SIFMA Data Center Design. IBM Research. Tom Keller, Austin Lab. Hendrik Hamann, T.J. Watson Lab
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1 SIFMA 2009 Securities Industry and Financial Markets Association Technology Conference June 2009 Data Center Design Tom Keller, Austin Lab Hendrik Hamann, T.J. Watson Lab IBM Research 6/24/2009 SIFMA 2009
2 Presenters Dr. Tom Keller Distinguished Engineer Austin Research Lab Dr. Hendrik F. Hamann Manager Photonics and Thermal Physics T.J.Watson Research Ctr data center energy efficiency & tools led first system-level power management for IBM products technical lead IBM AIX performance led Los Alamos performance evaluation of the Cray-1 serial #1 supercomputer at M.C.C. prototyped parallel database machine & led creation of the still surviving TPC- C benchmark Associate Director of the U. Texas Computation Center Chair, ACM Sigmetrics 50+ papers 2 06/24/2009 SIFMA 2009 physical aspects of thermal & energy management from the transistors to the data center nanoscale heat transfer research novel near-field optical microscopes to study single molecules at high spatial resolution, at joint institute between U. Colorado and NIST 30+ scientific papers 25+ patents with 25+ pending IBM Master Inventor National Academy of Sciences committee Industrial advisor to Universities
3 Agenda Case study from the financial Industry (Tom Keller) ACU Efficiencies Power Gap Analysis DC Measurement and Management Technologies (Hendrik Hamann) How to measure, model and manage DCs Results & Savings 3 06/24/2009 SIFMA 2009
4 Data Centers Three drivers have lead to DC crisis Insatiable IT demand Power-limited core technology Increasing energy costs DCs consume ~ 2 % of all US electricity Annual growth (15 %) is non-sustainable DC power projected to be > 8 % of US power by 2020 Every DC is different, DCs are heterogeneous and change over time DCs are not as efficient as they should Inefficiencies are caused by lack of best practices 4 06/24/2009 SIFMA 2009
5 IBM Data Centers = Delivery Hub 9% 6% Midrange Servers 6% 2% 1% % IBM SUN HP COMPAQ DELL OTHER Data Centers Americas Pan Euro Asia Pacific Total Count Square Feet (Millions) As of August, /24/2009 SIFMA 2009
6 The Energy Efficiency Management Challenge you know your DC could be more energy efficient, but Need ongoing energy efficiency measurements what you can t measure you can t improve no viable charge-back mechanism between IT and RESO current efficiency metrics are weather snapshots just too many good excuses/reasons not to implement Energy efficiency recommendations are like New Year s resolutions 1.) How much energy ($) do I really save? 2.) Are we really executing on the recommendations? 6 06/24/2009 SIFMA 2009
7 Energy Efficiency Proof of Concept Study Tier 4 (cooling, power) 40,000+ square feet Many hundreds of racks, plus Hundreds of freestanding racked machines Thousands of servers, SANs and routers Using Research s Measurement & Management Technology, we expected above floor temperatures to look something like this 40+ Computer Room Air Conditioners (ACUs) Cooling and actual power known to be underused Instead, the machine room was uniformly cool at all heights * Exact measures are client confidential 7 06/24/2009 SIFMA 2009
8 Instrumenting the ACUs revealed why -- excess cooling capacity being used And sometimes ACUs went nuts One of Many ACUs 8 06/24/2009 SIFMA 2009
9 Cooling kwh histories of two pairs of ACUs 121 kwh 100%! 9 06/24/2009 SIFMA 2009
10 After computational fluid dynamics modeling, recommended a schedule for turning ACUs on and off Procedure Preliminary data collection Deploy above and below floor sensor network Gather temperatures and pressure measurements Experimental evaluation of turning ACUs off Turn 2 ACUs off Check for system safety Sensor network to check temperatures and pressures Spot check air flow in critical areas via flowhood Compare results to model Calculate ACU efficiencies Repeat until suggested number of ACUs are off Monitor system for 1 week to gather additional data Deployment Turn on additional ACUs when a ACU fails Turn on all ACUs during ACU maintenance Installed inexpensive ACU temperature sensing network 10 06/24/2009 SIFMA 2009 Savings Each off ACU will save $10,000 to $20,000 per year in electric bill
11 Power gap analysis Displaying the difference between the actual power being used in a rack and the power allocated to the equipment in the rack showed opportunities for increasing the density of equipment in the data center Improvements in the 10 s of percent in equipment density can be made safely, deferring the construction of new data centers 11 06/24/2009 SIFMA 2009
12 Power Measures 600 square foot snippet from machine room ACUs Racks and freestanding equipment 12 06/24/2009 SIFMA 2009
13 Power stranded by overallocation 13 06/24/2009 SIFMA 2009
14 Power capacity wasted by overallocation the difference between derated kva and measured kva values 14 06/24/2009 SIFMA 2009
15 Agenda Case study from the financial Industry (Tom Keller) ACU Efficiencies Power Gap Analysis DC Measurement and Management Technologies (Hendrik Hamann) How to measure, model and manage DCs Results & Savings 15 06/24/2009 SIFMA 2009
