Energy and Thermal Management Metrics for Energy Efficiency in Data CentersC

Energy and Thermal Management Metrics
for Energy Efficiency in Data Centers
DataCloud2015_Monaco 2-4 June
Marta Chinnicia, Alfonso Capozzolib
aENEA C.R. Casaccia, Via Anguillarese, 301, Rome – 00123, Italy
bPolitecnico di Torino, Corso Duca degli Abruzzi, 24, Turin – 10129, Italy

Energy and Thermal Management Metrics for Energy
Efficiency in Data Centres
Marta Chinnici
Alfonso Capozzoli
Data Center (DC) Energy Efficiency:
Problem Statement
Key Findings:
 The role of DC within society is leading to a great increasing of interest both in ICT and
Energy sectors: DCs are complex-system contain IT equipment used for the processing
and storage data, and communications networking;
Main components of electricity consumption for the ICT sector (Ref: Greenpeace Report, May 2015)

Marta Chinnici
Alfonso Capozzoli
Data Center (DC) Energy Efficiency:
Problem Statement
 The rapid rise of energy-hungry Data
Center is driving significant growth of
the power consumption, electricity
usage.
 The growing power demand of DCs has
led to a heightened awareness of their
increasing impact on climate change
from greenhouse gas (GHG) emissions.
Data Center energy use extrapoled to 2015 (ref: Koomey J.G.,2008 ) Growth in Data Center GHG emissions -2002 to 2020 (ref: GeSI
report, 2012 )
However, DC emissions growth expected to slow
down from 9% to 7%

Marta Chinnici
Alfonso Capozzoli
Why to measure Energy Efficiency in DC?
The concept of Energy Efficiency became an important issue in DC design due to the increase of
energy price and policy pressures.
Energy
Efficiency
Policies
Best
Practices
Green IT
Efficiency
Cooling
Systems
Optimizing
Computing
resources
usage
Metrics
Renewable
Energy
Federation
Smart
Cities

Marta Chinnici
Alfonso Capozzoli
Measuring DC Energy Efficiency: Beyond Metrics
The complexity of DC creates serious difficulties in pinpointing a methodology in terms of
EE; within DC many variables need to be taken into account. In recent years, a variety of
Metrics were proposed to evaluate DC energy efficiency by measuring performance at
different levels and from different perspectives.
Major barriers:
There is a lack of a complete plan which provides standard metrics and methodologies for
DCs.
Key objectives:
 The first step in energy efficiency improvement is to effectively evaluate energy
consumption and DC environment by measuring the performance through a “HOLISTIC”
approach.
 Identify appropriate best practices and “optimization procedure” based on holistic
approach for improving the design stage and management of a DC.
 Identify standard ways for monitoring, measuring, verifying and reporting energy
consumption and performance in DCs.

Marta Chinnici
Alfonso Capozzoli
Measuring DC Energy Efficiency: Beyond Metrics
 Focus on the methodologies for capturing the energy consumption and carbon
emissions arising from the DCs.
 Introduce metrics capable to capture the complex phenomena occurring in a DC (e.g:
the contribution of IT, cooling systems, “useful work”, thermal management and so
on…).
 To analyse the mutual relation among energy and thermal metrics.
 To consider the contribution of renewable sources and hence, adapting the DC power
consumption to the availability of renewable energy in dynamically way.
 DCs in Smart Cities context: to adapt the requests received by the Smart City Energy
Management authority.
 Introduce metrics regarding DC Federation.
Evaluation of DCs should be based on globally accepted assessment systems in terms of
common metrics that promote the improvement of energy saving, renovation and
improvement of infrastructures, management methods and so on…

Marta Chinnici
Alfonso Capozzoli
Current Energy Metrics:
Criteria for selection and Methodologies
In literature, several different metrics were proposed during the last few years however, there are a
number of concerns with both a systematically approach and conceptual metrics tasks:
1. there is not a comprehensive classification of metrics;
2. a new perspective to connect thermal and energy consumption metrics.
Energy
Efficiency
Energy Power
Metrics
Thermal
Metrics
DC Energy long
term
assessment
Run-time
thermal
management
diagnosis
DC
Design
Stage
DC
Operati
onal
Stage
Globally DC-EE Assessment

Marta Chinnici
Alfonso Capozzoli
Metrics Overview
Ref: Public Deliverable D7.1 of DC4Cities FP7-SMARTCITIES-2013(ICT), Description of energy efficiency metrics for Data Centers, August 2014.

