data center – Lechuck Park

DynamoLLM

Posted on 2026-02-032026-02-02 by lechuck park

The provided infographic illustrates DynamoLLM, an intelligent power-saving framework specifically designed for operating Large Language Models (LLMs). Its primary mission is to minimize energy consumption across the entire infrastructure—from the global cluster down to individual GPU nodes—while strictly maintaining Service Level Objectives (SLO).

## 3-Step Intelligent Power Saving

1. Cluster Manager (Infrastructure Level)

This stage ensures that the overall server resources match the actual demand to prevent idle waste.

Monitoring: Tracks the total cluster workload and the number of currently active servers.
Analysis: Evaluates if the current server group is too large or if resources are excessive.
Action: Executes Dynamic Scaling by turning off unnecessary servers to save power at the fleet level.

2. Queue Manager (Workload Level)

This stage organizes incoming requests to maximize the efficiency of the processing phase.

Monitoring: Identifies request types (input/output token lengths) and their similarities.
Analysis: Groups similar requests into efficient “task pools” to streamline computation.
Action: Implements Smart Batching to improve processing efficiency and reduce operational overhead.

3. Instance Manager (GPU Level)

As the core technology, this stage manages real-time power at the hardware level.

Monitoring: Observes real-time GPU load and Slack Time (the extra time available before a deadline).
Analysis: Calculates the minimum processing speed required to meet the service goals (SLO) without over-performing.
Action: Utilizes DVFS (Dynamic Voltage and Frequency Scaling) to lower GPU frequency and minimize power draw.

# Summary

DynamoLLM is an intelligent framework that minimizes LLM energy use across three layers: Cluster, Queue, and Instance.
It maintains strict service quality (SLO) by calculating the exact performance needed to meet deadlines without wasting power.
The system uses advanced techniques like Dynamic Scaling and DVFS to ensure GPUs only consume as much energy as a task truly requires.

#DynamoLLM #GreenAI #LLMOps #EnergyEfficiency #GPUOptimization #SustainableAI #CloudComputing

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To the full Automation

Posted on 2026-02-022026-02-01 by lechuck park

This visual emphasizes the critical role of high-quality data as the engine driving the transition from human-led reactions to fully autonomous operations. This roadmap illustrates how increasing data resolution directly enhances detection and automated actions.

Comprehensive Analysis of the Updated Roadmap

1. The Standard Operational Loop

The top flow describes the current state of industrial maintenance:

Facility (Normal): The baseline state where everything functions correctly.
Operation (Changes) & Data: Any deviation in operation produces data metrics.
Monitoring & Analysis: The system observes these metrics to identify anomalies.
Reaction: Currently, a human operator (the worker icon) must intervene to bring the system “Back to the normal”.

2. The Data Engine

The most significant addition is the emphasized Data block and its impact on the automation cycle:

Quality and Resolution: The diagram highlights that “More Data, Quality, Resolution” are the foundation.
Optimization Path: This high-quality data feeds directly into the “Detection” layer and the final “100% Automation” goal, stating that better data leads to “Better Detection & Action”.

3. Evolution of Detection Layers

Detection matures through three distinct levels, all governed by specific thresholds:

1 Dimension: Basic monitoring of single variables.
Correlation & Statistics: Analyzing relationships between different data points.
AI Analysis with AI/ML: Utilizing advanced machine learning for complex pattern recognition.

4. The Goal: 100% Automation

The final stage replaces human “Reaction” with autonomous “Action”:

LLM Integration: Large Language Models are utilized to bridge the gap from “Easy Detection” to complex “Automation”.
The Vision: The process culminates in 100% Automation, where a robotic system handles the recovery loop independently.
The Philosophy: It concludes with the defining quote: “It’s a dream, but it is the direction we are headed”.

Summary

The roadmap evolves from human intervention (Reaction) to autonomous execution (Action) powered by AI and LLMs.
High-resolution data quality is identified as the core driver that enables more accurate detection and reliable automated outcomes.
The ultimate objective is a self-correcting system that returns to a “Normal” state without manual effort.

#HyperAutomation #DataQuality #IndustrialAI #SmartManufacturing #LLM #DigitalTwin #AutonomousOperations #AIOp

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Predictive/Proactive/Reactive

Posted on 2026-01-282026-01-27 by lechuck park

The infographic visualizes how AI technologies (Machine Learning and Large Language Models) are applied across Predictive, Proactive, and Reactive stages of facility management.