16 Single Energy Efficiency Metric is Not Enough PUE might be good for bragging rights, but it is only a start Fall Winter Spring PUE = Total DC Power / IT Power widely used today PUE metric can be problematic PUE is weather-, location-, application/tier-, and power density dependent does not include true IT performance metering is often not in place 16 06/24/2009 SIFMA 2009
17 Cooling Efficiency requires a more detailed Look Thermodynamic COP COP* P /P thermo RF Chiller Transport COP # of active ACUs COP P / P i trans RF ACU i = 1 *Average Chiller COP (throughout the year) Data Center COP 1 / COP 1/ COP + 1/ thermo COP trans THERMODYNAMIC PART OF COOLING: HOTSPOTS / HIGH INLET TEMPERATURES IMPACT CHILLER EFFICIENCY (~ 1.7 % per F) TRANSPORT PART OF COOLING: LOW ACU UTILIZATION IMPACTS ACU BLOWER CONSUMPTION (~ 5-8 kw/acu) 17 06/24/2009 SIFMA 2009
18 Measuring and Managing DC Best Practices IBM Measurement and Management Technologies (MMT 1.0) Optimize DC resources to reduce up to 15% of DC energy consumption Scans, digitize rapidly physical environment (temperature, flow, pressure etc..) of DC Cart tool comprises sensor network, where each sensor defines a virtual unit cell Integrates measurements, models and DC management 5.5 feet Hot spot Hot air mixing with cold air 18 06/24/2009 SIFMA 2009
19 MMT Process Solution Approach Three Steps 1 Measure 2 Model Capture high resolution temperature data, air flow data and infrastructure & layout data To identify improvement opportunities model the data center and use optimization algorithms ( best practices rules ) 3 Manage Best Practices Realize air transport energy savings Realize thermodynamic energy savings Achieve reduced energy consumption Potential for deferring new investments 19 06/24/2009 SIFMA 2009
20 IBM Research MMT 3D Temperature Distributions MMT Scans: Thermal measurements at different heights (1 ft increments in z) max 40 minutes scan time 30,000 thermal readings 3,000 humidity readings 200 air flow readings min 20 06/24/2009 SIFMA 2009
21 Example Energy Savings Saving = 177 kw saved 177 kw with measurement / metrics driven best practices implementation typical 1-2 Month turnaround to realize savings improved DC COP 2.39 to 3.44 COP thermo from 4.5 to 5.1 COP trans from 5.3 to 9.8 power consumption Cool IT Cool IT total energy savings ~ $200k (ROI: 7 months) before after Finding / Metrics Horizontal hotspots (HH) Vertical hotspots (VH) Non-targeted air flow Plenum temperatures ACU utilization ACU flow Key Action / Solution change tile layout & deploy high throughput tiles snorkels / fillers close leaks / cable cutouts service ACUs supply side / increase ACU utilization turn under-utilized ACUs off remove blockage thermo transport Case Study: DC Area = 20k sqf; Temp. Meas. = 200,000; Airflow Meas. = 1,200; Power density ~ 75 W / sqf 21 06/24/2009 SIFMA 2009
22 MMT Historical Record / Scorecard MMT service provided to more than 50 DCs different sizes, power densities, locations etc. usually energy savings of > 10 % of IT power (< 1 y ROI) has delayed major DC upgrades / capital investments MMT is an WW IBM service offering with Research support in three GEOs Example MMT Savings Survey Date RF power P RF [kw] DC area [feet 2 ] % Savings DC1 05/ k 13 DC2 06/ k 11 DC3 07/ k 9 DC4 09/ k 9 DC5 10/ k 11 DC6 10/ k 12 DC7 12/ k 8 DC8 12/ k 4 DC9 01/ k 10 TOTAL 10 WW MMT Activities 22 06/24/2009 SIFMA 2009
23 MMT 1.5: Move to a dynamic Solution DC can change over time IT power levels can change (e.g., % during a day) cooling conditions change etc.. new racks / new servers / re-arrangement of tiles etc.. MMT 1.5 provides high resolution combining MMT 1.0 for base model generation, sensor placement etc.. real-time sensors for creating dynamic models max min Animation of 3D heat map over 24 hours Real time Sensors: /24/2009 SIFMA 2009
24 MMT 1.5 Client MMT 1.5 Web Client FEATURES: efficiency of each cooling zone in real-time data analysis capabilities energy efficiency reporting detailed layout editor alarm services / hotspot services real-time temperatures / 3D capabilities current being integrated into Tivoli and Maximo /24/2009 SIFMA 2009
25 MMT 1.5 Client - continued indicates cooling efficiency provides alarm/threshold settings for sensors determines fan efficiencies in real-time and provides corresponding cooling zones provides weekly reports of all logged temperatures 25 06/24/2009 SIFMA 2009
26 MMT IBM Internal Deployment MMT 1.0 successfully WW deployed in > 1 M square feet of DC space ~2MW of savings so far $1.5M savings in utility bill savings > $10M delayed capital cost over 150 ACUs turned off / decommissioned ~ 8-9 % PUE improvements MMT 1.5 currently being rolled out WW active management large-scale dynamic DC large scale deployment in EMEA MMT 1.5 part of leadership DC design and architecture full deployment in leadership DC in Raleigh integrated into ITD data models & asset management systems 26 06/24/2009 SIFMA 2009
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