Sustainable Data Centers: HP Labs

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Information about Sustainable Data Centers: HP Labs
Technology

Published on July 30, 2009

Author: hewlettpackard

Source: slideshare.net

Description

A presentation and paper delivered by HP Labs director Prith Banerjee at the 46th Design Automation Conference.

Full paper available here:

http://www.docstoc.com/docs/9173417/Sustainable-Data-Centers-Enabled-by-Supply-and-Demand-Side-Management

July 2009.

www.hplabs.com
www.twitter.com/hplabs

Sustainable Data CentersEnabled by Supply and Demand Side Management Prith Banerjee Senior Vice President of Research and Director, Hewlett Packard Laboratories (Co-authors: Cullen Bash, Chandrakant Patel, Partha Ranganathan)

2 DAC2009-50-2 HP Labs Research Areas The next technology challenges and opportunities Digital Commercial Print Intelligent Infrastructure Content Transformation Sustainability Immersive Interaction Cloud Analytics Information Management

3 DAC2009-50-2 The rest of the global economy IT industry 98% 2% IT must play a central role in addressing the global sustainability challenge. Industry Challenge Create technologies, IT infrastructure and business models for the low-carbon economy Total carbon emissions As much as the aviation industryProjected to double by 2020IT can play a role in reducing this impact To do so, IT solutions must take a lifecycle perspective3

As much as the aviation industry

Projected to double by 2020

IT can play a role in reducing this impact

To do so, IT solutions must take a lifecycle perspective

4 DAC2009-50-2 Role of the IT EcosystemData Centers at the Hub Sustainable Data Centers enabled by supply and demand side management of power, cooling and IT resources 4

5 DAC2009-50-2 Supply and Demand Side Management Supply Side: Design of physical infrastructure with focus on lifecycle engineering and management, and the available energy required to extract, manufacture, operate and reclaim components; Utilization of local resources to minimize destruction of available energy in transmission, and construction of transmission infrastructure. Demand Side: Provisioning data center resources based on the needs and service level agreement of the user through use of flexible building blocks, pervasive sensing, knowledge discovery and policy based control 5

6 DAC2009-50-2 IT:SW IT:HW Power Cooling Autonomous Control Knowledge Discovery & Visualization Pervasive Cross-layer Sensing Flexible, Efficient, & Configurable Building Blocks Data Center Scale Lifecycle Design Sustainable Data Center Key Elements extraction operation manufacturing End of Life 6

7 DAC2009-50-2 Lifecycle Design extraction operation manufacturing End of Life 7

8 DAC2009-50-2 Lifecycle Design through Data Center Synthesis Automate design of datacenters based on lifecycle considerations Synthesis Process Flow 8

9 DAC2009-50-2 Flexible Building Blocks From chips, to servers to data centers Cooling Grid Qdata center + ∑W Power Grid – Wensemble Wblower Wblower Wpump Wcompressor Wpump Ground Coupled Loop Qsystem Wblower Wsystem Qchip Outside Air Wchip 9

10 DAC2009-50-2 Microblades and Megaservers The Inefficiencies in the Cloud Hardware Power & cooling

11 DAC2009-50-2 Disaggregation Efficient building blocks   

12 DAC2009-50-2 2X performance/$

13 DAC2009-50-2 Pervasive SensingIT & Facilities Cooling Infrastructure IT Hardware External Environment 13

14 DAC2009-50-2 Knowledge Discovery in the IT ecosystem minimizing material and minimizing energy PCA-based Anomaly detection Mobile-enabled diagnostics Collaborative Fault Analytics Data Center Room Infrastructure Power Micro-Grid Infrastructure Cooling Grid Infrastructure Client Infrastructure Data Aggregation Pathways Event/Episode Detection Model Creation Causality Pre-Process Raw Data Useful Knowledge Visualization Expedient Assessment Early Warning System Lifetime Estimation Performance Metrics Efficiency Metrics Expert Systems

