data center modeling 101 - bicsi · data center modeling 101 moises levy, phd. modeling physical...
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Data Center Modeling 101
Moises Levy, [email protected]
www.dcmetrix.com
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Do we really understand how a data center behaves?
Workloads ?
Physical environment ?
IT Equipment specs ?
Quality of Service ?
Power and Airflow requirements ?
Key Performance Indicators ?
Data Center Modeling 101
Moises Levy, PhD
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Ernest Orlando Lawrence Berkeley National LaboratoryU.S. Data Center Energy Usage Report, June 2016
o Energy intensive
o ITE > 1 kW/m2
o U.S. ~ 3 M data centers
o ~2% electricity consumption
o 2020: ~73 billion kWh
o Downtime $$$
It is important to model data centers
Data Center Modeling 101
Moises Levy, PhD
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Cyber physical system:
Integration of computational and physical components
Data centers modeled as CPS
Workload
Energy
Physical environment
At a data center:
High coupling between ITE and their physical environment
Data Center Modeling 101
Moises Levy, PhD
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Data center model
Simple
Correct
Useful
QoS, Power, Airflow, Energy, KPIs
ITE and cooling specs
Workloads
Data Center Modeling 101
Moises Levy, PhD
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Steps for modeling data centers as CPS
1. Modelingcyber components
2. Modelingphysical components
3. Key indicators
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Cyber components
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2
Data Center Modeling 101
Moises Levy, PhD
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ITE specs : , ,
Modeling cyber components
Win , = Win,DC * S ,Data Center Modeling 101
Moises Levy, PhD
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o ITE resource utilization: U , = Wout ,PRo Queue length:
L , = Win , + L , 1 - Wout ,o Waiting time: tw = L ,PR o Total processing time …
Parameters to predict QoS
Modeling cyber components
Quality of service
Processing in real time System overloaded
Wout , = Win ,No queue
Wout , = PRData Center Modeling 101
Moises Levy, PhD
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, = ∗ , + , = , ∗
ITE specs: , ,
Modeling cyber components
Power
ITE Power requirement
ITE Energy consumption
Data Center Modeling 101
Moises Levy, PhD
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Modeling cyber components
Power
The power required by ITE depends on the workload and QoS
No workload
Power (idle)
Workload QoSPower
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Physical components
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2
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
= Cp * ρ * Airflow * ∆T
Cp: Specific heat of airρ: Density of air
AirflowCFM = 3.2 * ∆ °
Modeling physical components
Airflow
Cyber and thermal components are coupled throughthe energy consumption of the ITE
The convective heat transfer at the ITE:
Airflow requirement (ITE):
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
The affinity laws for fans:- The airflow is proportional to fan speed- The power is proportional to the cube of the fan speed- The power requirement is proportional to the cube of the airflow
=
Modeling physical components
Airflow
Examples:1.- A data center with 1 CRAH unit.
If the airflow required by the ITE is reduced by half, the power required will be reduced by a factor of 8.
2.- If the airflow required by the ITE can be supplied by 4 CRAH units instead of 1 unit at full capacity.With 4 units operating at a fourth of the maximum speed, the power is 16 times lower.
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk MetricModeling physical components
Power
= ∑ + ∑ + ∑
Sensible Coefficient of Performance:
= net sensible cooling capacitypower required to produce cooling ( )
= ∑Power requirement (cooling system)
values for commercial precision cooling systems without economizers usually range from 1.8 to 3.8
Data Center Modeling 101
Moises Levy, PhD
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0
20
40
60
80
100
120
140
160
180
Airf
low
(CFM
)
% utilization
Series1 Series2
A Framework for Data Center Site Risk Metric
Rack server example
Power Airflow
Is this model accurate?
The model is accurate within a 20% margin of error, andwith greater precision (< 7% margin of error) if utilization > 50%.
Data Center Modeling 101
Moises Levy, PhD
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Data center key indicators
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Efficiency key indicators such as PUE
= ∑= ∑ + ∑∑
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Simulations to predict behavior
Types of workload
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Simulations to predict behavior
2 nodes (ITE)WL distribution: 30%, 70%WL input peak: 250 jobsRun time: 1 hour
= 50, 80 j/s= 200= 50
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Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Simulations to predict behavior. . 532 s 821 s
400 W 35.6 cfm 273W-h
Data Center Modeling 101
Moises Levy, PhD
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A Framework for Data Center Site Risk Metric
Simulations to predict behavior
Equal node distribution and Normal workload input
Workload vs. # nodes vs. Run time Workload vs. # nodes vs. Energy Workload vs. # nodes vs. Max wait time
Data Center Modeling 101
Moises Levy, PhD
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o Calibrate
o Validate
Real-time data
Data Center Modeling 101
Moises Levy, PhD
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o Simple formulation to predict parameters (under certain assumptions)
QoS, power, airflow, energy, KPIs
o Modeling helps understand data center performance
o Basis to develop simulations to assess data centers
o Assist in finding areas of improvement, providing a basis for decision-making
o Foundation to understand end-to-end resource management
Data Center Modeling is useful
Data Center Modeling 101
Moises Levy, PhD
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Q & A
Moises Levy, [email protected]
www.dcmetrix.com
Data Center Modeling 101