WHITE PAPER

How Artificial Intelligence Is Cooling
Data Center Operations
Dynamically match cooling to IT load in real time
Executive overview

Recent swift changes in societal habits have increased demand for data usage atan unprecedented rate. While this may mean more revenue for commercial data
centers, the surge in usage is also increasing risks of downtime – creating moredemand on staff, equipment and energy consumption. These changes give rise
to a bigger challenge: how do you scale to meet current demand and plan for
future capacity in an age of hyperconnectivity?
Artificial Intelligence (AI) offers data centers promising solutions to improve
operations over the long term. In fact, Gartner analysts predict that the early
adoption of AI will be a key factor separating data centers of the future fromthose destined to be dinosaurs of the past. While incorporating AI into anorganization’s systems can be a long, challenging process, there is good news.
Data centers can easily and successfully implement AI in their operations withnew thermal cooling solutions.
This paper explores AI and its impact on data centers, using white space cooling
optimization as an example of how AI can be implemented today. Starting witha look at the changing data center landscape, this paper provides a glimpse into
what the future holds and examines the critical aspects of thermal cooling,
specifically thermal optimization. It focuses on how a data center can easily begin
integrating AI into its processes through whitespace cooling optimization (WSCO)
and reviews how a global financial firm is using WSCO as part of its thermal
optimization plan, with promising results.

usa.siemens.com/datacenters

Table of Contents

Challenges facing data centers today........................................................................ 3AI‘s impact on data centers...................................................................................... 5
Where can AI provide immediate results?.................................................................. 6
The answer: thermal optimization!........................................................................... 6
Benefits of thermal optimization.............................................................................. 8
AI and WSCO........................................................................................................... 8
Increasing staff efficiency...................................................................................... 10
Improved planning and budgeting.......................................................................... 10
Building toward the future...................................................................................... 11
Use case: WSCO keeps financial data cool and safe................................................... 11

Challenges facing data centers today
Out of necessity, data centers have continually evolved to keep up with customer demands andhigh-tech innovations. The challenges of staying in front of new trends and changing user
requirements are significant. The constant question is: What’s next?
In early 2020, Forbes Insights, the strategic research and thought leadership practice of Forbes
Media, attempted to answer that question. In doing so it identified three major challenges
facing the data center of the future:• Lack of readiness to upgrade: The nature of data centers is changing. To be able to tap intoa variety of new cost-cutting opportunities, data centers need to decentralize. They need tomove data, processing and resources away from the organization’s local data hub to the edge
(user devices) – moving aspects of processing to the devices used by end users.
• Infrastructure challenges: As 5G enables hyperconnectivity, more users and devices willenter the network, increasing traffic volume and bandwidth usage. To address this new
demand, data center leaders need to incorporate smartinfrastructure into their data centersplans, integrating automated systems supported by devices with built-in artificial intelligencecapabilities that provide real-time maintenance, configuration and resolution.
• Staffing challenges: Forbes Insights cites recent research that reports that one-third of the IT
infrastructure workforce will be retiring over the next decade. In response, more than half ofthe executives surveyed stated that they expect staffing will be taken over by external cloudand service providers.
Surprisingly, very few organizations surveyed were ready to rethink their data center strategies
in preparation for the next five years. Just 11% of C-suite executives and 1% of engineers said
their data centers are ahead of the curve and primed for higher data volumes, according toForbes Insights.

Surprisingly, very
few organizations
surveyed were readyto rethink their data
center strategies in
preparation for the
next five years.

0%

Cooling Output

IT LoadSince the survey was taken, the world has changed. While readiness, infrastructure and staffing
remain areas of significant concern for the future, the main challenge facing data centers today
is exponential growth. Mobile networks propelled by 5G technologies are providing a new levelof hyperconnectivity. In addition, more people are working remotely, transforming American
workplaces and adding a significant spike in demands on data center use.
At the same time, online shopping and ecommerce have surged, further fueling the demand fordata and data storage. In fact, ecommerce experienced the equivalent of 10 years of growth in
the space of 90 days due to COVID-19, according to an analysis conducted by McKinsey &Company. In the 10-year period between 2009 and 2019, U.S. ecommerce penetration grewfrom 5% to roughly 16%. In the first three months of 2020, ecommerce penetration doubled in
the United States, growing from 16% to almost 35%.
US e-commerce penetration, %CEOs and CIOs are
now focusing on how
to digitally transformtheir operations sothey can keep up
with rapid increases
in demand.

