Cyber-Physical Systems for Sustainability

Guoliang Xing

Assistant ProfessorDepartment of Computer Science and Engineering

Michigan State University

Research Objective

Address challenges of sustainability by advancing interdisciplinary research 2

Healthcare

HazardsEnergy

Environment Sustainability

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 solicitation135023

Our CPS Projects

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

Robotic fish, Smart Microsystems Lab, MSU

 Tungurahua Volcano, Ecuador

Volcano Monitoring Sensors

Data Center Monitoring, HPCC, MSU

Harmful Algae Bloom in Lake Mendota in Wisconsin, 1999

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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/. 5

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. 6

Temperature Forecasting

• Predict server temperature evolution– Identify potential hot spots– Enable high CRAC set-points for energy saving

• Temperature at inlets/outlets indicates hotspots

Inlets Outlets 7

cool air hot air

Challenges

• Complex air and thermal dynamics

• Highly dynamic workloads

• Physical failures – ACs, servers, fans

Row 1

Row 2

Raised-floor cold air

Server exhaust

12-day CPU utilization data of one rack (64 servers with 512 CPU cores) in High Performance Computer Center at Michigan State University

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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

Calibration

Sensing(CPU, fan speed, temperature, airflow)

Real-time Prediction

CFD Modeling

geometric model (server/rack dimension and placement)

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Data Center Experiment

• Testbed configuration– 5 racks, 229 servers, 2016 cores– 4 in-row CRAC units– 35 temperature sensors– 4 airflow sensors

• Dynamic CPU utilization

Airflow sensor

Temperature sensor

Chained Temp. sensor

In-row CRACs

In-row CRACs

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Experiment Results

• 12-day experimentOutlet

Inlet

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10-minute temperature prediction

Outline

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

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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

Industrial25.5%

Residential36.7%

Commercial34.2%

Others

Electricity retail sales in U.S. 2011

[US EIA-861, EIA-923]Appl. Joul % When?

Bed light 5% 7pm-11pm

Fridge 8% Every 1h

Space heater

30% Jan 1 …

…. …. ….

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SuperoSmart meter

Light and acoustic sensors

Base station

Event Correlation(remove false alarm)

Event clustering

Event-Appliance Association

100W

‘+1’

Light/acoustic event Power reading14 / 23

Light + acoustic captures90% power consumption

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

15 / 23

10-day Results

• Supero– All 146 light events detected, no false alarm, no miss– Comparable to Oracle

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Appliance Supero Oracle BaselinekWh Error (%) kWh Error (%) kWh Error (%)

Light 1 4.17 0.5 4.11 0.9 4.11 0.9Light 2 4.96 0.1 4.92 0.8 4.92 0.8Light 3 6.24 1.4 6.25 1.7 6.25 1.7Light 4 1.45 0.1 1.45 0.1 1.48 1.7Light 5 0.39 0.2 0.39 0.7 0.41 5.5

Water boiler 0.48 0.5 0.48 0.5 0 100Tower fan 0.21 50 0.17 17.9 0.24 66.2

Rice cooker 0.98 2.2 1.01 1.2 1.01 0.8Hair dryer 0.07 19.2 0.09 0.4 0.02 73.2

Fridge 11.8 3.7 11.8 3.2 11.8 3.2Bath fan 0.12 N/A 0.17 N/A 0 N/ARouter 2.03 4.3 3.04 43.3 3.04 43.3

Average error 7.5 6.5 27.0

Outline

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

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Volcano Hazards

• 7% world population live near active volcanoes• 20 - 30 explosive eruptions/year

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

Eruptions in Iceland 2010A week-long airspace closure[Wikipedia]

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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/ 19

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) 20

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

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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 fishHABs in a lake Boat sensingphoto credits: Prof. E. Litchman and Prof. Xiaobo Tan

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