The provided infographic visually explains a new business model where data center resources are used and billed flexibly, much like cloud computing.
At the top, the main headline reads, “The Future of Data Centers: Use and Pay Like the Cloud,” accompanied by a subheadline introducing an “On-Demand Power & Cooling Control and As-a-Service Pay-As-You-Go Model”.
On the left side, the graphic illustrates a user’s hand interacting with an “API Control UI” on a tablet device, featuring toggle switches for “POWER” and “COOLING”. This interface connects via a “DATA STREAM” to both “Your Datacenter,” depicted by colorful server racks, and a “Virtual Dashboard” displaying performance graphs.
The right side details the four core features of this service:
01. Dynamic Power Bursting: Represented by a lightning bolt icon, it explains that users can temporarily raise their power limits via API when needed. It allows flexible scaling of per-rack power during heavy AI training or complex compute workloads, ensuring users only pay for what they use.
02. Custom Cooling / Thermal SLA: Denoted by a thermometer icon, this feature allows users to directly control liquid cooling (DLC) flow rates and temperatures via software. It enables lower temperatures during high-performance runs and saves energy during standard operations.
03. Pay-As-You-Go Billing: Illustrated with an invoice and coins, it highlights the ability to lighten fixed contract rent. Users pay proportionally for the exact amount of power and cooling consumed and receive credits (incentives) back when they reduce loads during off-peak hours.
04. Safety Guardrails: Shown with a shield and lock icon, this point emphasizes uncompromised safety. Even when tenants hold control, the data center’s core facility and infrastructure remain fully protected 24/7 by failsafe hardware override boundaries.
At the bottom, the graphic concludes with the slogan, “Now, freely adjust your data center’s energy and temperature to fit your business,” and identifies the system as a “[ Landlord – Tenant Collaboration Platform ]”.
Summary This image serves as an advertisement-style infographic showcasing an innovative “As-a-Service” infrastructure model, where data center tenants can independently control their power and cooling resources via APIs, pay strictly based on their actual consumption, and operate safely within strict hardware boundaries.
The central theme of the image is captured by the prominent text in the sky: “The Foundation Never Betrays You.” Just below it, a supporting phrase states, “True speed and innovation are possible only on a solid foundation.”
The Bottom Section: The Solid Foundation (80%) The lower portion of the structure resembles an ancient, massive stone base. This represents the essential framework (Data, Protocol, Infrastructure) that accounts for 80% of the entire system. This foundation consists of four key pillars:
Data Collection: Sensors, Logs, Metrics, Events.
Standard Protocol: SNMP, Redfish, gRPC/API, Open Standards.
Data Quality & Governance: Accuracy, Consistency, Security, CMDB.
The Top Section: Innovation & High Speed (20%) Atop the stone foundation sits a transparent, modern data center building. This represents the innovation layer, making up the remaining 20%, which symbolizes AI Agent, Orchestration, Automation, and Performance.
The sky on the right lists the resulting benefits: Faster Response, Higher Efficiency, Greater Innovation, and Self-Operating.
Surrounding Background & Details
Bottom Left: A boy with a backpack and a cat sit on a grassy hill, looking up at the massive structure. A nearby signpost reads, “Small details build big results.”
Right Background: Along with the phrase “From a Stable Foundation to Infinite Possibilities,” a spacecraft (or jet) is depicted flying rapidly over a futuristic city, hinting at technological advancement and the future.
π Summary
This conceptual illustration visually emphasizes that in order for cutting-edge technologies (such as AI and automation) to operate successfully and achieve true innovation, they must be supported by a rock-solid, unseen foundation (80%) comprising underlying infrastructure, data collection, and standard protocols.
The table categorizes the key metrics required to safely manage a Coolant Distribution Unit (CDU) and its secondary cooling loop using a 25% Propylene Glycol (PG25) mixture into two main sections:
Real-time Monitoring: This section focuses on physical states that require immediate attention. It includes Temperature (ΞT), Pressure Drop (ΞP), Flow Rate, Leak Detection, and Conductivity.