35
30
25
20
15
10
5
0

2009

2011

2013

2015

2017

2019 Q1 2020

Source: Bank of America; Forrester Analytics; ShawSpring Research;
US Department of Commerce; McKinsey analysis

With this exponential growth, CEOs and CIOs are now focusing on how to digitally transformtheir operations so they can keep up with rapid increases in demand. Digital transformation
is a fundamental rethinking of an organization’s use of technology, according to George
Westerman, senior lecturer at the MIT Sloan School of Management and author of the bookLeading Digital: Turning Technology Into Business Transformation. Westerman argues that IT
departments must break out of their traditional roles in organizations and quickly become
co-creators of “digital transformation” in order to help their companies thrive in today’s rapidlychanging world.

AI’s impact on data centers
Artificial Intelligence (AI) is part of the digital transformation and is poised to have atremendous impact on data center management, productivity and infrastructure. Several
companies already are experimenting with the idea of fully automating data centers for periodsof time, allowing a “lights out” operation. The theory is that a number of energy-efficient
strategies can be implemented if data centers were built and optimized entirely based on IT
considerations and monitored remotely via DCIM software. By using AI and robots to eliminate
the need for employees in the physical space, data centers could lower oxygen levels to reducefire risk, eliminate the need for lighting, create more efficient cooling designs, and increase
rack heights.
But there are challenges. While leading organizations expect to double the number of AI
projects over the next two years, according to the Gartner 2020 CIO Agenda Survey, most are
struggling to implement them. Stakeholders face challenges that include transparency and“interpretability” around algorithmic decision-making, data quality and bias, safety and security,
accountability, social and economic impact assessments, and governance. One of the biggest
obstacles is scaling AI pilots into enterprise-wide production. AI can address many issues in acontrolled environment; however, replicating the experience across an organization is fraught
with difficulty. Due to these challenges, most companies question how quickly
AI can provide a return on investment.

What is AI?
Artificial Intelligence (AI) is the creation of human-like intelligence using computer-based algorithms.
According to the Oxford Dictionary, AI is the “theory and development of computer systems able to performtasks that normally require human intelligence, such as visual perception, speech recognition,
decision-making, and translation between languages.”
AI systems are based on three types of computer-based machine learning: supervised learning, unsupervised
learning, and reinforcement learning. White Space Cooling Optimization (WSCO) uses supervised learning,where you train the machine to use data that is “labeled.” In other words, the computer is fed data that istagged with correct answers to potential questions, or scenarios. Its algorithm learns from this labeled training data how to map answers between input examples (questions) and the target variable (best answers).
The algorithms teach the computer memory to how to predict outcomes from similar, unforeseen data andrespond to future events.

Where can AI provide immediate results?
One area where AI can immediately deliver real benefits is data center cooling and control. As
demand for data grows, so does the need to better manage cooling conditions in data centers.
Among the factors that make control challenging is the mix of legacy equipment from a variety
of manufacturers found in many data centers, each with its own recommended standards that
often call for more cooling than is actually needed. Managing airflow is also complex, especiallywith the dynamic nature of data centers and the unpredictable demands of IT equipment.
Added to this is the fact that on average, 20% to 30% of servers in large data centers are unusedor obsolete but still consume electricity. These ghost or zombie servers not only add costs to theelectrical bill, they also create excess heat and drive higher demand for cooling throughout thedata center. As a result of all these factors, most data centers
are overcooled, wasting valuable resources.

The answer: thermal optimization!
Thermal optimization is a comprehensive Siemens solution that eliminates the need to manually
maintain the optimal, cool and consistent temperatures required to house data center equipment
safely. Its benefits go beyond temperature control to include the optimizing of energy use andpersonnel time to better manage resources.
Thermal optimization features a package of facility improvement measures based on datacollection and analysis. The performance of everything from the cooling tower to the rackis measured and approved. The solution also applies intelligence about the density of IT
workloads to regulate the cooling within a data center. The goal is to optimize primary andsecondary cooling cycles as well as the temperatures within the “white space” – the unusable
square footage of a data center.

