Power Flow

Posted on by lechuck park

Power Flow Diagram Analysis

This image illustrates a power flow diagram for a data center or server room, showing the sequential path of electricity from external power sources to the final server equipment.

Main Components:

  1. Intake: External power supply at 154 kV / 22.9 kV with 100MW(MVA) capacity
  2. Transformer: Performs voltage conversion (step down) to make power easier to handle
  3. Generator: Provides backup power during outages, connected to a fuel tank
  4. Transformer #2: Second voltage conversion, bringing power closer to usable voltage (220/380V)
  5. UPS/Battery: Uninterruptible Power Supply with battery backup for blackout protection, showing capacity (KVA) and backup time
  6. PDU/TOB: Power Distribution Unit for connecting to servers
  7. Server: Final power consumption equipment

Key Features:

  • Red circles indicate power switching/distribution points
  • Dotted lines show backup power connections
  • The bottom section details the characteristics of each component:
    • Intake power specifications
    • Voltage conversion information
    • Blackout readiness status
    • Server connection details
    • Power usage status

Summary:

This diagram represents the complete power infrastructure of a data center, illustrating how electricity flows from the grid through multiple transformation and backup systems before reaching the servers. It demonstrates the redundancy measures implemented to ensure continuous operation during power outages, including generators and UPS systems. The power path includes necessary voltage step-down transformations to convert high-voltage grid power to server-appropriate voltages, with switching and distribution points throughout the system. This comprehensive power flow design ensures reliable, uninterrupted power delivery critical for data center operations.

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OOM Killer

Posted on by lechuck park

OOM (Out-of-Memory) Killer

This diagram explains the Linux OOM Killer mechanism:

  1. Memory Request Process:
    • A process requests memory allocation from the operating system.
    • It receives a handler for the allocated memory.
  2. Memory Management System:
    • The operating system manages virtual memory.
    • Virtual memory utilizes physical memory and disk swap space.
    • Linux allows memory overcommitment.
  3. OOM Killer Operation:
    • When physical memory becomes scarce, the OOM Killer is initiated.
    • The OOM Killer selects and terminates “less important” processes based on factors such as memory usage and process priority.
    • This mechanism maintains the overall stability of the system.

Linux OOM Killer is a mechanism that automatically activates when physical memory becomes scarce. It maintains system stability by selecting and terminating less important processes based on memory usage and priority.

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There’s such thing as ‘impossible’.

Posted on by lechuck park

This infographic illustrates a software development philosophy titled “There’s such thing as ‘impossible’.” It emphasizes that there are real limitations in development:

  1. Development process flow:
    • “Machine Code” (represented by binary digits)
    • “Software Dev” (showing code editor)
    • “Application” (showing mobile interface)
    • Arrow pointing to infinity symbol labeled “Unbounded” with a warning sign
  2. Practical constraints:
    • “Reality has no ∞ button. Choose.” (emphasizing limitations exist)
    • Icons representing people and money (resource management)
    • “Everything requires a load” (showing resources are needed)
    • “Energy” and “Time” with cycling arrows (demonstrating finite resources)
  3. Keys to successful development:
    • Clear problem definition (“Clear Definition”)
    • Setting priorities (“Priorities”)
    • Target goals

The overall message highlights that impossibility does exist in software development due to real-world constraints of time, energy, and resources. It emphasizes the importance of acknowledging these limitations and addressing them through clear problem definition and priority setting for effective development.

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Rule-Based vs LLM AI

Posted on by lechuck park

Rule-Based AI vs. Machine Learning: Finding the Fastest Hiking Route

Rule-Based AI

  • A single expert hiker analyzes a map, considering terrain and conditions to select the optimal route.
  • This method is efficient and requires minimal energy (a small number of lunchboxes).

Machine Learning

  • A large number of hikers explore all possible paths without prior knowledge.
  • The fastest hiker’s route is chosen as the optimal path.
  • This approach requires many attempts, consuming significantly more energy (a vast number of lunchboxes).

👉 Comparison Summary

  • Rule-Based AI: Finds the best route through analysis → Efficient, low energy consumption
  • Machine Learning: Finds the best route through trial and error → Inefficient but discovers optimal paths, high energy consumption

with ChatGPT

Rule-base AI vs ML

Posted on by lechuck park

The primary purpose of this image is to highlight the complementary nature of Rule-base AI and Machine Learning (ML), demonstrating the need to integrate these two approaches.

Rule-base AI (Top):

  • Emphasizes the importance of fundamental and ethical approaches
  • Designing strict rules based on human expertise and logical thinking
  • Providing core principles and ethical frameworks

Machine Learning AI (Bottom):

  • Highlighting scalability and innovation through data-driven learning
  • Ability to recognize complex patterns and adaptive learning
  • Potential for generating new insights and solutions

Hybrid Approach:

  • Combining the strengths of both approaches
  • Maintaining fundamental principles and ethical standards
  • Simultaneously achieving innovation and scalability through data-driven learning

The image illustrates the complementary nature of Rule-base AI and Machine Learning (ML). Rule-base AI represents precise, human-crafted logic with limited applicability, while ML offers flexibility and innovation through data-driven learning. The key message is that a hybrid approach combining the fundamental ethical principles of rule-based systems with the scalable, adaptive capabilities of machine learning can create more robust and intelligent AI solutions.

with Claude

CFD & AI/ML

Posted on by lechuck park

CFD (Computational Fluid Dynamics) – Deductive Approach [At Installation]

  • Data Characteristics
    • Configuration Data
    • Physical Information
    • Static Meta Data
  • Features
    • Complex data configuration
    • Predefined formula usage
    • Result: Fixed and limited
    • Stable from engineering perspective

AI/ML – Inductive Approach [During Operation]

  • Data Characteristics
    • Metric Data
    • IoT Sensing Data
    • Variable Data
  • Features
    • Data-driven formula generation
    • Continuous learning and verification
    • Result: Flexible but partially unexplainable
    • High real-time adaptability

Comprehensive Comparison

Harmonious integration of both approaches is key to future digital twin technologies

CFD: Precise but rigid modeling

AI/ML: Adaptive but complex modeling

The key insight here is that both CFD and AI/ML approaches have unique strengths. CFD provides a rigorous, physics-based model with predefined formulas, while AI/ML offers dynamic, adaptive learning capabilities. The future of digital twin technology likely lies in finding an optimal balance between these two methodologies, leveraging the precision of CFD with the flexibility of machine learning.

With Claude