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Published on: HotPower ‘09 Presentation By: Liang Hao

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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 Stewart Of Kai 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 Datacenters powered by renewable energy need backups
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 Load balancing
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 Intermittency 1.Datacenter Modeling
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 Intermittency 1.Datacenter Modeling
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

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

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

11 Intermittency 3.Renewable Utility
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

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 Request-level Event Profiling
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 (Ccache), memory transactions per CPU cycle (Cmem), and the ratio of non-halt CPU cycles (Cnonhalt).

14 Request-level Event Profiling
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.

15 Request-level Event Profiling micro benchmarks
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 .

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

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 2007. [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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