On average, 20%
to 30% of servers inlarge data centers
are unused or
obsolete but stillconsume electricity.

In this paper, we define thermal optimization specifically as the combination of Demand Flow®
chiller optimization and White Space Cooling Optimization (WSCO).
Demand Flow: To reduce energy costs and ensure ongoing operational efficiency, Demand
Flow takes a holistic approach that employs thermodynamic principles. It focuses on optimizing
every subsystem of the chiller plant, including chillers, chilled water pumps, condenser water
pumps and cooling towers. A powerful solution that provides immediate results, Demand Flowcollects and analyzes data to deliver the proper amount of chilled water to meet the current
cooling load.
White Space Cooling Optimization (WSCO): Using a network of sensors, WSCO collects
temperature and air supply data. Its AI Engine applies the data to algorithms and calculates therequired adjustments in airflow to maintain the correct temperature for each aisle of racks. It
goes beyond alerting human operators to temperature fluctuations and automatically makes
adjustments itself. By automating the control of cooling fans, it reduces the risk of a thermal
outage and maintains consistent air temperatures among server racks and white space. It also
reduces wasted energy by dynamically matching cooling in the IT load in real time, automatically responding to temperature changes andeliminating overcooling. At the same time, it gives
the management team critical data that supports future operational business decisions.

Chiller optimization with Demand Flow
Demand Flow operates on the simple premise that we need to think differently about chilled water systems.
It challenges the industry norm of pumping a constant volume of water through the chilled water system
all year long with little regard for the varying load on the system.
Instead, Demand Flow takes a holistic approach that uses thermodynamic principles. It focuses on
optimizing every subsystem of the chiller plant, including chillers, chilled water pumps, condenser water
pumps, and cooling towers. Through the use of high-accuracy precision instrumentation and variable
pressure curve logic, Demand Flow is able to dynamically adapt to load changes while maintaining
optimal total system performance.
A Demand Flow solution typically includes installing variable speed drives on all chilled water pumps,
condenser water pumps, and cooling tower fans as well as employing the high-accuracy controllers andinstrumentation needed for proper control.
With Demand Flow, water flow varies through both the evaporator and condenser tube bundles innear-real time to perfectly optimize total system energy consumption in response to system load
fluxuations. The effects of optimized flow through the chillers in near-real time produces significant
energy savings at the chiller and can increase the nominal tonnage of a typical plant by as much as 20%.

35
30
25

Benefits of thermal optimization

20

By optimizing chilled water cooling (Demand Flow) and white space cooling (WSCO) together,15

thermal optimization can help a data center achieve more savings than using one or the other10

solution individually. The combination delivers many benefits, including:

5
0

1.

2009

2011
2013indoor
2015 environmental
20172019 Q1 2020
Collecting
data in real time and providing valuable insights formaking short- and long-term operational decisions

Source: Bank of America; Forrester Analytics; ShawSpring Research;
US Department of Commerce; McKinsey analysis

2.

Freeing up capacity and better positioning data center workloads

3.

Providing dynamic control and energy savings by managing thermal airflow

4.

Enabling systems to adjust in real time to match facility cooling needs as IT loads change

5.

Maintaining optimal performance by dynamically controlling pumps, chillers and fans

6.

Optimizing central plant operations to help a facility achieve energy savings up to 50%

Benefits of thermal optimizationenergysavings

airflowmanagement

Dynamicallycontrolling pumps,
chillers and fans canlead to maintained
optimal performance.

chiller
optimization

Managing thermal
airflow provides
dynamic control andpotential energy
savings.

24/7

Optimizing central
plant operations
can help a facility
achieve energy
savings up to 50%.

real-time
monitoring

Systems adjust in realtime to match facility
cooling needs as IT
loads change.

