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Cyber-Physical Systems for Sustainability

Cyber-Physical Systems for Sustainability. Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University. Research Objective. Energy. Address challenges of sustainability by advancing interdisciplinary research. Sustainability. Environment.

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Cyber-Physical Systems for Sustainability

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  1. Cyber-Physical Systems for Sustainability Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University

  2. Research Objective Energy Address challenges of sustainability by advancing interdisciplinary research Sustainability Environment Healthcare Hazards

  3. Cyber-Physical Systems • “Cyber-physical systems are engineered systems that are built from and depend upon the synergy of computational and physical components”1 • Many critical sustainability application domains • Environment, smart grid, medical, auto, transportation… • # 1 national priority for Networking and IT Research and Development (NITRD) • NITRD Review report by President's Council of Advisors on Science and Technology (PCAST) titled “Leadership Under Challenge: Information Technology R&D in a Competitive World”, 2007 1 NSF Cyber-physical systems solicitation13502

  4. Our CPS Projects • Data center thermal monitoring • Residential electricity usage profiling • Real-time volcano monitoring • Aquatic process profiling  Tungurahua Volcano, Ecuador Harmful Algae Bloom in Lake Mendota in Wisconsin, 1999 Data Center Monitoring, HPCC, MSU Volcano Monitoring Sensors Robotic fish, Smart Microsystems Lab, MSU

  5. Motivation • Data centers are critical computing infrastructure • 509,147 data centers world wide, 285 million sq. ft.1 • 2.8M hours of downtime, 142 billions direct loss/year1 • 23% server outages are heat-induced shutdowns An aerial view of EMC's new data center in Durham, North Carolina2 An EMC data center 2 1Emerson Network Power, State of the Data Centers 2011, 2http://www.datacenterknowledge.com/archives/2011/09/15/emc-opens-new-cloud-data-center-in-nc/.

  6. Motivation • Many data centers are overcooled • Low AC set-points, high server fan speeds • Excessive cooling energy • up to 50% or more of total power consumption • Rapid increase of energy use in data centers • From 2005 to 2010, electricity use in data centers grew 36% (US) and 56% (world wide)1 • An estimated 2% of electricity budget of US1 1Jonathan G. Koomey, “Grouth in data center electricity use 2005 to 2010”, Analytics Press, 2011.

  7. Temperature Forecasting • Predict server temperature evolution • Identify potential hot spots • Enable high CRAC set-points for energy saving • Temperature at inlets/outlets indicates hotspots cool air hot air Inlets Outlets

  8. Challenges • Complex air and thermal dynamics • Highly dynamic workloads • Physical failures • ACs, servers, fans Row 2 Server exhaust Raised-floor cold air Row 1 12-day CPU utilization data of one rack (64 servers with 512 CPU cores) in High Performance Computer Center at Michigan State University

  9. System Architecture • CFD + Wireless Sensing + Data-driven Prediction • Preserve realistic physical characteristics in training data • Capture dynamics by in situ sensing and real-time prediction Data Center Sensing (CPU, fan speed, temperature, airflow) geometric model (server/rack dimension and placement) CFD Modeling Real-time Prediction Calibration

  10. Data Center Experiment Chained Temp. sensor • Testbed configuration • 5 racks, 229 servers, 2016 cores • 4 in-row CRAC units • 35 temperature sensors • 4 airflow sensors • Dynamic CPU utilization In-row CRACs In-row CRACs Airflow sensor Temperature sensor

  11. Experiment Results • 12-day experiment Outlet Inlet 10-minute temperature prediction

  12. Outline • Data center thermal monitoring • Residential electricity usage profiling • Real-time volcano monitoring • Aquatic process profiling

  13. Residential Electricity in U.S. • Residential electricity • Largest sector • Rising cost • Increase by 75% in 10 years • Understanding usage • Real-time power readings • Fine-grained usage info Industrial 25.5% Residential 36.7% Others Commercial 34.2% Electricity retail sales in U.S. 2011 [US EIA-861, EIA-923]

  14. Supero Smart meter Base station ‘+1’ Event-Appliance Association 100W Event clustering Event Correlation (remove false alarm) Light and acoustic sensors Light + acoustic captures 90% power consumption Light/acoustic event Power reading