Marta Chinnici
Alfonso Capozzoli
Metrics Overview

Marta Chinnici
Alfonso Capozzoli
Metrics Overview (IT level)

Marta Chinnici
Alfonso Capozzoli
Current Energy Metrics:
Analysis of the current state of the art of metrics for DCs*.
 Metrics were classified/discussed at different levels:
1. A first level of classification according to the nature of variables addressed:
 power/energy metrics (related to energy consumption (kWh) or power demand
(kW) at different levels);
 IT useful work metrics (related to computing processes, data transfer, or storage);
 thermal metrics (related to temperature);
 waste and emission metrics (to measure the amount of natural sources wasted or
the quantity of pollution generated by building and managing a DC).
2. A second level according to the physical infrastructure of DCs:
 whole infrastructure system level;
 component level (IT, HVAC, lighting,etc.).
3. A third level, according to selected objectives :
 Minimize energy use, minimize emission/source consumption, renewable energy,
energy reuse, scalability.
*Ref: 1) Capozzoli A., Chinnici M., Perino M., Serale G.: Review on Performance Metrics for Energy Efficiency in Data Center: The Role of Thermal
Management, Energy Efficient Data Center, Third International Workshop, E2DC 2014, Cambridge, UK. Ed. LNCS 8945, pp. 135-151, 2015.
2) Public Deliverable D7.1 of DC4Cities FP7-SMARTCITIES-2013(ICT), Description of energy efficiency metrics for Data Centers, August 2014.

Marta Chinnici
Alfonso Capozzoli
Ref: Public Deliverable D7.1 of DC4Cities FP7-SMARTCITIES-2013(ICT), Description of energy efficiency metrics for Data Centers, August 2014.

Marta Chinnici
Alfonso Capozzoli
Metrics: Progress Today & Challenges
DC metrics in DC4Cities project: is developing its activities establishing a method to
compare the measurements processes and assess new energy efficiency indicators for
DCs, in order to outline a standardization procedure.
DC4Cities project is leading the Smart City Cluster collaboration. The main objectives of the Cluster
are:
• ensure that all projects are able to use common KPIs (Key Performance Indicators) to characterize
the energy, environmental and economic behaviour of their DCs;
• enable the comparison between different projects;
• collaborate with DC standardization organizations.
New metrics focus on the energy behaviour of the DC:
 Flexibility mechanisms in DCs:
 Demand shifting: workloads are shifted from a time period to another, but always within
the same Data Centre
 Demand being federated: shifting the workloads to other Data Centres
 Renewables integration: Energy produced locally and renewables usage
 Primary energy savings and CO2 emissions avoided

Marta Chinnici
Alfonso Capozzoli
Challenge. The understanding and the actual possibility of calculating in mathematical way
the "useful work" within a DC is particularly complicated: it depends on the
applications/services within DC.
What is the "useful work” (or Work
Done) by a DC?
Total amount of computing by all
applications/services running in DC
How calculate the "useful work" (or
Work Done) done by DC?
DCeP* (DC energy Productivity)
To characterize the energy requested
to produce useful computational work
Total Work
Done/Total
Energy

Marta Chinnici
Alfonso Capozzoli
None of the current metrics directly gives a (mathematical) measure of the useful work in a DC.
Issue: How it is calculated the value of V (normalization factor)?
“Work Done” of different applications/services can’ t be simply added
At present, there is no practical solution to these questions...
“useful work” = describes the number
of tasks executed by the DC and EDC
represents the consumed energy
respectively for the completion of the
tasks.
Practical solution: To «unpack» the sum of different tasks and to evaluate the normalization factor in
order to compare the different workloads.
Benchmark Procedure: evaluation of workload-based metrics

Marta Chinnici
Alfonso Capozzoli
• It is possible to define mutual relations among performance metrics
and reciprocal physical influences?
• A thermal awareness approach can help to achieve the energy
saving in DC?
The key role of thermal management at design stage and
during the operation of a DC in order to achieve energy
saving will be discussed
The role of thermal management
• Which is the impact of thermal phenomena on the behavior of the
global energy consumption and on the reliability of the IT
equipment?

Marta Chinnici
Alfonso Capozzoli
Thermal metrics
Typical design layout of server rooms
AIR INLET to datacom equipment IS the important specification to meet
OUTLET temperature is NOT important to equipment
All power required to run IT equipment is dissipated as heat
and because IT equipment needs to operate at appropriate
temperature, designing an efficient cooling system becomes of
crucial importance.

Marta Chinnici
Alfonso Capozzoli
Effect of temperature on component efficiency
Infrastructure side
An increasing of the DC environmental temperature causes a positive effect on
cooling system. The efficiency of the cooling components rises.
Tang, Q., Gupta, S. K. S., & Member, S. (n.d.). Energy-Efficient , Thermal-Aware
Task Scheduling for Homogeneous , High Performance Computing Data
Centers : A Cyber-Physical Approach, 1–14.
Lee, K.-P., & Chen, H.-L. (2013). Analysis of energy
saving potential of air-side free cooling for data centers
in worldwide climate zones. Energy and Buildings, 64,
103–112. doi:10.1016/j.enbuild.2013.04.013

Marta Chinnici
Alfonso Capozzoli
IT equipment side
With the increasing of the DC room temperature, the consumption of the IT equipment
rises. This is due to a major fan power consumption and server energy leakage.