1. Predictive Stage

This is the most advanced stage, anticipating future issues before they occur.

Core Goal: “Predict failures and replace planned.”
Icon Interpretation: A magnifying glass is used to examine a future point on a rising graph, identifying potential risks (peaks and warnings) ahead of time.
Role of AI:
- [ML] The Forecaster: Analyzes historical data to calculate precisely when a specific component is likely to fail in the future.
- [LLM] The Interpreter: Translates complex forecast data and probabilities into plain language reports that are easy for human operators to understand.
Key Activity: Scheduling parts replacement and maintenance windows well before the predicted failure date.

2. Proactive Stage

This stage focuses on optimizing current conditions to prevent problems from developing.

Core Goal: “Optimize inefficiencies before they become problems.”
Icon Interpretation: On a stable graph, a wrench is shown gently fine-tuning the system for optimization, protected by a shield icon representing preventative measures.
Role of AI:
- [ML] The Optimizer: Identifies inefficient operational patterns and determines the optimal configurations for current environmental conditions.
- [LLM] The Advisor: Suggests specific, actionable strategies to improve efficiency (e.g., “Lower cooling now to save energy”).
Key Activity: Dynamically adjusting system settings in real-time to maintain peak efficiency.

3. Reactive Stage

This stage deals with responding rapidly and accurately to incidents that have already occurred.

Core Goal: “Identify root cause instantly and recover rapidly.”
Icon Interpretation: A sharp drop in the graph accompanied by emergency alarms, showing an urgent repair being performed on a broken server rack.
Role of AI:
- [ML] The Filter: Cuts through the noise of massive alarm volumes to instantly isolate the true, critical issue.
- [LLM] The Troubleshooter: Reads and analyzes complex error logs to determine the root cause and retrieves the correct Standard Operating Procedure (SOP) or manual.
Key Activity: Rapidly executing the guided repair steps provided by the system.

Summary

The image illustrates the evolution of data center operations from traditional Reactive responses to intelligent Proactive optimization and Predictive maintenance.
It clearly delineates the roles of AI, where Machine Learning (ML) handles data analysis and forecasting, while Large Language Models (LLMs) interpret these insights and provide actionable guidance.
Ultimately, this integrated AI approach aims to maximize uptime, enhance energy efficiency, and accelerate incident recovery in critical infrastructure.

#DataCenter #AIOps #PredictiveMaintenance #SmartInfrastructure #ArtificialIntelligence #MachineLearning #LLM #FacilityManagement #ITOps

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Power-Driven Predictive Cooling Control (Without Server Telemetry)

Posted on 2026-01-222026-01-21 by lechuck park

For a Co-location (Colo) service provider, the challenge is managing high-density AI workloads without having direct access to the customer’s proprietary server data or software stacks. This second image provides a specialized architecture designed to overcome this “data blindness” by using infrastructure-level metrics.

1. The Strategy: Managing the “Black Box”

In a co-location environment, the server internal data—such as LLM Job Schedules, GPU/HBM telemetry, and Internal Temperatures—is often restricted for security and privacy reasons. This creates a “Black Box” for the provider. The architecture shown here shifts the focus from the Server Inside to the Server Outside, where the provider has full control and visibility.

2. Power as the Primary Lead Indicator

Because the provider cannot see when an AI model starts training, they must rely on Power Supply telemetry as a proxy.

The Power-Heat Correlation: As indicated by the red arrow, there is a near-instantaneous correlation between GPU activity and power draw ($kW$).
Zero-Inference Monitoring: By monitoring Power Usage & Trends at the PDU (Power Distribution Unit) level, the provider can detect a workload spike the moment it happens, often several minutes before the heat actually migrates to the rack-level sensors.

3. Bridging the Gap with ML Analysis

Since the provider is missing the “More Proactive” software-level data, the Analysis with ML component becomes even more critical.

Predictive Modeling: The ML engine analyzes power trends to forecast the thermal discharge. It learns the specific “power signature” of AI workloads, allowing it to initiate a Cooling Response (adjusting Flow Rate in LPM and $\Delta T$) before the ambient temperature rises.
Optimization without Intrusion: This allows the provider to maintain a strict SLA (Service Level Agreement) and optimize PUE (Power Usage Effectiveness) without requiring the tenant to install agents or share sensitive job telemetry.

