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Energy Usage in Cloud Part2

Energy Usage in Cloud Part2. Salih Safa BACANLI. Cooling Virtualization Energy Proportional System Conclusion. Cooling. After servers, the second largest consumer of power in a data center is the cooling system . (Kava, 2012)

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Energy Usage in Cloud Part2

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  1. Energy Usage in Cloud Part2 Salih Safa BACANLI

  2. Cooling • Virtualization • Energy Proportional System • Conclusion

  3. Cooling • After servers, the second largest consumer of power in a data center is the cooling system.(Kava, 2012) • This cloud-computing hardware must be cooled by something, regardless of the hardware’s location or owner.  • Requiredcooling infrastructure depends on the computing resources’ power density and layout.(Infotech,2011)

  4. Air cooling is popular, inexpensive,simple to scale to fit room needs. Fine for systems up to 15 KW • You need to move the air through the equipment (air’s heat transfer coefficient is nearly constant). • Ventilation air and natural winds OR • You need to cool the air in the cloud room.

  5. Raised Floor • The equipments (racks) are put on a platform higher than the ground. The area is used for air space or water cooling pipes.

  6. For systems who energy consumption is bigger than 15 KW/rack you need to use liquid. Movinglarge volumes of air can be very expensive for very-high-density racks. • Mineral oil and water can be used for liquid.

  7. Virtualization as solution? • Virtualization is nice if used properly. • Having many computers in the same hardware. • Scaling: • Horizontal: Increasing VM • Vertical:increase resource allocation of current VMs (increase their CPU share) (Paya & Marinescu, 2013)

  8. Disadvantageous Virtualization 1) The rise of high density– Higher power density is likely toresult from virtualization, at least in some racks. Areas of high density can pose cooling challenges that, if left unaddressed, could threaten the reliability of the overall data center. 2) Reduced IT load can affect PUE– After virtualization, the data center’s power usage effectiveness (PUE) is likely to worsen. This is despite the fact that the initial physicalserver consolidation results in lower overall energy use. If the power and cooling infrastructure is not right-sized to the new lower overall load, physical infrastructureefficiency measured as PUE will degrade.

  9. 3) Dynamic IT loads– Virtualized IT loads, particularly in a highly virtualized, cloud data center, can vary in both time AND location. In order to ensure availability in such asystem, it’s critical that rack-level power and cooling health be considered before changes are made. 4)Lower redundancy requirements are possible– A highly virtualized data centerdesigned and operated with a high level of IT fault-tolerance may reduce the necessity for redundancy in the physical infrastructure. This effect could have a significantlypositive impact on data center planning and capital costs.

  10. To improve post-virtualization PUE, the data center’s infrastructure efficiency curve must beimproved (lowered) by optimizing power and cooling systems to reduce the waste of oversizing and better align capacity with the new, lower load. In addition to improvingefficiency, optimized power and cooling will directly impact the electric bill by reducing thepower consumed by unused power and cooling capacity.

  11. Energy proportional system • Normal System

  12. For normal server Utilization is measure of application perfomance like requests per second on a web server

  13. Energy efficiency of servers have increased as the time passed.

  14. For Energy Proportional Server

  15. Conclusion • The introduction ofmore efficient CPUs based on chip multiprocessing hasalso contributed positively toward more energy-efficientservers. • However, long-term technology trends invariably indicate that higher performance means increasedenergy usage. As a result, energy efficiency must improveas fast as computing performance to avoid a significantgrowth in computers’ energy footprint. (Barroso & Hölzle, 2007)

  16. To a first-order approximation, bothcooling and provisioning costs are proportional to theaverage energy that servers consume, therefore energyefficiency improvements should benefit all energydependent components

  17. References N.A (2011).Cooling the Cloud,Infotech. [ONLINE] Retrived from http://it.tmcnet.com/topics/it/articles/216010-cooling-cloud.htm [24.10.2013] Kava,J. (2012). Cooling the cloud: A look inside Google’s Hot Huts.Google Green Blog[ONLINE]Retrieved from http://googlegreenblog.blogspot.com/2012/10/cooling-cloud-look-inside-googles-hot.html [24.10.2013] Barroso,L.A. & Hölzle,U. (2007). The case for energy proportional computing.Computer,December 2007,33-37. Schneider Electricity-Data Center Science [ONLINE] Retrieved from http://www.facilitiesnet.com/microsite/energy-efficient-data-center-strategies/pdf/WhitePaper118.pdf [24.10.2013] Paya, A. & Marinescu, D.C.(2013). Energy-aware Application Scaling on a Cloud.Arxiv.org[ONLINE] Retrieved from http://arxiv.org/abs/1307.3306 [24.10.2013]

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