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A Low-cost and Scalable Visualization System for Electricity Consumption
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A Low-cost and Scalable Visualization System for Electricity Consumption


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AIST booth presentation slides at SC2011.

AIST booth presentation slides at SC2011.

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  • 1. A Low-cost and Scalable Visualization System for Electricity Consumption Ryousei Takano Information Technology Research Institute,National Institute of Advanced Industrial Science and Technology (AIST), Japan SC2011@Seattle, Nov. 17 2011
  • 2. Outline •  Background•  System overview•  Demonstration at SC11•  Summary 2
  • 3. Background •  The power consumption of data centers and networks becomes an issue of vital importance to IT industries. Google data center in the Dalles, Oregon •  In Japan, we faced planed power outages (rolling power outages) due to power shortages caused by 3.11. Fukushima Daiichi Nuclear Power Plant 3
  • 4. Motivation •  To plan electricity savings, visualization of electricity consumption per segmented unit, such as a power distribution board, is becoming essential.•  But, the total system cost and scalability are problem. –  a lot of distributed sensors...•  The system has to be low-cost, scalable, and ease to develop applications.   Cheap power measuring hardware unit   Power logging software utilizing cloud computing   A simple REST protocol 4
  • 5. System overview Data collecting unit Data collecting unit 2 Data collecting unit … … 3 Data collecting server Update power usage Google App Engine using REST w/ JSON Retrieve data using Datastore REST w/ JSON Alert 4 Applications Viewer 1 Data measuring unit Observe the state of power consumption Plan electricity saving 5
  • 6. Small start Go big Google App Engine Datastore 4 sensors 32 ports = 128 sensors Datastore Sensors can be incrementally installed, GAE can scale up according to the workload. ... 6
  • 7. Low-cost power measuring unit •  Send data to data collecting unit every second. •  The production cost is approximately 120 USD, including the cost of 4 current sensors.Clamp-on current sensor (max: 4) RJ-45 port Signal processing board (dsPIC30F3013) 7
  • 8. Data collecting unit (1/2) •  Gather data from up to 32 power measuring unit•  Push data to GAE –  Can be placed behind NAT To power measuring unit To GAE via the Internet (Not Ethernet, data transfer and power supply) (Ethernet/100BaseT) 8
  • 9. Data collecting unit (2/2) Power GAE measuring unit RJ-45 ports x 32 CPU (SH3) board- Linux + Python Serial-Parallel signal transform (Now, it has been replaced by FPGA.) 9
  • 10. Google App Engine •  PaaS cloud service for web applications –  Java, Python, Go are supported –  Your application will have URL like•  Scalable and stable data storage –  Data are replicated to 5 different datacenters –  Allows 2 of them to be lost during operation•  Maintenance free –  No need to manage, almost•  Almost free, for now –  Price change is scheduled this Dec. though. 10
  • 11. REST API path method description /update POST Upload data /latest GET Get all data for the last minute /latest,N GET Get all data for the last N minutes /summary.s/YYYYmmDDHHMMSS,N GET Get all data for each second start from YYYYmmDDHHMMSS, for N seconds /summary.m/YYYYmmDDHHMM,N GET Get all data for each minute start from YYYYmmDDHHMM, for N minutes /summary.h/YYYYmmDDHH,N GET Get all data for each hour start from YYYYmmDDHH, for N hours /summary.d/YYYYmmDD,N GET Get all data for each day start from YYYYmmDD, for N days /query.s/LOC/YYYYmmDDHHMMSS,N GET Get data for locations that name start with LOC /query.m/LOC/YYYYmmDDHHMM,N GET /unit-config/UNIT_ID GET Get configuration data /unit-config/UNIT_ID PUT Set configuration data 11
  • 12. Update from Data collecting unit Each data collection unit sends GAE data every 20 seconds –  POST the following JSON string { "id": "UNIT_ID" "time": "1319837460” /* elapsed seconds from the UNIX epoch time */ "power": { /* data for the last 20 seconds per measurement point */ "sensor0.0": [VAL0, VAL1, VAL2, VA3, ..., VAL19], "sensor0.1": [VAL0, VAL1, VAL2, VA3, ..., VAL19], "sensor1.0": [VAL0, VAL1, VAL2, VA3, ..., VAL19], .... } } 12
  • 13. Data retrieval Applications periodically (e.g., 1 GAE,N min) get data from GAE –  GET the following JSON string { "time": “1319837460” /* epoch time */ "timeStr": “201110290631” /* human readabletime in JST */ "power": { "LOCATION0": [1234] "LOCATION1": [1234] "LOCATION2": [1234] "LOCATION3": [1234] "LOCATION4": [1234] ... Viewer application }} 13
  • 14. Demonstration at SC11 AIST (JP) 7 data collecting units Data collecting 82 sensor modules 1 gather unit Data collecting 290 measurement points unit 2 tore 1 … 3 view Sensor module … Clamp-on current transformer 2 Google App Engine Update power usage using REST w/ JSON Retrieve data using Datastore REST w/ JSON NICT booth #4717(US) 2 3 Data collection Viewer unit 1 Viewer 2 sensor built-in power strips AIST booth #917(US) 14
  • 15. AIST server room (Japan) Data collecting unit in free access floor Clamp-on current sensor GAE Power distribution board Power measuring unit 15
  • 16. NICT booth #4717 (US) 2 sensor modules (sensor built-in power strip) Tiled display wall server Data collecting module Juniper’s routers GAE 16
  • 17. Visualization App. AIST server room (ACS cluster) at Japan NICT booth at the US 17
  • 18. Summary •  Our newly developed system helps reduce total system cost and improve scalability by employing low-cost power measuring units (30 USD per measurement point), and utilizing cloud computing. –  The development of the system was completed within 3 months. –  At SC11, we have successfully demonstrated visualization of power data gathering from sensors installed at the US and Japan.•  Future work: Incorporate into reactive/dynamic server consolidation on a data center to reduce power consumption aggressively. 18
  • 19. Q&A Thanks for your attention! Thank NICT members for their help in the demonstration.""This research was partially supported by the NEDO researchproject entitled “Research and Development Project for GreenNetwork/System Technology (Green IT Project).”" 19
  • 20. Demo: GAE dashboard 0.75 HTTP requests/second 5000 bytes received/second 20
  • 21. Visualization Applications less than 90% of the upper limit less than 95% of the upper limit more than 95% of the upper limit Offline (a) Web application (b) Desktop application 21