Example: It highlights that a leak at the rack or joints poses a risk of IT short circuits and fire, dictating immediate actions such as triggering the Emergency Power Off (EPO) and shutting off the loop valves.
Periodic Maintenance: This section covers the chemical and biological fluid quality checks that must be performed regularly. It sets targets for PG Concentration (25% Β± 2%), pH Level (7.5 ~ 9.0), Corrosion Inhibitor, Microbes (< 1,000 CFU/ml), and Turbidity/Filtration (< 50 ΞΌm).
Example: To mitigate the risk of biofilms clogging the tightly packed microchannels, it advises taking actions like biocide shock dosing or checking the UV sterilization system.
π Summary
This document is a practical, structured matrix designed for data center operators. It explicitly outlines the essential operational telemetry, potential hardware and fluid risks, and precise mitigation actions needed to maintain a reliable and highly efficient liquid cooling infrastructure.
Data & Knowledge Driven AI Agent for Data Center Operations
This illustration describes how data center operations can evolve from facility data β operational knowledge β AI Agent β automated operations.
The key message is not simply that AI controls data center equipment.
Rather, it shows how AI Agents can connect operational data with accumulated human knowledge, understand operational situations, reason about incidents, and support or automate operational actions.
1. Facilities & Systems
The process starts with the physical infrastructure and operational systems of the data center.
The illustration represents:
Power
Cooling
Network
IT / GPU
Security
Environment
Sensors and systems continuously generate operational information.
Systems such as DCIM, NMS, BMS, and log platforms collect this information.
In simple terms:
Facilities generate information, and systems collect it.
2. Data β From Information to Metrics
Facility information is transformed into measurable operational data.
For example:
Temperature β 42.3Β°C
Power Load β 12.6 MW
Water Flow β 3.2 mΒ³/h
Utilization β 78%
The important point is that the AI Agent uses both:
Real-time Data + Historical Data
Real-time data tells the Agent what is happening now, while historical data provides the operational context and previous experience.
3. Event β Turning Numbers into Meaning
Raw numbers are not always meaningful to operators.
Therefore, data is transformed into understandable events.
For example:
GPU Inlet Temperature is high (42.3Β°C)
Now the numerical value has become a meaningful operational event.
The event also contains context such as:
What happened + Where + When + Severity + Impact
This is important because the AI Agent does not need to operate only on raw numbers. It can reason about meaningful operational situations.
4. Response β Operational Knowledge
When an event occurs, traditional operations rely on manuals and experienced operators.
The illustration represents this knowledge through:
Runbook
MOP
EOP
SOP
Best Practices
A typical response process can be:
Check β Analyze β Execute β Verify
This represents the transformation of human experience into reusable operational knowledge.
Human Experience β Documentation β Operational Knowledge
This knowledge becomes one of the most important assets for the AI Agent.
5. Final Decision β Judgment & Action
The final stage goes beyond detecting an event.
The operational process becomes:
Root Cause β Action Plan β Service Restore β Record
Traditionally, experienced operators perform much of this reasoning manually.
With an AI Agent, operational data and knowledge can be combined to support:
Root-cause analysis
Action recommendations
Runbook execution
Operator guidance
Controlled automation
In a real data center, however, autonomous action should be governed by policies, safety controls, and human approval where required.
The Center of the Illustration β AI Agent
The AI Agent sits at the center because it connects Data and Knowledge.
Data
Operational Data
Facility & Asset Data
Event & Incident Data
Historical Cases
Asset / Relationship Data
Knowledge
Manuals
Runbooks
SOP / MOP / EOP
Domain Knowledge
Best Practices
Past Cases & Lessons
The Agent combines these two layers to perform:
Learn β Reason β Act
A simple way to express the concept is:
Data tells the Agent what is happening. Knowledge tells the Agent what it means and what to do.
The Core Message
The most important point of the illustration is that the AI Agent itself is not the foundation.
The real foundation is:
Data β Knowledge β AI Agent β Operations
Without accurate data, the Agent cannot reliably understand the current state.
Without structured operational knowledge, the Agent cannot reliably determine what the situation means or what response is appropriate.
