Post on 14-Feb-2021
transcript
Data Center Modeling 101
Moises Levy, PhDLevyMoises@dcmetrix.com
www.dcmetrix.com
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
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
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
Data center model
Simple
Correct
Useful
QoS, Power, Airflow, Energy, KPIs
ITE and cooling specs
Workloads
Data Center Modeling 101
Moises Levy, PhD
Steps for modeling data centers as CPS
1. Modelingcyber components
2. Modelingphysical components
3. Key indicators
Data Center Modeling 101
Moises Levy, PhD
A Framework for Data Center Site Risk Metric
Cyber components
1
2
Data Center Modeling 101
Moises Levy, PhD
ITE specs : , ,
Modeling cyber components
Win , = Win,DC * S ,Data Center Modeling 101
Moises Levy, PhD
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
, = ∗ , + , = , ∗
ITE specs: , ,
Modeling cyber components
Power
ITE Power requirement
ITE Energy consumption
Data Center Modeling 101
Moises Levy, PhD
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
A Framework for Data Center Site Risk Metric
Physical components
1
2
Data Center Modeling 101
Moises Levy, PhD
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
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
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
0
20
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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
Data center key indicators
Data Center Modeling 101
Moises Levy, PhD
A Framework for Data Center Site Risk Metric
Efficiency key indicators such as PUE
= ∑= ∑ + ∑∑
Data Center Modeling 101
Moises Levy, PhD
A Framework for Data Center Site Risk Metric
Simulations to predict behavior
Types of workload
Data Center Modeling 101
Moises Levy, PhD
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
.
Data Center Modeling 101
Moises Levy, PhD
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
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
o Calibrate
o Validate
Real-time data
Data Center Modeling 101
Moises Levy, PhD
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
Q & A
Moises Levy, PhDLevyMoises@dcmetrix.com
www.dcmetrix.com
Data Center Modeling 101