Marta Chinnici
Alfonso Capozzoli
Effect of Temperature on server reliability

Marta Chinnici
Alfonso Capozzoli
Temperature variation effects on total energy consumption
It is necessary to calculate an
optimal temperature set-point. This
would be the ideal tradeoff between
cooling system and IT equipment
energy consumption.
This compromise could be reached
by modifying the air inlet
temperature in the cold aisle. It
mainly depends on the server and
CRAC characteristics.
Durand-Estebe, B., Le Bot, C., Mancos, J. N., & Arquis, E. (2013). Data center optimization using PID regulation in CFD simulations. Energy and
Buildings, 66, 154–164.
The thermal management can affect
the opportunity to set an optimal
temperature?

Marta Chinnici
Alfonso Capozzoli
IT Equipment Environment – ASHRAE Psychrometric Chart

Marta Chinnici
Alfonso Capozzoli
The specifications are related to supply temperature in the datacom equipment
and not to return CRAC/ambient temperature.
ASHRAE Indications

Marta Chinnici
Alfonso Capozzoli
Airflow optimization and management
This scenario wastes cooling
capacity
This scenario increases the inlet
temperature to equipment
HOT - COLD SPOT
A fraction of inlet cold air does not
contribute to cooling of the IT equipment
The cold air intake into IT equipment is
not sufficient and as a consequence a
fraction of the hot air is recirculated

Marta Chinnici
Alfonso Capozzoli
Airflow within a DC

Marta Chinnici
Alfonso Capozzoli
Metric Formula Information provided
Supply Heat
Index
Warm air infiltration inside cold aisle
Return Heat
Index
Return heat at CRAC units after
recirculation
Negative
Pressure Ratio
Warm air infiltration inside air-supply
plenum due to negative pressure. It can
be calculated from CFD analysis
Bypass Ratio
Mass flow rate that returns at CRAC units
without heat power exchange
Recirculation
Ratio
Mix between cold air supply and exhaust
air from hot aisle
Balance
Balance between airflow at CRAC unit and
across IT equipment
Return
Temperature
Index
Balance between airflow across IT
equipment and at CRAC unit
Global thermal metrics
Based on DC average air temperatures

Marta Chinnici
Alfonso Capozzoli
Metric Formula Information provided
β index
Local increase of air temperatures
along the rack
Rack Cooling
Index Low
Rack cooling efficiency considering a
lower threshold values
Rack Cooling
Index High
Rack cooling efficiency considering an
upper threshold values
Capture
Index
(cold aisle)
Cold airflow ingested by a rack. . It can
be obtained from CFD analysis
Capture
Index
(hot aisle)
Warm airflow captured by a local
extractor or cooler. It can be
obtained from CFD analysis
Local thermal metrics based on rack
punctual air temperatures or mass flow rates

Marta Chinnici
Alfonso Capozzoli
1. RCIHi 2. RCILo 3. RTI 4. SHI/RHI Overall Airflow
Efficiency
Evaluation
Standard
Ideal
Good
Acceptable
Poor
Ideal
Good
Acceptable
Poor
Target
Good
Poor
Good
SHI<0.2
RHI>0.8
1 Poor - - -
No Good2 - Poor - -
3 - - Poor -
4 Acceptable - - -
Acceptable
5 Good/Ideal Poor
Bypass or
Recirculation
-
6 Good/Ideal Good/Ideal
Bypass or
Recirculation
- Good
7 Good/Ideal Good/Ideal Good - Very Good
8 Ideal Ideal Target Good Ideal
Overall Evaluation Efficiency
• To assure IT equipment reliability;
• To assess absence of over cooling for energy saving;
• To investigate presence of bypass or recirculation phenomena
• Information about overall airflow efficiency

Marta Chinnici
Alfonso Capozzoli
Temperature variation effects on thermal metrics
The temperature of the air supplied in the cold aisle is the most sensitive parameter for
the environment temperature and air flow efficiency.
Cho, J., Yang, J., & Park, W. (2014). Evaluation of air distribution system’s airflow performance for cooling energy savings in high-density data centers.
Energy and Buildings, 68, 270–279.
0
0.2
0.4
0.6
0.8
1
13 15 17 19 21
SHI
RHI
Supply temperature
Certain thermal metrics (SHI, RHI, RTI) are not sensitive to the variation of a single
temperature parameter, because based only on temperature differences. Other metrics,
such as RCI, are very sensitive to temperature variations.

