Green Wireless Technology Panel Presentation

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Green Wireless Technology Panel Presentation

Professor Sandeep K. S. Gupta

p

IMPACT

(Intelligent Mobile Pervasive Autonomic Computing & Technologies) LAB

(

http://impact.asu.edu

)

School of Computing, Informatics, Decision Systems Engineering

Arizona State University

Sandeep Gupta, IEEE Senior Member

• Heads

Use-inspired, Human-centric research in distributed cyber-physical systems

@

School of Computing & Informatics

Use inspired, Human centric research in distributed cyber physical systems

Pervasive Health Monitoring

Thermal Management for Data Centers

Criticality Aware-Systems ID Assurance Intelligent Container Mobile Ad-hoc Networks

BEST PAPER AWARD: Security Solutions for Pervasive HealthCare – ICISIP 2006

BOOK: Fundamentals of Mobile and Pervasive Computing, Publisher: McGraw-Hill Dec. 2004

HealthCare ICISIP 2006.

• TCP Chair

•TCP Co-Chair:

GreenCom’07

• Area Editor

Also for IEEE TPDS WINET

Email:

;

IMPACT Lab URL: http://impact.asu.edu;

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Two

extreme examples of

Greenness

1. Embedded networks, e.g. Body Area Networks (BANs), consisting

of embedded and inter‐communicating sensor devices

EKG EEG BP

g

– Constrained resources • Principally battery powered with limited available energy – Sustainabilityis the main issue • Un‐interrupted operations, e.g. sensing & communication, for long periods Required Approaches SpO2 BP Base Station – Required Approaches • Energy‐efficient algorithm design • Scavenging energy from the environment (e.g. human body) Motion Sensor Body Area Network (BAN)

2. Large‐scale networked systems, e.g. data centers, integrating a

large number of computing resources for service provisioning

– Power density increases ( ) • Circuit density increases by a factor of 3 every 2 years • Energy efficiency increases by a factor of 2 every 2 years • Effective power density increases by a factor of 1.5 every 2 years [Keneth Brill: The Invisible Crisis in the Data Center] – Total Cost of Ownership (TCO)rising

• Data Center TCO doubles every three yearsData Center TCO doubles every three years

• Cooling the data center can cost up to half of the total electricity bill • Power Usage Efficiency has to be reduced [Uptime Institute] – Required Approaches • Energy‐efficient thermal‐aware resource management C di t d t f ti d li • Coordinated management of computing and cooling resources Data Center

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Green Data Centers

• System Requirements

– Equipment Safety

• Equipment operating temperature should be within a manufacturer‐specified redline temperature

– Service Level Agreements (SLAs)

• The throughput and turn‐around time should Typical Data Center Design g p meet the user requirements

• Energy Issues

– Computing Energy: Trade‐off with meeting the SLAs Cooling Energ Trade off ith eq ipment safet – Cooling Energy: Trade‐off with equipment safety • Stems from the data center thermal‐issues

• Thermal‐ Issues

–

Heat recirculation

Green data centers call for

Coordinated

Thermal‐aware resource management

to reduce cooling and computing

Heat recirculation

• Hot air from the equipment air outlets is fed back to the equipment air inlets –

Hot spots

Eff t f H t R i l ti

_{energy consumption while meeting the}

equipment safety and SLAs

• Effect of Heat Recirculation • Areas in the data center with alarmingly high temperature –

Consequence

• Cooling has to be set very low to have all inlet temperatures within the redline for safety

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Ecosystem of Datacenters

Different task assignments lead to different power

consumption distributions

Different power consumption distributions lead to

different temperature distributions

Different temperature distributions lead to different total

energy costs

80 100 3000 3500 100

gy

1 2 3 4 5 0 20 40 60 1 2 S3 0 500 1000 1500 2000 2500 1 2 S2 S3 0 20 40 60 80 5 6 7 8 9 10 11 12 S1 S3 S5 2 3 4 5 S1 S2 2 3 4 S1

Server load

Power consumption

_Temperature

7

Server load

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Coordinated Resource Management for Green Data Centers

Energy reduction in data centers has three

Energy‐reduction in data centers has three

directions

1. Thermal‐aware workload management • Schedule and place jobs such that low power servers having less impact on the cooling demand are used 2. Server Power Management • Operate under‐utilized servers at low power states (e.g. turn‐off idle servers) 3. Cooling Management

• Dynamically set the cooling to the highest temperaturesDynamically set the cooling to the highest temperatures that meets the equipment safety

There is a spatio‐temporal workload schedule that minimizes the total energy (cooling + computing) demand. Find it and perform dynamic cooling

management and server power management with it to

Energy Savings Over Current Practice

minimize the total energy consumption while meeting the SLAs and equipment safety!

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Online Data Center Thermal Management

Data Center-Level Thermal Models

To enable on-line real-time thermal-aware job scheduling

Power Characterization

Characterize the power consumption of

a given workload (CPU, memory, disk

• fast (analytical, non CFD based) • non-evasive (machine-learning)

g

(

,

y,

etc) on a given server machine

Model the thermal impact of

Thermal Management Infrastructure

multicore systems Sensor Data Gathering Service Data Center Monitoring Performance Monitoring Service & Services for Data Centers

Thermal-aware

Job Scheduling

On-line job scheduling algorithm to minimize peak air inlet

Non-Invasive Thermal Evaluation Fast Thermal Evaluation Service Thermal/Power & Performance Correlation Service

Resource & to minimize peak air inlet

temperature, thus minimizing the cost of cooling. Policy Enforcement Thermal Management Policy Enforcement Service Thermal Control Policies Resource & Server Management OS-Level Services Performance Monitoring

Service Control Policies

PI: Sandeep K. S. Gupta [email protected]

