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Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter Published on: HotPower ‘09 Presentation By: Liang Hao Tuesday, August.

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Presentation on theme: "Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter Published on: HotPower ‘09 Presentation By: Liang Hao Tuesday, August."— Presentation transcript:

1 Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter Published on: HotPower ‘09 Presentation By: Liang Hao Tuesday, August 03, 2010

2 Author  Christopher Stewart from The Ohio State University  Kai Shen from University of Rochester  Publications:  Of Christopher StewartChristopher Stewart  Of Kai ShenKai Shen

3 Motivation  To reduce datacenters’ dependence on costly and less clean energy from the grid  Hence to maximize the use of renewable energies  To explore the possibility of evaluating the request-level power consumption

4 Problems Applications in the datacenter must be available 24x7 But wind and solar energy are intermittent  Datacenters powered by renewable energy need backups Primary options: Grid, generator, battery Alternatives are either dirty and/or costly  Renewables are precious! renewable = joule converted from solar/wind The preferred energy source Available only sometimes and costly to store

5 Opportunities  Capacity planning Compute power should fluctuate with intermittent outages—i.e., turn machines off  Load balancing Route requests to datacenters with unused renewables Migrate services to datacenters with renewables

6 Intermittency 1.Datacenter Modeling

7  Automatic transfer switch (ATS) Input 2 power sources, outputs 1 power source Monitors power from the primary source When power from primary dips below threshold, ATS switches to secondary When primary exhibits power of threshold, ATS switches back to secondary

8  Key parameters related to the ATS to ensure dependability or reliability, threshold equals peak consumption to make full use of renewable energies, scale down the threshold Intermittency 1.Datacenter Modeling

9  Battery backup too costly  But if we apply renewable-aware management, there is enough supply from other datacenters Intermittency 2.Wind Intermittency

10  when threshold set to peak power, the utility of wind turbine power production drops 65% compared to when threshold set to zero. Intermittency 3.Renewable Utility

11  Economical feasibility (metric: cost per KW-hour)  Average price for commercial electricity $0.10 KW-hour  $2.4M to erect a wind turbine that is connected (directly) to a datacenter [European Wind Energy Assc.] $1.6M installation 2% annual maintenance fees Lifetime of turbine: 20 years  Datacenter at CA or MT could use 24M KWh Either high power consumption or zero threshold Wind-powered datacenter in MT: $0.04 KWh Intermittency 3.Renewable Utility

12 Request-level Event Profiling  To estimate the power consumption of individual requests  With quantized statistics, scheduler could possibly route some requests to datacenters with unused renewables

13  Tracing the route that a request go through, including CPU usage and other hardware events.  We configured the performance counters to assemble three predictor metrics for our power model:  L2 cache requests per CPU cycle (C cache ),  memory transactions per CPU cycle (C mem ),  and the ratio of non-halt CPU cycles (C nonhalt ). Request-level Event Profiling

14  Power consumption is calculated according to the expression below  where P’s are coefficient parameters for the linear model. are constants that approximate ceiling values for the predictor metrics. Request-level Event Profiling

15  1) idle  2) CPU spinning with no access to cache or memory  3/4) Apache web server with either short requests (no more than 1KB files) or long requests (files of 100 KB– 1 MB)  5/6) OpenSSL RSA encryption/decryption using either a small key or a large key. We also use four full server workloads  7) TPC-C running on the MySQL database  8) TPC-H running on the MySQL database  9) RUBiS  10) WeBWorK. Request-level Event Profiling micro benchmarks

16  Request workloads executed in isolation  WattsUp power meter measures watts and joules  Processor was not adjusted during tests Request-level Event Profiling

17 Vision

18 Reference [1] Green House Data: Greening the data center. [2] Realistic nonstationary workloads. [3] Wind power. [4] Google solar panel project. June [5] P. Barham, A. Donnelly, R. Isaacs, and R. Mortier. Using Magpie for request extraction and workload modeling. In USENIX Symp. on Operating Systems Design and Implementation, Dec [6] F. Bellosa. The benefits of event-driven energy accounting in power- sensitive systems. In 9th ACM SIGOPS European Workshop, Sept [7] J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle. Managing energy and server resources in hosting centers. In ACM Symp. on Operating Systems Principles, Oct [8] European Wind Energy Association. The economics of wind energy. [9] U. Hölzle. Powering a Google search. Jan

19 [11] D. Meisner, B. Gold, and T. Wenisch. Powernap: Eliminating server idle power. In Int’l Conf. on Architectural Support for Programming Languages and Operating Systems, Mar [12] National Renewable Energy Laboratory. NREL: Western wind resources dataset [14] K. Shen, M. Zhong, S. Dwarkadas, C. Li, C. Stewart, and X. Zhang. Hardware counter driven on-the-fly request signatures. In Int’l Conf. on Architectural Support for Programming Languages and Operating Systems, Mar [15] C. Stewart, T. Kelly, and A. Zhang. Exploiting nonstationarity for performance prediction. In EuroSys Conf., Mar [16] C. Stewart, M. Leventi, and K. Shen. Empirical examination of a collaborative web application. In IEEE Int’l Symp. On Workload Characterization, Seattle, WA, Sept Benchmark available at [17] P. Thibodeau. Wind power data center project planned in urban area. ComputerWorld, Apr [18] A. Vahdat, A. Lebeck, and C. Ellis. Every joule is precious: the case for revisiting operating system design for energy efficiency. In ACM SIGOPS European Workshop, Sept


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