Comparison for Co-location Providers

Feature	Ideal Model (Image 1)	Practical Colo Model (Image 2)
Visibility	Full-stack (Software to Hardware)	Infrastructure-only (Power & Air/Liquid)
Primary Metric	LLM Job Queue / GPU Temp	Power Trend ($kW$) / Rack Density
Tenant Privacy	Low (Requires data sharing)	High (Non-intrusive)
Control Precision	Extremely High	High (Dependent on Power Sampling Rate)

Summary

For Co-location providers, this architecture solves the lack of server-side visibility by using Power Usage ($kW$) as a real-time proxy for heat generation.
By monitoring Power Trends at the infrastructure level, the system can predict thermal loads and trigger Cooling Responses before temperature sensors even react.
This ML-driven approach enables high-efficiency cooling and PUE optimization while respecting the strict data privacy and security boundaries of multi-tenant AI data centers.

Hashtags

#Colocation #DataCenterManagement #PredictiveCooling #AICooling #InfrastructureOptimization #PUE #LiquidCooling #MultiTenantSecurity

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Peak Shaving

Posted on 2026-01-122026-01-12 by lechuck park

“Power – Peak Shaving” Strategy

The image illustrates a 5-step process for a ‘Peak Shaving’ strategy designed to maximize power efficiency in data centers. Peak shaving is a technique used to reduce electrical load during periods of maximum demand (peak times) to save on electricity costs and ensure grid stability.

1. IT Load & ESS SoC Monitoring

This is the data collection and monitoring phase to understand the current state of the system.

Grid Power: Monitoring the maximum power usage from the external power grid.
ESS SoC/SoH: Checking the State of Charge (SoC) and State of Health (SoH) of the Energy Storage System (ESS).
IT Load (PDU): Measuring the actual load through Power Distribution Units (PDUs) at the server rack level.
LLM/GPU Workload: Monitoring the real-time workload of AI models (LLM) and GPUs.

2. ML-based Peak Prediction

Predicting future power demand based on the collected data.

Integrated Monitoring: Consolidating data from across the entire infrastructure.
Machine Learning Optimization: Utilizing AI algorithms to accurately predict when power peaks will occur and preparing proactive responses.

3. Peak Shaving Via PCS (Power Conversion System)

Utilizing physical energy storage hardware to distribute the power load.

Pre-emptive Analysis & Preparation: Determining the “Time to Charge.” The system charges the batteries when electricity rates are low.
ESS DC Power: During peak times, the stored Direct Current (DC) in the ESS is converted to Alternating Current (AC) via the PCS to supplement the power supply, thereby reducing reliance on the external grid.

4. Job Relocation (K8s/Slurm)

Adjusting the scheduling of IT tasks based on power availability.

Scheduler Decision Engine: Activated when a peak time is detected or when ESS battery levels are low.
Job Control: Lower priority jobs are queued or paused, and compute speeds are throttled (power suppressed) to minimize consumption.

5. Parameter & Model Optimization

The most advanced stage, where the efficiency of the AI models themselves is optimized.

Real-time Batch Size Adjustment: Controlling throughput to prevent sudden power spikes.
Large Model -> sLLM (Lightweight): Transitioning to smaller, lightweight Large Language Models (sLLM) to reduce GPU power consumption without service downtime.

Summary

The core message of this diagram is that High-Quality/High-Resolution Data is the foundation for effective power management. By combining hardware solutions (ESS/PCS), software scheduling (K8s/Slurm), and AI model optimization (sLLM), a data center can significantly reduce operating expenses (OPEX) and ultimately increase profitability (Make money) through intelligent peak shaving.

#AI_DC #PowerControl #DataCenter #EnergyEfficiency #PeakShaving #GreenIT #MachineLearning #ESS #AIInfrastructure #GPUOptimization #Sustainability #TechInnovation

DC Digitalizations with ISA-95

Posted on 2025-12-242025-12-23 by lechuck park

5-Layer Breakdown of DC Digitalization

M1: Sensing & Manipulation (ISA-95 Level 0-1)

Focus: Bridging physical assets with digital systems.
Key Activities: Ultra-fast data collection and hardware actuation.
Examples: High-frequency power telemetry (ms-level), precision liquid cooling control, and PTP (Precision Time Protocol) for synchronization.