Therefore, the real objective of AI-enabled data center operations is not simply:
βDeploy AI.β
It is:
βMake operational data and knowledge usable by AI.β
The Transformation of Data Center Operations
The bottom of the illustration shows:
Data-Driven β Knowledge-Centric β AI-Powered
This represents the evolution of operational models.
Traditional Operations
Human β Data β Manual Analysis β Manual Action
AI Agent-Based Operations
Data + Knowledge β AI Agent β Reasoning β Recommended / Controlled Action
The role of people does not disappear.
Instead, it changes.
AI handles repetitive monitoring, analysis, and operational assistance, while people focus more on judgment, decision-making, exception handling, and continuous improvement.
This is why the final concept is:
People + AI
rather than simply AI replaces People.
One-Sentence Summary
By connecting data generated from data center facilities with operational knowledge, an AI Agent can understand, reason, and support or automate operational actionsβtransforming human-centered operations into data- and knowledge-driven intelligent operations.
Response Speed: Ranges from microseconds to 1 second, offering the most immediate reaction.
Independence: Highest (Enables standalone operation even if communication drops entirely).
Failure Impact: Results in an immediate shutdown of the specific equipment.
Responsible Scope: Handled by the equipment manufacturer.
PLC / DDC
Response Speed: Milliseconds to several seconds.
Independence: High (Base operations continue even if the upper layer stops).
Failure Impact: Can cause power outages or temperature increases.
Responsible Scope: Handled by electrical and mechanical contractors.
BAS / EPMS
Response Speed: Takes 1 to 30 seconds.
Independence: Medium (Field-level control survives even if the upper system stops).
Failure Impact: Causes “blindness” (loss of monitoring visibility), though physical operation continues.
Responsible Scope: Managed by automation system integrators (SIs).
DCIM
Response Speed: Operates on a time scale of minutes.
Independence: Low (The facility can continue operating even without it).
Failure Impact: Leads to management inefficiencies such as increased electricity costs rather than immediate outages.
Responsible Scope: Handled by IT solution companies.
Core Message
The note at the bottom highlights a critical paradigm shift: rather than debating whether to choose PLC or DDC based on theory, engineering teams must focus on empirically verifying worst-case latency through actual measurements.
Summary
Automation control architectures are strictly layered by response speed and independence, requiring engineers to prioritize measured worst-case latency verification over conventional component selection debates.
This image file (image_a2495a.png) is a presentation slide titled “PG25 Metrics(1).” It explains the chemical composition of PG25, a cooling fluid often used in liquid cooling systems, and details the key metrics required to manage it effectively.
1. Composition of PG25 (Top Section)
PG25 is defined as a mixture of two primary components.
Deionized Water: 75%
Inhibited Propylene Glycol: 25%
This specific formula is described as a “Safe (Non-toxic) Antifreeze.”
2. Management Metrics (Bottom Table)
The table below categorizes the management metrics into four distinct phases based on frequency and purpose:
Real-time:
Metrics: Temperature change ($\Delta T$)
Location/Reference: CDU (Coolant Distribution Unit) Supply / Return lines
Monitoring:
Metrics: Pressure Drop ($\Delta P$), Flow Rate (LPM/GPM), Leak Detection, and Conductivity
Location/Reference: CDU pump inlet/outlet and manifold, Main loop piping and rack branches, Rack bottom pan and pipe joints, Internal CDU sensor
Periodic:
Metrics: PG (Propylene Glycol) Concentration (%)
Location/Reference: Maintained at 25% ($\pm 2\%$)
Maintenance:
Metrics: pH Level, Corrosion Inhibitor
Location/Reference: pH level kept between 7.5 and 9.0; Corrosion inhibitors managed per Manufacturer’s Recommendation (e.g., Azole)
π Summary
This slide provides a systematic guide for maintaining the optimal condition of PG25 cooling fluid (75% Deionized Water + 25% Propylene Glycol) used in liquid cooling applications. It breaks down the fluid management process into Real-time, Monitoring, Periodic, and Maintenance phases, clearly outlining the essential metrics (temperature, pressure, flow rate, concentration, pH) and target values for each stage.
