Average and Marginal Reductions in Greenhouse Gas Emissions from Data Center Power Optimization

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1 Average and Marginal Reductions in Greenhouse Gas Emissions from Data Center Power Optimization Sabrina Spatari, Civil, Arch., Environmental Engineering Nagarajan Kandasamy, Electrical and Computer Engineering Dara Kusic, Electrical and Computer Engineering Eugenia V. Ellis, Architecture & Interiors 1

2 Data Deluge 1.25 trillion gigabytes generated in 2010 growing by a factor of 10 every 5 years due to rapid growth of digital sensing/multimedia Total data generated > storage capacity Maintaining data flow and storing is expensive: Cost Energy References: Economist, February 25, 2010; R. Baraniuk (Rice University) 2

3 Data Deluge Presence of data creates new: markets/activities consumption possibilities Efficiency improvements* Sustainability in information age Growth in data generation Data consumption Energy/GHG cost consumption Focus: energy/ghg emissions from housing and storing data Good/bad *Economist, February 25,

4 DATA CENTERS CARBON FOOTPRINT IS HIGH AND GROWING Key points on data centers greenhouse gas emissions Data center electricity consumption is almost.5% of world production* Average data center consumes energy equivalent to 25,000 households Worldwide energy consumption of DC doubled between 2000 and 2006 Incremental US demand for data center energy between now and 2010 is equivalent of 10 new power plants 90% of companies running large data centers need to build more power and cooling in the next 30 months Server energy consumption is growing at 9% per year Data centers are projected to surpass the airline industry in CO 2 emissions due to electricity consumption by 2020 Cost ¼ of total IT budget Carbon dioxide emissions as percentage of world total industries Percent Carbon emissions countries Mt CO 2 p.a. * 4Including custom-designed servers (e.g., Google, Yahoo) Source: Financial Times; Gartner report 2007; Stanford University; AMD; Uptime Institute; McKinsey analysis 0.3 Data centers 170 Data centers Airlines Shipyards Steel plants Argentina Netherlands Malaysia

5 A problem of: Data centers & Energy Power supply Building/HVAC system operation CPU load management Life cycle/supply chain inputs 5

6 A problem of: Data centers & Energy Power supply Building/HVAC system operation CPU load management Literature: LCA energy, air quality Shehabi, Horvath et al 2008 (LBNL) Power load mgt: Horvath et al 2004; 2007 LCA: hardware, IT infrastructure 6 Williams, E.; Horvath, A.

7 Virtualization for Power Management Performance-isolated platforms, called virtual machines (VMs), allow resources (e.g., CPU, memory) to be shared on a single server Enables consolidation of online services onto fewer servers Increases per-server utilization and mitigates server sprawl Technique Selectively turn off core components to increase remaining unit efficiency Deploy virtualization for existing and new demand Efficiency Impact 3-5% 25-30% Enables on-demand computing, a provisioning model where resources are dynamically provisioned as per workload demand Implement free cooling Introduce greener and more power efficient servers 0-15% 10-20% McKinsey & Co. Report: 7

8 Project Scope and Outline Examine net GHG savings through virtualization power management strategy in small-medium sized data centers Effects of time-of-day savings and selection of average or marginal unit of power output Combined power/performance management of virtualized computing environments Potential for interaction with building design LCA model 8

9 SERVER UTILIZATION Server utilization averages about 6%, accounting for idle servers Peak daily server utilization (%) Server Utilization Up to 30% of servers are idle! McKinsey & Co. Report: Average daily server utilization (%) 9

10 PROBLEM STATEMENT Address the problem of maximizing the efficiency of a data center hosting online services Minimize SLA violations Minimize power consumption Service-level agreement (SLA) violations are generated by system responses that do not meet minimum timing requirements 10

11 PROPOSED SOLUTION We perform combined power and performance management in a virtualized computing environment The problem is posed as one of sequential optimization under uncertainty and solved using limited look-ahead control (LLC) The notion of risk is encoded explicitly in the problem formulation The performance of the control approach is validated experimentally on a cluster of heterogeneous servers We show how concepts from approximation theory can be applied to control larger clusters 11

12 PROBLEM FORMULATION The power/performance management problem is posed as a dynamic resource provisioning problem under dynamic operating constraints Objectives: Maximize the profit generated by the system (i.e., minimize SLA violations and the power consumption cost) Control or decision variables to be optimized Number of VMs to provision to each service The CPU share given to each VM Number of servers to turn on or off Number of VMs to migrate Fraction of incoming workload to distribute to each of the VMs 12

