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It&smart grid


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Information technology and smart grid

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It&smart grid

  1. 1. Information Technology & Smart Grid Energy Efficient Information Technology Heather Brotherton
  2. 2. 2 What is Information Technology (IT)?  Information     + Technology Technology: fire, wheel Early information technology: Dewy decimal system Information collections continue to expand Compiling data into information became exponentially labor intensive
  3. 3. 3 Information Systems  Information systems make it possible to process large amounts of data into information  Now that we have good information how do we quickly share research findings?  Networking; the internet
  4. 4. 4 Ubiquitous Communication  The internet gave rise to ubiquitous realtime communications     Anyone can transmit: Files Photos Video  We use the internet to connect with others
  5. 5. 5 Convenience  Bank by phone  Turn by turn directions  Researching purchases  Home buying  Meal planning  Home automation  Smart Grid
  6. 6. 6 Smart Grid: House Concept
  7. 7. 7 End User Implementation
  8. 8. 8
  9. 9. 9 Smart Grid & IT  Information technology and the power grid are interdependent   2003 North East Blackout The extent and duration of the outage was due to a race condition in the SCADA system
  10. 10. 10 Smart Grid & IT  Smart   Grid expands this interdependence However it also enhances the reliability and redundancy of the SCADA systems More situations are automated to avoid human error
  11. 11. 11 Data Centers & Smart Grid  Can coordinate energy consumption during   Low periods Periods where a high percentage of renewable energy such as wind or solar is available
  12. 12. 12 What types of IT processes could take advantage of this?  Batch jobs such as running  Reports  Back ups  Scheduled maintenance
  13. 13. 13 Big Data Problem  All of these integrated systems will create vast amounts of data   This data will be used for modeling to make real time decisions This data will depend upon information systems infrastructure to maintain; creating additional IT processing and archive loads.
  14. 14. 14 What’s the big deal storage space is cheap?  Not really  Hard drive usage must be powered  Additional load for backups  Can be a huge deal in a virtualized environment   Memory usage is also decreased by 34% Multiply that by 100 in a virtual environment, the resource savings is huge
  15. 15. 15 Storage Problem  Vast amounts of data some will be used in real-time situational analysis to allow Independent Systems Operators to make operational decisions.   Past data may not be accessed daily, but will need to be readily available for generation of reports to make strategic decisions Accessibility to large amounts of data creates substantial energy overhead
  16. 16. 16 Energy Efficient Data Storage  Hard Disk Drive (HDD) vs. Solid State Drives (SDD)   HDD  Uses 80% of maximum power draw at start-up SDD  Linear proportion usage to power relationship  This means that any HDD active, but running at under 80% utilization is wasting energy.
  17. 17. 17 Energy Efficient Data Storage  So is the answer to use all solid state drives?  That is one possible answer, but it is currently an expensive answer.  Other  possibilities: Storage consolidation  Virtualization is one possible method of achieving this goal  Shared storage arrays are another
  18. 18. 18 Storage Problem  Increased use of technologies such as memcached for frequently accessed data    This technique uses RAM caching Used in conjunction with compression this can be very useful for transactional loads Hybrid SDD/HDD storage systems managed intelligently
  19. 19. 19 Data Center Power consumption  In 2005 data center power usage was 1% of the worlds power consumption.  The current power usage is estimated to be as high as 1.5% world wide and up to 2.2% of US power consumption.
  20. 20. 20 Data Center Power consumption  Why    should you care? Every year there are rolling blackouts though the summer because utilities cannot keep up with demand. Energy costs are higher than equipment costs. Loss of electricity or computing capabilities can pose national security risks
  21. 21. 21 Efficiency Basics  Power Usage Effectiveness (PUE) developed by the Green Grid is a widely accepted measure of data center efficiency.  PUE Calculation Total data center energy consumption IT energy consumption=PUE
  22. 22. 22 PUE explained A PUE of one means that all energy consumptions is being used by the servers, storage devices and networking equipment.   Reaching one is the goal, but may not be possible currently… However, Google has reached reach a Power Usage Effectiveness (PUE) of 1.16.
  23. 23. 23 There is a better way… According to Google “if all data centers operated at the same efficiency as ours, the U.S. alone would save enough electricity to power every household within the city limits of Atlanta, Los Angeles, Chicago, and Washington, D.C.”