DAC2009-50-2 Air Mixture Out Warm Water Air Mixture In Air Mixture In Cooling Tower loop Wp QCond Makeup Water Return Water Chiller Refrigerant loop Wcomp QEvap Wp Chilled Water loop Data Center CRAC units Example: Chiller Unit Ensemble Data center cooling infrastructure Clustering 15

DAC2009-50-2 Active Control of Cooling Resources Conventional Mode With active control using rack inlet temperature 35% Energy SavingsImproved reliability

DAC2009-50-2 Dynamic Workload Placement based on Cooling Efficiency 8am 10am 3pm Results (Thermal Management Margin)j + (AC Margin)j 32% Energy savings over random job placement $1M/year in savings (for large DCs)Improved thermal reliability of IT equipmentIncreased uptime(Hot Air Recirculation)j (Tset – Tin)i + [(TSAT - TSAT,min)j TCIj]i (Tin – TSAT)i Premise: Hotspots exist that impact efficiencyUse LWPI to place workload in more efficient locationsTest using batch loads in real data centerLWPI = 0.33 LWPI = 3.8 Batch load for tests LWPI = 4.9 Local Workload Placement Index … LWPI = = 17 17

32% Energy savings over random job placement

 $1M/year in savings (for large DCs)

Improved thermal reliability of IT equipment

Increased uptime

Hotspots exist that impact efficiency

Use LWPI to place workload in more efficient locations

Test using batch loads in real data center

18 DAC2009-50-2 Demonstration at Scale Software Operations, Bangalore Consolidation of 14 lab data centersFacility Building Blocks IT Building Blocks ServersNon-Stop serversProliant serversBlade serversCustom EnclosuresStorage (XP/EVA)Multiple Network topologiesSensorNetwork7500 sensorsChillers3 air-cooled2 water-cooledPumps7 Primary5 SecondaryCRAC units55 unitsDiesel Generators5 3MW units 5 floors @14k sq. ft. 900kW cooling per floor Need based provisioning of compute, power and cooling resources based on available energy consumed (supply side) Dynamic cooling control implemented Data Analysis, Visualization and Knowledge Discovery to detect anomalies, improve reliability and minimize redundancy40% reduction in AHU power 20% reduction in Infrastructure Power 7,500 tons of CO2 prevented annually 18 27 July 2009

Software Operations, Bangalore

Consolidation of 14 lab data centers

Servers

Non-Stop servers

Proliant servers

Blade servers

Custom Enclosures

Storage (XP/EVA)

Multiple Network topologies

SensorNetwork

7500 sensors

Chillers

3 air-cooled

2 water-cooled

Pumps

7 Primary

5 Secondary

CRAC units

55 units

Diesel Generators

5 3MW units

Need based provisioning of compute, power and cooling resources based on available energy consumed (supply side)

Dynamic cooling control implemented

Data Analysis, Visualization and Knowledge Discovery to detect anomalies, improve reliability and minimize redundancy

19 DAC2009-50-2 Rack heterogeneity X VM Enclosure IAM Server iLO CPU CHAOS!! (“Power” Struggle) X VM-res.all Peak thermal power Peak electrical power OS-wlm Average power Vmotion OS-gwlm Local optima LSF X SIM global optima X Power Struggles! X X X X X X performance performance X X X performance X X X X X X X X X

20 DAC2009-50-2 Cross Layer Monitoring and Management Framework 20

21 DAC2009-50-2

22 DAC2009-50-2 It works!

23 DAC2009-50-2 It works well! 65% savings (OpEx) 20% savings (CapEx) Similar performance

24 DAC2009-50-2 Other interesting insights… No VMC VMC only Unified

25 DAC2009-50-2 Conclusions Environmental impact of IT is a growing worldwide concern Governments are beginning to take notice and regulations are increasing Management of available energy required to run cost effective operation An integrated, life-cycle approach to data center design and management is necessary to improve efficiency and reduce impact. Demonstrated results with economic payback

Environmental impact of IT is a growing worldwide concern

Governments are beginning to take notice and regulations are increasing

Management of available energy required to run cost effective operation

An integrated, life-cycle approach to data center design and management is necessary to improve efficiency and reduce impact.

Demonstrated results with economic payback

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