AI and WSCO
AI is what makes White Space Cooling Optimization (WSCO) so effective in improving
temperature control and cool air distribution with less staff support. WSCO’s AI engine utilizes
a type of machine learning known as supervised learning that enables the WSCO software tocontinuously get smarter based on the calculations of a series of algorithms. The software
learns by continuously analyzing the sensor data for environmental changes. Past and current
records of temperature control data are used as a basis for the algorithms and to establish
baselines. The software quickly learns how to best respond to temperature variations.
The backbone of WSCO consists of a wireless mesh system with a dense array of sensors andcontrollers that fuel the powerful analytics and machine learning process. Rack-level sensors
capture both top and bottom rack temperatures while cooling unit controllers provide data suchas supply and return temperatures, status, speed, and cooling unit power. This data helps
identify cooling influences on the racks so that WSCO’s learning software can dynamicallydetermine how much cooling is needed for ideal conditions.

By optimizing chilledwater cooling(Demand Flow) andwhite space cooling(WSCO) together,thermal optimization
can help a data
center achieve moresavings than using
one or the othersolution individually.

Leveraging data collected by the sensor network, WSCO’s AI engine automatically creates areal-time model of the facility’s thermal environment. The AI engine maps influences anddetermines the precise cooling influence of every Computer Room Air Handling (CRAH) unit,
both individually and collectively, at every spot across the data center. In most facilities,
the AI engine is able to identify influence patterns and measure cooling influences in lessthan 24 hours.
The WSCO system then takes dynamic control of the cooling units – turning them on and off,and ramping fan speeds up and down — to meet pre-specified temperature settings in themost efficient manner possible. As the AI software learns the effects of control actions, it
manipulates the cooling equipment by itself without staff intervention, automatically managing
cooling and balancing airflow in critical areas in the data center hall.
In the event of a system failure or when temperatures exceed a certain threshold, the WSCO
system has a “guard mode” that activates cooling. The “guard mode” delivers added protection
to the data center until the mechanical system returns to normal operation, all driven by theautomation and learning algorithms.
WSCO’s impact on air cooling efficiency
100%

Before Wasted Energy

After Spare Capacity

50%

0%

IT Load

Cooling Output

WSCO solutions are infrastructure agnostic, so they are immediately compatible with existing
systems with minimal on-site configuration and setup. The WSCO wireless architecture makes
installation non-intrusive and flexible. Without the need to run hundreds of cables, even large
sites can be up and running in a matter of weeks.
WSCO’s key success factor is its ability to dynamically match cooling to the IT load in real time.
The AI engine uses real-time data to produce algorithms that predict the best level of coolingthat will deliver the desired temperature at each sensor. And because most facilities are overcooled, WSCO uncovers pre-existing redundancy and redirects airflow. It typically level-setscooling capacity while delivering significant reductions in energy use.

The AI engine uses
real-time data to
produce algorithms
that predict the bestlevel of cooling thatwill deliver thedesired temperature
at each sensor.

Increasing staff efficiency
As a truly autonomous solution, WSCO can be capable of increasing staff efficiency in numerous
ways. It minimizes the need for staff supervision and personnel on site, allowing valuable
staff to be assigned to other critical tasks and limiting overall site access. While matchingcooling requirements to IT load in real-time, WSCO can self-correct and quickly scale to meetdemand, eliminating the need for human intervention. At the same time, it provides visibility
into real-time system performance so operators can make necessary adjustments with the userinterface. As a result, data centers don’t need employees or third-party vendors to physicallyaddress the tactical, complex and time-consuming task of adjusting cooling systems or to
manually recommission the server room. When third-party support is needed, the WSCO system
can quantify the effectiveness of the vendor’s work by using historical data to compare
performance before and after maintenance actions and ensure that repairs are fully resolved.

Improved planning and budgeting
Since the WSCO interface can integrate with any building management system (BMS) via
BACnet/IP, it gives managers immediate access to real-time, trend-based insights and new levelsof control at their fingertips. For example, WSCO may help improve capacity planning. Onceovercooling is addressed, managers can use the system to identify how much actual cooling is
still available and then determine when and where to deploy new IT equipment. Likewise,
managers may be able to defer or reduce capital expenditures since fewer units are needed tomeet day-to-day cooling requirements and duty cycles on existing equipment can be reduced,
which may lead to extending equipment life. Managers have the visibility and reporting needed
to identify exactly which cooling units need to be maintained, which are running optimally andwhich are underperforming. Easy identification of poorly performing equipment helps managers
plan when equipment needs to be retired and which low-efficiency units should be the first togo. In addition, operators can use the interface to identify any area in the data center that faces
thermal risk so that high-value IT assets are not deployed there until the problem is resolved.
To maintain configured limits, operators have the option to continually improve thermal
management by directly and automatically managing temperatures as they see fit. They canalso operate their sites at higher temperature set points while increasing reliability and maintaining quantifiable SLA compliance.