  24. 24. 24 Google   Google tracks its PUE Data centers are run at 80 degree Fahrenheit or more     This “conforms with both the American Society of Heating, Refrigerating, and Air Conditioning Engineers' recommendations and most IT equipment manufacturers' specs.” Data centers can be cooled without chillers Remaining chillers have disabled “dehumidifying and reheating functions on CRACs. Most are set to dehumidify air to 40% and reheat air if the return air is too cold, but these functions aren’t needed.” Each data center element is designed to operate at optimal efficiency
  25. 25. 25 Google: How did they do it?  Power Supply    The average power supply converts power from AC to DC accounting for 30 to 40% in power loss. This process also produces heat Google power supplies do not perform this costly conversion and have an integrated UPS.  “We’ve also cut out 2 of the AC / DC conversion stages by putting back-up batteries on the server racks themselves.”
  26. 26. 26 Google: Custom Servers  Parts are omitted “on servers that aren't needed for our applications. Hardware is limited to what is necessary for the applications to run, and does not include unnecessary components such as peripheral connectors or video cards. We also optimize our servers and racks to use minimal fan power, and the fans are controlled to spin only as fast as necessary to keep the server temperature below a threshold.”
  27. 27. 27 Cascade Effect
  28. 28. 28 Facebook  Facebook recently adopted a novel power distribution design that removes uninterruptible power supply (UPS) and power distribution units (PDUs) from the data center.  The new design shifts the UPS and battery backup functions from the data center by adding 12 volt battery cabinets  Facebook’s datacenter has a PUE of 1.07
  29. 29. 29 Role of Software  ”(T)he only way to have software consume zero resources is not to run it at all. Even running very well-behaved software at the minimum will, in practice, require some resource overhead.”        Virtualization Power Management Node Management Scheduling Lean Operating systems Software development De-duplication
  30. 30. 30 Virtualization  Virtualization is useful for consolidation of under utilized servers   DO: consolidate servers that typically have usage under 70% DO NOT: consolidate servers that have usage higher than 90%  Unless the server resources are more than enough to cover usage patterns including the other virtualized servers consolidated into the new server  WARNING: Consolidating several high usage servers on one server may be a recipe for disaster.
  31. 31. 31 Virtualization  Remember: Virtualization can lead to the need to rework cooling in the data center.  Fewer machines could lead to unnecessary cooling of space no longer occupied by servers. This could result in wasted cooling.
  32. 32. 32 Lean Operating systems  Graphical efficient    1 watt can be saved at the server level though using a command line only server operating system The overhead of the GUI is about 100 threads This does not mean no Windows server OS  Windows  User Interfaces (GUI) are not server 2008 R2 Core Uses less disk space  Standard 7.5 GB  Core 3.5 GB
  33. 33. 33 Software Development  CPU    UTILIZATION Write event-triggered not time-based checks Use batch processing for processes that cannot be event based Make sure batch jobs can be adjusted for time conflicts  MEMORY   UTILIZATION Avoid memory leaks Release memory when it is no longer needed, don’t wait for the system to do it foru you.
  34. 34. 34 Software Development  I/O  UTILIZATION Buffer/batch I/O requests  EFFICIENT   SYSTEM STACK “Features to reduce power consumption of underutilized system resources have become pervasive in even the largest systems, and the software layers responsible for managing those resources must evolve in turn” Implement policies:  Increase performance of resources in use  Reduce power consumption for resources not in use
  35. 35. 35 Software Development (Continued)  “Resource consumers clearly have a significant opportunity either to contribute to or undermine the efficiency of the broader stack. Though getting programmers to think differently about the way they design software is more than a technical problem, tools such as PowerTOP represent a great first step by providing programmers and administrators with observability into software inefficiency, a point of reference for optimization, and awareness of the important role software plays in energy-efficient computing."
  36. 36. 36 Hardware  What is better one really powerful server of several low powered servers?  Answer: It depends.  Many low powered servers are ideal for transactional loads  Powerful servers are better for simulation and computation
  37. 37. 37 Conclusion  "Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius -- and a lot of courage -- to move in the opposite direction.” ~Albert Einstein  In IT this kind of thinking results in huge payoffs when implemented correctly.