13 PROBLEM FORMULATION (Contd.) Maximize u k [ R( x( k), u( k)) O( u( k)) S( u( k))] Subject to : x( k + 1) = φ( x( k), u( k), ω( k)) H ( x( k)) 0, u( k) U ( x( k)) Power consumption cost of operating servers Switching costs SLA violations incurred during VM migration Transient power consumption costs Key characteristics of the control problem Some control actions have (long) dead times; e.g., switching on a server, instantiating VMs, migrating VMs Decision variables must be optimized over a discrete domain Optimization must be performed under explicit constraints Ref: Kusic, D., J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, Power and Performance Management of Virtualized Computing Environments via Lookahead Control,'' Cluster Computing, vol. 12, no. 1, pp. 1-15, Springer Netherlands, 13 March 2009.

14 Experimental Testbed The testbed is configured as a two-tier architecture with front-end application servers and back-end databases The goal is to maximize profits across three applications hosted in the virtualized execution environment Perform resource provisioning at the application tier Workload in traces from 1998 World Cup website accessed over 24 hrs 14

15 Virtualization Main Results A server cluster managed using LLC saves 41% in power-consumption costs over a 24-hour period when compared to an uncontrolled system Power savings are achieved with very few SLA violations (0.01% of the total number of requests) Controller execution time is reduced by as much as 80% with only a small sacrifice in performance when approximation theory is applied Ref: Kusic, D., J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, Power and Performance Management of Virtualized Computing Environments via Lookahead Control,'' Cluster Computing, vol. 12, no. 1, pp. 1-15, Springer Netherlands, March

16 Life Cycle Model: Power and GHG Emissions Savings Combine small-cluster virtualization study findings with LCI model: Uses cradle-to-gate electricity grid mix for Northeast US states Supply mix varies over the day with peak supply from coal and natural gas Estimate the hour-by-hour energy savings through virtualization strategy Load curve data from PJM to represent average and marginal power supply each hour of the year Combine with LCA data for power generation mix: hydro, nuclear, coal, natural gas, oil GWI electricity delivered Data Center Application Tier Workload Arrivals Dispatcher LLC Controllable Parameters: host on/off: N(k), VM on/off: n i (k), Workload fraction: γ ij (k), CPU share f ij (k) Bacchus Gold Tomcat Silver WebSphere Gold Tomcat Silver WebSphere Bronze Tomcat OS OS OS OS OS f 11 (k) f 21 (k) f 12 (k) f 22 (k) f 32 (k) Gold Tomcat Silver WebSphere Bronze Tomcat OS OS OS f 13 (k) f 23 (k) f 33 (k) Gold Tomcat Silver WebSphere Bronze Tomcat OS OS OS f 14 (k) f 24 (k) f 34 (k) Silver WebSphere OS f 2N (k) Apollo Chronos Demeter Eros Poseidon Sleep Operational Hosts Powered-down Hosts Gold DB2 OS Bronze DB2 OS Silver DB2 OS NFS Gigan File System Ares Chaos Database Tier 16

17 Load Curve August ,000 Power Output (kw) 140, , ,000 80,000 60,000 40,000 20, Time (hour) GWI electricity mix kgco 2 e/kwh Time (hour) RTO Stack Demand Hydro Nuclear Coal Natural Gas Oil (MW) ,676 4% 24% 54% 15% 4% ,524 4% 23% 54% 15% 5% ,081 3% 23% 54% 15% 5% ,678 4% 23% 54% 15% 5% ,140 4% 24% 54% 15% 4% 17

18 Energy Saved (Virtualization) over 24 hours Energy Savings (%) Time (hours) 18

19 AVOIDED GHG EMISSIONS OVER PEAK AUGUST DAY 80 Marginal unit ~ 1 ton CO 2 avoided per day Avoided GHG emissions (kg/hour) Average Marginal Time (Hour) 19 Small-medium size data center, ~ 500 machines

20 Findings Through virtualization, small medium sized data center can reduce energy demand by 28%-61% over one day Up to 1 ton/day in CO 2 avoided Usually higher avoided C with marginal unit Greater energy efficiency gains can be realized through combining building cooling strategies with virtualization 20

21 Future Work Use locational marginal prices (day-ahead electricity market) to incorporate electricity cost into control; Include upstream power source energy, GHG emissions Moving data workloads where power is greener, including to remote or cool parts (e.g., Iceland) to access low-c energy Sensors within the building to move power load to cooler parts of the data center Decrease HVAC power needs 21

22 Acknowledgements Drexel Engineering Cities Initiative (DECI) Andrew Huemmler, University of Pennsylvania 22