  15. Implementation & Deployments • System • TelosB/Iris + TED5000 + KAW ground truth meters • Five deployments • Three apartments (40~150 m2), two houses • 9 ~ 22 sensors TelosB (light) Iris (acoustic) Kill-A-Watt Apartment-1 deployment

  16. 10-day Results • Supero • All 146 light events detected, no false alarm, no miss • Comparable to Oracle

  17. Outline • Data center thermal monitoring • Residential electricity usage profiling • Real-time volcano monitoring • Aquatic process profiling

  18. Volcano Hazards • 7% world population live near active volcanoes • 20 - 30 explosive eruptions/year Eruptions in Iceland 2010 A week-long airspace closure [Wikipedia] Eruption in Chile, 6/4, 2011 $68 M instant damage, $2.4 B future relief. www.boston.com/bigpicture/2011/06/volcano_erupts_in_chile.html

  19. Volcano Monitoring • Seismic activity monitoring • Earthquake localization, tomography, early warning etc. • Traditional seismometer • Expensive (~$10K/unit), difficult to install & retrieve • Only ~10 nodes installed for most threatening volcanoes! Photo credit: USGS, http://volcanoes.usgs.gov/activity/methods/

  20. VolcanoSRI Project • Large-scale, long-term deployment • Up to 500 nodes on an active volcano in Ecuador • Sampling@100Hz, several month lifetime • Collaborative in-network processing • Detection, timing, localization • 4D tomography computation • The tentative deployment map at Ecuador • (Photo credits: Prof. Jonathan Lees)

  21. Current Work • Smartphone-based sensing platform • Distributed earthquake detection/timing algorithms • Field deployment in 2012 in Tungurahua, Ecuador

  22. Aquatic Environment Monitoring • Monitoring aquatic ecosystems is critical for urban planning, clean water, etc. • Traditional approaches • Boats, sea sliders, etc. • Our approach • Robotic fish, collaborative sensing and actuation Robotic fish Boat sensing HABs in a lake photo credits: Prof. E. Litchman and Prof. Xiaobo Tan

  23. Representative Publications • Nemo: A High-fidelity Noninvasive Power Meter System for Wireless Sensor Networks, The 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), acceptance ratio: 24/115=21%, SPOTS Best Paper Award. • Supero: A Sensor System for Unsupervised Residential Power Usage Monitoring, 11th IEEE International Conference on Pervasive Computing and Communications (PerCom), 2013, acceptance ratio: 18/170 = 10.6%, Best Paper Award Runner-up. • Beyond Co-existence: Exploiting WiFi White Space for ZigBee Performance Assurance, The 18th IEEE International Conference on Network Protocols (ICNP), Kyoto, Japan, October 5-8, 2010, acceptance ratio: 31/170 = 18.2%, Best Paper Award. • Passive Interference Measurement in Wireless Sensor Networks, The 18th IEEE International Conference on Network Protocols (ICNP), Kyoto, Japan, October 5-8, 2010, acceptance ratio: 31/170 = 18.2%, Best Paper Candidate (6 out of 170 submissions). • Volcanic Earthquake Timing using Wireless Sensor Networks, The 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), acceptance ratio: 24/115=21%. • Quality-driven Volcanic Earthquake Detection using Wireless Sensor Networks, The 31st IEEE Real-Time Systems Symposium (RTSS), November 30 - December 3, 2010, San Diego, CA, USA. • Fidelity-Aware Utilization Control for Cyber-Physical Surveillance Systems, The 31st IEEE Real-Time Systems Symposium (RTSS), November 30 - December 3, 2010, San Diego, CA, USA. • ZiFi: Wireless LAN Discovery via ZigBee Interference Signatures, The 16th Annual International Conference on Mobile Computing and Networking (MobiCom), Chicago, USA, September 2010, acceptance ratio: 33/233=14.2%. • Negotiate Power and Performance in the Reality of RFID Systems, The 8th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), 2010, acceptance ratio: 27/227=12%, Best Paper Candidate (3 out of 227 submissions) . • Adaptive Calibration for Fusion-based Wireless Sensor Networks, The 29th Conference on Computer Communications (INFOCOM), March 15-19, 2010, San Diego, CA, USA, acceptance ratio: 276/1575=17.5%. • Total number of citations since 2003: 3,800

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