Job Scheduling Service

Cluster

Management Resource Job Queues

Queues Cooling Control Service Air-flow Control Service Facility Management

http://impact.asu.edu

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Green

Body Area Networks (BANs)

• System Requirements

– Sustainability

• Uninterrupted BAN operations for long periods

EEG Sensors Uninterrupted BAN operations for long periods essential for medical care

– Operational Safety

• Side‐effects of BAN operations (e.g. heat SpO2 EKG BP Base S i Base Station dissipation) should be within a threshold

– Security

• Sensitive medical data collection should be secure as per legal requirements (HIPAA)

Station Motion Typical BAN Design secure as per legal requirements (HIPAA) • Secure wireless inter‐sensor communication is essential

• Energy Issues

Sensor

Green BANs call for

energy analysis of

the computing and communication

ti

d

il bilit

f

gy

– Computing and Communication Energy: Trade‐off with security and sustainability • Limited battery power

S i f th h b d i ti l

_operations

_and

_{availability of energy}

for potential scavenging

to ensure

sustainability and security.

– Scavenging energy from the human body is essential • Security adds computation and communication overhead – Energy analysis of security primitives important – Reduced power consumption in the sensors can enable safety by reducing potential heat dissipation

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Sustainable Physiological Value‐based Security for BANs

h i l i l i

l b

d

Ph i l i l i l Ph i l i l i l

• Physiological signal based Key

Agreement (PKA)

– Perform key agreement by combining

f f Physiological signal Physiological signal features key features k

y g

y

g

cryptographic primitives with signal

processing

Sender Receiver

hide Un‐hide key PKA P P fil (R di ON V l Si 5000) 0 04 0.05 0.06

PKA Power Profile (Radio-ON, Vault Size = 5000)

Receiver(Radio ON) 0.02 0.03 0.04 _{Sender(Radio ON)} Scavenging Technique Source Power Gain

• The max power required by PKA

1 2 3 4 5 6 7 8 9

0.01

Sensing FFT Peak + Quant

Poly Gen +

Eval Add Chaff Vault Tx/Rx

Lagrangian Interpolation Ackn Tx/Rx q Body Heat Latent heat of vaporization of perspiration 200mW – 320mW

Respiration Chest Expansion while ~420mW

The max power required by PKA

(58 mW) low enough to be

sustained by prominent energy

scavenging techniques

Respiration Chest Expansion while breathing

420mW Ambulation Arm & Leg Movement 1.W‐1.6W Photovoltaic Cells Photovoltaic Cells 100mW/cm2

scavenging techniques

Venkatasubramanian et al Green and Sustainable Security Solutions for BANs, BSN’08

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Safety: Minimize Tissue Heating

• Medical sensors implanted/worn by

Medical sensors implanted/worn by

human need to be safe.

• Sensor activity causes heating in the

tissue.

H

i

d b RF i d

i

– Heating caused by RF inductive

powering

– Radiation from wireless

communication

Tissue Blow-up

– Power dissipation of circuitry

• Goal:

minimize tissue heating.

• Two solutions:

Communication scheduling

for

– Communication scheduling

for

minimizing thermal effects:

• Rotate cluster leader – balance energy usage + distribute heat dissipation _{H ti} dissipation

– Thermal aware routing:

route

around thermal hotspots

Cluster leader Heating Zone

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BSN Scheduling

Medium 1(free space) ε1,µ1, σ1

Medium 2(Body tissue) ε2,µ2, σ2

g

Requirement

SAR = σ E

2

_{/ ρ (W/kg)}

E = induced Electric Field

Incident Plane Wave with power P0

Transmitted Wave Cluster Leader RF Powering Source depth d

System Model

• Consider only one cluster • 2D Model

• FCC

Regulation

IEEE Requirement (1g Tissue)

E induced Electric Field Ρ = tissue density

σ = electric conductivity of tissue Reflected Wave Control Volume and

a cluster of biosensors

• 2D Model • Rotate cluster

head - dist.

energy consump.

d h ti Temperature Rise: Pennes

Heat by metabolism SAR = 0.4W/Kg Whole Body Average SAR = 8W/Kg

Peak Local reduce heating Temperature Rise: Pennes

Bio-heat Equation

Heat

accumulated bHeat transfer d ti

Heat by

radiation Heat transferb ti

Heat by power dissipation SAR = .08W/Kg Whole Body Average SAR = 1.6W/Kg Peak Local CE UCE metabolism accumulated by conduction radiation by convection dissipation

Solution

• Random selection may lead to higher

1 2 5 1 2 5 1 2 5 Results FDTD + enumeration FDTD + Genetic Algorithm Optimal Near Optimal 720960 hrs (est.) 100 hrs (est.) temperature rise • Similar to Traveling

salesman problem but with dynamic metric

3

4 4 3 4 3

(a) Ideal Rotation (b) Nearest Rotation (c) Farthest Rotation

Four Approaches

• FDTD + enumeration Ge e c go TSP + Genetic Algorithm TSP +enumeration Optimal Near Optimal Near Optimal 100 hrs (est.) 7.6 hrs 5 min ure

Temp rise in sensor surroundings 0.11 Temperature Comparative Result • Heuristic: Leader selection based on sensor location, rotation history • FDTD + Genetic Algorithm • TSP + enumeration • TSP +Genetic T emperat u surroundings 0.07 0.08 0.09 0.1 Temperature Rise ° C Temperature Mean ± Deviation Mean TSP +Genetic Algorithm

Q. Tang, N. Tummala, S. K. S. Gupta, and L. Schwiebert, Communication scheduling to minimize

thermal effects of implanted biosensor networks in homogeneous tissue, Proc of IEEE

Transactions of Biomedical Engineering

Worst Dynamic Manual Genetic Optimal 0.06

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