M2: Monitoring & Supervision (ISA-95 Level 2)

Focus: Holistic visibility and IT/OT Convergence.
Key Activities: Correlating physical facility health (cooling/power) with IT workload performance.
Examples: Integrated dashboards (“Single Pane of Glass”), GPU telemetry via DCGM, and real-time anomaly detection.

M3: Manufacturing Operations Management (ISA-95 Level 3)

Focus: Operational efficiency and workload orchestration.
Key Activities: Maximizing “production” (AI output) through intelligent scheduling.
Examples: Topology-aware scheduling, AI-OEE (maximizing Model Flops Utilization), and predictive maintenance for assets.

M4: Business Planning & Logistics (ISA-95 Level 4)

Focus: Strategic planning, FinOps, and cost management.
Key Activities: Managing business logic, forecasting capacity, and financial tracking.
Examples: Per-token billing, SLA management with performance guarantees, and ROI analysis on energy procurement.

M5: AI Orchestration & Optimization (Cross-Layer)

Focus: Autonomous optimization (AI for AI Ops).
Key Activities: Using ML to predictively control infrastructure and bridge the gap between thermal inertia and dynamic loads.
Examples: Predictive cooling (cooling down before a heavy job starts), Digital Twins, and Carbon-aware scheduling (ESG).

Summary of Core Concepts

IT/OT Convergence: Integrating Information Technology (servers/software) with Operational Technology (power/cooling).
AI-OEE: Adapting the “Overall Equipment Effectiveness” metric from manufacturing to measure how efficiently a DC produces AI models.
Predictive Control: Moving from reactive monitoring to proactive, AI-driven management of power and heat.

#DataCenter #DigitalTransformation #ISA95 #AIOps #SmartFactory #ITOTConvergence #SustainableIT #GPUOrchestration #FinOps #LiquidCooling

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Predictive Count/Resolve Time for .

Posted on 2025-12-192025-12-17 by lechuck park

the “Predictive Count/Resolve Time” Diagram

This diagram illustrates the workflow of IT Operations or System Maintenance, specifically comparing Predictive Maintenance (Proactive) versus Recovery/Reactive (Reactive) processes.

It is divided into two main flows: the Preventive Flow (Left) and the Reactive Flow (Right).

1. Left Flow: Predictive Maintenance

This represents the ideal process where anomalies are detected and addressed before a full system failure occurs.

Process:
- Work Changes / Monitoring: Routine operations and continuous system monitoring.
- Anomaly: The system exhibits abnormal patterns, but it hasn’t failed yet.
- Detection (Awareness): Monitoring tools or operators detect this anomaly.
- Predictive Maintenance: Maintenance is performed proactively to prevent the fault.
Key Performance Indicators (KPIs):
- Count: The number of times predictive maintenance was performed.
- PTM Success Rate: A metric to measure success (e.g., considered successful if no disability/failure occurs within 14 days after the predictive maintenance).

2. Right Flow: Reactive Recovery

This is the response process when an anomaly is missed, leading to an actual system failure.

Process:
- Abnormal → Alert: The condition worsens, triggering an alert. The time taken to reach this point is MTTD (Mean Time To Detect).
- Fault Down: The system actually fails or goes down.
- Propagation Time (to Experts): The time it takes to escalate the issue to the right experts. This relates to MTTE (Mean Time To Engage Expert).
- Recovery Time: The time taken by experts to fix the issue.
Key Performance Indicators (KPIs):
- MTTR (Mean Time To Resolve/Repair): The total time from the failure (Fault Down) until the system is fully recovered. Reducing this time is a critical operational goal.

3. Summary & Key Takeaway

The diagram visually emphasizes the importance of “preventing issues before they happen (Left)” rather than “fixing them after they break (Right).”

Flow Logic: If an ‘Anomaly’ is successfully ‘Detected’, it leads to ‘Predictive Maintenance’. If missed, it escalates to ‘Abnormal’ and results in a ‘Fault Down’.
Goal: The objective is to minimize MTTR (downtime) on the right side and increase the PTM Count (proactive prevention) on the left side to ensure high system availability.

#DevOps #SRE #PredictiveMaintenance #MTTR #IncidentManagement #ITOperations #SystemMonitoring #DisasterRecovery #MTTD #TechMaintenance

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