1 / 28

Cutting the Electric Bill for Internet-Scale Systems

Cutting the Electric Bill for Internet-Scale Systems. Andreas Andreou Cambridge University, R02 aa773@cam.ac.uk. What’s this all about?. Energy expenses are an increasingly important fraction of data center operating costs Electricity prices show both temporal and geographical variation

varick
Download Presentation

Cutting the Electric Bill for Internet-Scale Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cutting the Electric Bill for Internet-Scale Systems Andreas Andreou Cambridge University, R02 aa773@cam.ac.uk

  2. What’s this all about? • Energy expenses are an increasingly important fraction of data center operating costs • Electricity prices show both temporal and geographical variation • Exploit variations in electricity prices for economic gain

  3. Key observations • Electricity prices vary • Prices vary on an hourly basis • Often not well correlated at different locations • Substantial variations • Large distributed systems already incorporate request routing and replication • Dynamic request routing to map clients to servers • Mechanisms to replicate data necessary to process requests at multiples sites

  4. Problem Specification • Large system composed of server clusters spread out geographically • Map client requests to clusters such that the total electricity cost is minimized • Assumptions • System fully replicated • Optimize for cost every hour • No knowledge of the future • Rate of change slow enough to be compatible with existing routing mechanisms • Fast enough to respond to electricity market fluctuations • Incorporate bandwidth and performance goals as constraints

  5. Terminology • Energy Elasticity • Degree to which energy consumed by a cluster depends on the load placed on it • Ideally: no load, no power • Worst case: no difference between peak and idle power • State-of-the-art: idle power around 60% of peak • Differential Duration • Number of hours one location is favored over another by more than $5/MWh • PUE • Power usage effectiveness (measure of data center energy efficiency)

  6. Background

  7. Wholesale Electricity Markets (1) • Generation • Government and independent power producers • Coal (~50%), natural gas (~20%), nuclear power (~20%), hydroelectric generation (~6%) • Different regions, different power generation profiles • Transmission • Producers and consumers are connected to an electric grid • 8 reliability regions

  8. Wholesale Electricity Markets (2) • Market Structure • Each region managed by Regional Transmission Organization (RTO) • RTO administer wholesale electricity markets • Auctioning mechanism: • Producers present supply offers • Consumers present demand bids • Coordinating body determines flow and sets prices • Market Types • Day-ahead markets • Real-time markets

  9. Wholesale Electricity Markets (3) • Market Structure • Assumptions • Real-time prices are known and vary hourly • Electric bill is proportional to consumption and indexed to wholesale prices • Request routing behavior induced by our method doesn’t significantly alter prices and market behavior

  10. Daily Variation

  11. Different Market Types • Hourly real-time (RT) market is more volatile than day-ahead market

  12. Hour-to-Hour Volatility

  13. Geographic Correlation

  14. Price Differentials

  15. Differential Distributions

  16. Time-of-Day

  17. Differential Duration

  18. Akamai: Traffic and Bandwidth • Over 2000 content provider customers in the US • 9-region traffic with electricity price data • Data covering 24 days worth of traffic • Traffic data of 5-minute intervals from public clusters • Bandwidth costs are significant • Aggressively optimized to reduce bandwidth costs • 95/5 billing model • Client-Server Distances • Use geographic distance as a coarse proxy for network performance

  19. Cluster Energy Consumption (1) • Roughly linear to its utilization • Pidle : average idle power draw of single server • Ppeak : average peak power draw of single server • r: empirical derived constant • ut : average CPU utilization at time t • what is important in determining savings

  20. Routing Energy • Increased path lengths will not alter energy consumption significantly • Average energy for a packet to pas through is on the order of 2mJ • Incremental energy dissipated by each packet passing through a core router would be as low as 50μJ per medium size packet • New routes may overload existing routers • Additional bandwidth could lead to upgrade • Can ignore by incorporating 95/5 bandwidth constraints

  21. Simulation Strategy • Real-time market prices for 29 different locations • Traffic data for Akamai public clusters in 9 of those • Data set spanning Jan 2006 through Mar 2009 • Workload data set contains 5-minute samples in 25 cities • Period of 24 days and some hours • Discarded 7 and grouped remaining 18 cities to 9 clusters • Akamai’s geographic server distribution • Two routing schemes • Akamai’s original allocation • Distance constrained electricity price optimizer • Energy model as shown before

  22. 24 Days of Traffic (1) • Energy Elasticity • Bandwidth Costs

  23. 24 Days of Traffic (2) • Distance and savings

  24. 39 Months of Prices • Derived from 24-day Akamai workload (US traffic only) • Dynamic beats static

  25. Results • Existing systems can reduce energy costs be at least 2% without any increase in bandwidth costs or significant reduction in client performance • Google-like energy elasticity • Akamai-like server distribution • 95/5 bandwidth constraints • Savings increase with energy elasticity • Fully elastic system with relaxed bandwidth constraints can reduce energy cost be 30% (13% with bandwidth constraints) • Allowing increase of client-server distances leads to increased savings

  26. Considerations (1) • Not reacting immediately to price changes noticebly reduces overall savings

  27. Considerations (2) • Server operators should be able to negotiate contractual arrangements • Distributed systems with energy elastic clusters can be more flexible than traditional consumers • Triggered demand response programs

  28. Future Work • Implementing Joint Optimization • RTO Interaction • Weather Differentials • Environmental Cost

More Related