 13.14% with a trend to be stable ≈11%. 18 / 24
- 19. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Suspended physical hosts comparison - Trace 2 (15.9337, 34.7465) Figure : Suspended physical hosts [100 hosts / Trace 2] 19 / 24
- 20. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Execution time comparison - Trace 3 (15.1083, 44.7083) Figure : Suspended physical hosts [100 hosts / Trace 3] 20 / 24
- 21. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Conclusions - Time The Iterated-KSP is 11% to 15% faster than Iterated-EC. The execution time difference tend to increase 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 than Iterated-KSP 21 / 24
- 22. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References Thanks github.com/vonpupp/sbrc-2014-simulation albert at ime.usp.br http://www.ime.usp.br/~albert http://www.albertdelafuente.com 22 / 24
- 23. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References References I [1] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and survey of energy-efficient data centers and cloud computing systems,” arXiv e-print 1007.0066, Jul. 2010. [Online]. Available: http://arxiv.org/abs/1007.0066 [2] Rich Brown, “Report to congress on server and data center energy efficiency: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 high performance computing environments,” in 2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2013, pp. 233–240. [4] J. Mehnert-Spahn, E. Feller, and M. Schoettner, “Incremental checkpointing for grids,” in Linux Symposium, vol. 120, 2009. [Online]. Available: https://www.kernel.org/doc/ols/2009/ols2009-pages-201-208.pdf 23 / 24
- 24. Energy- Efficient VMs Placement Albert De La Fuente Vigliotti Daniel Macˆedo Batista The Problem The objective Motivation Related Work The pyCloudSim Framework Experiments Results and Conclusions References References II [5] A. Beloglazov and R. Buyya, “OpenStack neat: A framework for dynamic consolidation of virtual machines in OpenStack clouds–A blueprint,” Technical Report CLOUDS-TR-2012-4, Cloud Computing and Distributed Systems 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 computing environments 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 24 / 24