A Cyber Physical Approach to

Combined HW-SW Monitoring for

Improving Energy Efficiency in

Data Centers

Josué Pagán, Marina Zapater, Oscar Cubo, Patricia Arroba, Vicente Martín and José M. Moya

Universidad Politécnica de Madrid1 / 20

Contents

1. Power consumption problem in Data Centers

I. Introduction

II. Related work

2. Optimization Framework & Data analysis

I. A Cyber-physical system

II. Data analysis and sensor configuration

3. Results

4. Conclusions

2

1. POWER CONSUMPTION

PROBLEMS IN DATA CENTERS

Data Center. Consumption

3

1. Power consumption

problems in Data Centers

• The numbers of the energy problem:– DC world power consumption >1.3%

– In urban areas >50% of DC exceeds power grid capacity

– USA: 80 TWh/year in 2011 = 1.5 x NY

•

4Projection of total electricity use by datacenters in the US and the world based on Koomey’s and EPA’s data

Power >600 TWhr expected in 2015 in the global footprint

Data Centers’ power consumption is unsustainable

5

Cooling

• Allow the room temperature to increase

• Longer task → cooler server

Computation

• Balancing workload between servers

• Reducing voltage/ frequency (DVFS)

Holistic(IT+cooling)

• Room environment affects (environmental monitoring)

• Measuring server, workload and environmental variables to improve energy efficiency → usage of a CPS

1. Power consumption

problems in Data Centers

• Related work (approaches)

– These two approaches are not enough individually

1. Consumption problems in

Data Centers

6

Requirements Our contributions

Energy optimization

Make a holistic optimization framework including environmental,

server and workload information

Dynamically adapt on runtime to workload and environment

Gather, monitor and analyze in real time

Gather useful data at the appropriate rate

In a non-intrusive way, reducing the data collected with an adaptable

sampling rate

2. OPTIMIZATION FRAMEWORK

& DATA ANALYSIS

Cyber-Physical System. Data acquisition

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• One step ahead. Optimization– 80% Wpeak – 30% of workload (↓η)

– An energy model supposes apply optimizations over the Data Center

2. Optimization Framework &

Data analysis

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GATHERDATA

GENERATEKNOWLEDGE

PROPOSEOPTIMIZATIONS

• Monitoring– How a Data Center works?

– 30-50% cooling→ energy optimization 9

2. Optimization Framework &

Data analysis

• What measure and why– Environmental monitoring

Inlet and outlet temperature

Differential pressure

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2. Optimization Framework &

Data analysis

– Server monitoringServer consumption, CPU temperature, fan speed

• …to predict

• How…

• exploring sampling intervals

2. Optimization Framework &

Data analysis

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– Different sampling rates for different parameters

– Temperature and power values

for AMD server under the

benchmark SPEC CPU 2006

• Using… Multilevel star topology architecture

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Air conditioning- Exhaust

temperature, RH% and airflow

WSN- Reconfigurable low -power:

only useful data without information loss

- Adapt to changes in the environment

RM- Spatio-temporal allocation

- Possibly to change decisions if needed

Server Sensors- Internal sensors

- Polled via SW

Gateway-Fan-less, managed with a light OS

-Receive, store, analyze and convert data. Establishes a timestamp.

-Sends data to the opt. platform

2. Optimization Framework &

Data analysis

3. RESULTS

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3. Results

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WSN deployment• Applied over Magerit Supercomputer in CeSViMa Supercomputing and Visualization Center

of Madrid• Cluster 9 racks 260 servers

3. Results

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– The goal: develop techniques to allow energy optimization in real environments

– With reconfigurable sampling rate:

– we achieve up to 68 % of reduction in gathered data

– Increase the WSN’ s life time depending on the occupancy

4. CONCLUSIONS

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4. Conclusions

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Energy efficiency has to be faced in a holistic way

We propose an optimization framework monitoring environmental, server and workload parameters

After a first monitoring study: a WSN has been deployed to gather environmental data

Up to a 68% of reduction in the amount of gathered data

Maximizing the life time of WSN nodes

Solution applied in a real case study

FIN

This project has been funded in part by the INNPACTO \ LPCLOUD: "Optimal Management Of low-power modes in cloud computing" IPT-2012-1041-430000, developed in collaboration with Elite Ermestel and Converging Technologies and the CDTI project \ CALEO:

Distribution of operational thermal and optimization of energy consumption in data centers, "developed in collaboration with INCOTEC. The author gratefully acknowledges the computer resources, technical expertise and assistance provided by the Supercomputing and

Visualization Center of Madrid (CeSViMa). 18

Thank you for your attention

4. Results and Conclusions

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• Results: gathering data• Inlet and outlet temperature

Magerit Supercomputer

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• Cluster 9 racks 260 servers 245 are IBM PS702 2S

o 16 Power7 processors @ 3.3 GHz

o 32 GB of RAM

15 are IBM HS22

o 8 Intel Xeon processors @ 2.5 GHz

o 96 GB of RAM

200 TB of storage

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Industry

Software

22

• Pasarela

Cyber-Physical System

23

Psychrometric chart

24

Differential pressure and

airflow

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Installing nodes [6T+1H]

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