Marta Chinnici
Alfonso Capozzoli
Mutual Relation Among Thermal and Energy Consumption
Metrics
Some authors recognizes that the mixing of hot and cold streams in the DC airspace is an
irreversible process and must therefore lead to a loss of exergy. The proposed exergy-
based approach can provide a foundation upon which the DC cooling system can be
simultaneously evaluated for thermal manageability and energy efficiency.
• Vice versa, thermal metrics are still of limited use because few information is gained
regarding the energy efficiency of the system.
• In general power/energy metrics provide no information about bypass/recirculation
phenomena and corresponding impacts on the thermal manageability of DCs.
• Thermal metrics are used to enable real-time feedback and control of DC thermal
architecture, while power/energy metrics to outline the global energy consumption.
Usually these metrics are used in parallel

Marta Chinnici
Alfonso Capozzoli
Second-law analysis that considers both global
thermal management and local phenomena
(recirculation) as irreversible processes source of
exergy losses.
Exergy losses occur in:
- Air space;
- Rack units;
- CRAC units.
An exergy-based approach for the evaluation of thermal
manageability and energy efficiency
A.J. Shah, V.P. Carey, C.E. Bash, C.D. Patel, Exergy Analysis of Data Center Thermal Management Systems, J. Heat Transfer. 130 (2008) 021401.
Comparison with other metrics for thermal management and hot-spot
recognition
Exergy losses balance into a DC
The airspaces losses are divided themselves into:
- Airspace into hot and cold aisles;
- Airspace into rack units;
- Airspace into CRAC units.

Marta Chinnici
Alfonso Capozzoli
.
The sum of the different exergy losses
provides an appropriate metric for the
overall DC.
The share of recoverable exergy represents a
possible improvement for the DC.
Exergy losses distribution in the air-space of a DC
Temperature distribution in the air-space of a DC
A map of the data center airspace that highlights
locations of recirculation. This maps is related to
the temperature distribution
The exergy-based metric is more sensitive to
recirculation than the traditional temperature-
based metrics.
An exergy-based approach for the evaluation of thermal
manageability and energy efficiency

Marta Chinnici
Alfonso Capozzoli
Mutual Relation Among Thermal Management and Energy
Consumption
• In order to offset by pass and recirculation air issues, the CRAC units are often
designed and controlled to supply air at a lower temperature, thus with an
higher energy consumption.
• Global energy indices are not necessary capable to detect these phenomena.
Indeed, hotspots are local phenomena whose influence on the global
power/energy efficiency may be negligible.
• The energy metrics are referred to long term period (e.g. a year or a season)
while hotspots are phenomena which depends on short term variation of
boundary conditions
• Therefore it is necessary to apply a continuous commissioning to detect the
occurrence of local phenomena through thermal and energy metrics.

Energy Efficiency Management Through Thermal Performance
Awareness

Tang, Q., Gupta, S. K. S.,Varsamopoulos, G.: Thermal-Aware Task
Scheduling for Data Centers Through Minimizing Heat Recirculation. 2007
IEEE International Conference on Cluster Computing, 129–138.
doi:10.1109/CLUSTR.2007.4629225 (2007)

Marta Chinnici
Alfonso Capozzoli
Energy Efficiency Management Through Thermal Performance
Awareness
This way of task scheduling to IT equipment with thermal awareness guarantees
both the minimization of hot air recirculation (low values of SHI) and energy
consumption, also in relation with other algorithms of task assignment.

Marta Chinnici
Alfonso Capozzoli
Conclusions
 Evaluation of EE in DCs should be based on globally accepted assessment systems in terms of common metrics
and methodologies.
 A holistic framework may help to take into account the effects on all metrics simultaneously, but the need for
the development of new, more accurate and/or usable metrics is recognized
 Alongside to existing metrics, is necessary to provide renewable energy metrics for DC in order to facilitate with
renewable power their operations.
 Identify DC energy metrics for meeting future energy needs in smart cities context.
 Long term energy assessment and “real time” thermal environment diagnosis should be not considered as
separate tasks for a comprehensive DC performance analysis. These two aspects should be always coupled.
 The thermal management should be achieved through the calculation of local thermal metrics primarily, and
then by other average thermal metrics referred to the whole DC environment.
 On the other hand the energy assessment should be performed through power/energy metrics capable to
capture in a correct way the effect of energy consumption variation for both cooling and computing as well as the
adaptability at part load conditions of DC infrastracture
 The improvement and optimisation of DC performance through a thermal awareness approach to minimize
recirculation effect represents an effective way to obtain energy savings.

Marta Chinnici
Alfonso Capozzoli
Thanks you for your attention!
Any questions?
MARTA CHINNICI
marta.chinnici@enea.it
ALFONSO CAPOZZOLI
alfonso.capozzoli@polito.it

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