WSCO increases
staff efficiency byminimizing the needfor staff supervisionand personnel onsite, allowing
valuable staff to be
assigned to othercritical tasks andlimiting overallsite access.

Building toward the future
In combination with other emerging technologies, AI promises to reshape data center
operations in the coming years. However, data centers need to prepare today to be relevant
tomorrow. AI-managed thermal cooling solutions like WSCO help data centers lay the groundwork for the future. These solutions help managers address today’s challenges – personnel
management, operating costs, operational efficiency and energy efficiency – while preparing
their operations to adopt other new AI applications. The simplicity of WSCO’s agnostic
infrastructure design, for example, makes it responsive and scalable to meet future demands.
It’s an AI application that is ready to implement today and a smart investment for
long-term operational excellence.

Use case: WSCO keeps financial data cool and safe
In 2009, a global financial firm invested heavily in the construction of a new data center thatwas classified as a Tier III facility by the Uptime Institute. Less than five years later, the firm
realized it needed to increase the data center’s cooling capacity while reducing its energy
consumption. The data center was built with redundant mechanical plants for both chilledwater and air handling. The chilled water plant used a typical primary\secondary loop chilledwater system serving constant volume Computer Room Air Handling (CRAH) units in the data
halls. While the data halls were built following typical data center design standards, their rackdensity underwent numerous changes that created hot spots. The hot spots caused chronic overprovisioning of cooling throughout many of the data halls. And like many other data centers,
the facility ultimately lost its Tier III rating because it had to turn on what should have been
redundant chillers, pumps and CRAH units to mitigate the hot spots. The data center also faced
increased energy consumption and a loss of adequate cooling redundancy throughout.

The simplicity of
WSCO’s agnostic
infrastructure design
makes it responsive
and scalable to meet
future demands.

The company hired Siemens Smart Infrastructure to implement a comprehensive thermal
optimization strategy for the data center. The first step was to address the chilled water plants’inability to provide cooling to the farthest end of the secondary loop without running its
redundant chiller systems. To help resolve this issue, Siemens implemented its Demand Flowsolution for chilled water. The solution helped the data center accomplish two initial goals: it
was able to reduce the total number of chillers needed to support the load and it was able toutilize the existing plate and frame heat exchanger longer. As a result, Demand Flow helped thedata center achieved an immediate 37% reduction in annual energy usage. It also achieved a$200,000 utility rebate and more than $200,000 in annual savings, which resulted in a simple
payback in 2.75 years.
Second, after optimizing the chilled water plant, Siemens addressed the data center’s airflow
issue: it had to run all 72 of its CRAH units in order to satisfy the data halls’ cooling needs.
Each 30-ton CRAH unit was originally designed as constant air volume (CAV) units. Siemens

The data center
achieved an
immediate 37%
reduction in annual
energy usage. It alsoachieved a $200,000
utility rebate andmore than $200,000
in annual savings,
which resulted
in a simple paybackin 2.75 years.

converted them to variable air volume (VAV) and used ModBus to integrate them to the data
center’s Building Automation system for greater visibility and control.
The third step was to apply White Space Cooling Optimization (WSCO), which has built-in
Artificial Intelligence (AI) for real-time learning and control. The WSCO solution employs
wireless technologies to monitor rack inlet temperatures. Then, using AI-enabled predictive
control algorithms, WSCO automatically manages cooling and balances airflow in areas ofinfluence in the data center hall. The results were significant: WSCO reduced the need for
CRAH units from 72 to 35, restoring the data center’s Tier III rating. The solution also achieved
a 71% savings in kilowatt hours, from 241.7 kW to 69.1 kW. This generated $241,817 in annual
savings, a utility rebate of $150,000 and a simple payback in less than two years.
To top it off, an additional 5% in energy savings was realized at the chiller plant due to a
reduction in required pumping after WSCO was completed.

This document contains a general description of available technical options. Any specific solution(s) will be based ona client’s particular requirements and will be addressed in the contract for the project.

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