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Building Futuristic Data and AI Development Centers
Introduction:
Data and Development Centers are critical to any company, country-governments, culture, social media, ... etc. These centers come with a hefty price and their running and maintenance are even heftier.

Issues with Data Centers:
Data centers face numerous challenges, including energy consumption, environmental impact, cybersecurity threats, and physical infrastructure issues like power outages and cooling failures. Other significant concerns include scalability, sustainability, and rising operational costs.

Issues with Development Center:
Development centers face a variety of challenges, including issues with communication, cultural differences, time zones, and geopolitical instability. These challenges can affect the effectiveness of offshore development teams and the overall success of projects.

Issues with AI Development Center:
AI data centers face a number of significant challenges and issues. These are categorized as:

       • High Energy Consumption
       • Water Usage
       • High Capital Expenditure
       • Operational Costs
       • Workforce and Skills Gap
       • Demand for Specialized Talent
       • Labor Shortages

How Governments view Data Centers:
Governments view data centers as critical infrastructure for a variety of reasons, including their role in storing and processing essential government data, supporting public services, and ensuring national security. Data centers are also seen as important for economic development, attracting investment, and fostering innovation. Furthermore, governments are increasingly concerned about the environmental impact and sustainability of these facilities.

Issues with Data Centers:
Searching the internet for Data Centers issues, we literally ran into over 50 issues if not more.
We summarize all the issues into the following:

       1. Cooling and Energy Efficiency
       2. Dwindling power availability
       3. Need To Optimize Software and Hardware
       4. Increased Complexity of Hybrid and Multi-Cloud Environments
       5. Skilled Workforce Shortage
       6. Security and Cyber Threats
       7. Intelligent Hardware Security
       8. Staffing shortages
       9. Expensive outages
       10. Facility constraints

We as IT professionals, we can categorize the listed issues into:

       • Vertical
       • Horizontal

Both Vertical and Horizontal Scaling is not fully understood by the IT community:

       • Vertical means each Unit is modified to have more power, functionality, production, flexibility, ... etc.
       • Horizontal means add more units.

Vertical – Internal Structure:

       1. Cooling and Energy Efficiency
       2. Skilled Workforce Shortage
       3. Security and Cyber Threats
       4. Intelligent Hardware Security
       5. Staffing shortages
       6. Need To Optimize Software and Hardware
       7. Increased Complexity of Hybrid and Multi-Cloud Environments

Horizontal – External Structure:

       1. Expensive outages
       2. Facility constraints
       3. Dwindling power availability

Our Strategy:

       Management + Energy + Security + Performance + DevOps + AI + Storge + Maintenance +
       Services + Communication + Automated Updates + Optimum Size

Our strategy is building a solid automated AI support foundation.
As for the cost, our focus would be reducing the energy and environment impact.
As for the future, we need to revisit our cost, performance, maintenance and updates.
The following are Points of Interests:

Energy:

       1. Processing Servers – produce Heat
       2. Energy consumption – to run data centers
       3. Use solar panels for energy
       4. Use windmill for energy
       5. Self-sufficient Energy production
       6. Backup Diesel Generators
       7. Cooling
       8. Using water cooling system
       9. Locations
       10. Distributed system
       11. New Building structure for better ventilation
       12. Power consumption and environment impact
       13. Water purification plants

How to Generate Electricity Using Sees or Ocean:
Electricity can be generated from the sea by harnessing various forms of ocean energy, including tides, waves, currents, and thermal differences between warm surface water and cold deep water. This can be done through devices like tidal turbines, wave energy converters, and ocean thermal energy conversion (OTEC) systems.

Performance – DevOps Support:

       1. High-performance environmental systems
       2. Better design of servers
       3. Compression-encryption reduce data size and security
       4. Data compression and encryption
       5. Using text files instead of database
       6. New software approaches
       7. Bare-Metal server restructure
       8. Server structure - CPU and Core processes
       9. Internal Structure and raking servers and equipment

AI:

       1. AI processes
       2. AI for building security
       3. Robots for physical work replacing human
       4. AI and Automation of management - running the place
       5. Controlling and maintaining AI
       6. Physical and Cyber Security using AI
       7. Bare-metal structure to meet AI processing demands
       8. Keeping and maintaining the dynamic AI processing
       9. Using and implementing market AI products
       10. AI Management System - Managing AI

Management and Tracking:

       1. System and Products management processes and tracking with AI support
       2. Updates and storing management

Maintenance:

       1. Software and hardware automated AI processes
       2. Building (cooling, power, … etc.) maintenance with AI support
       3. Using Robots
       4. Retraining of staff

Security:

       1. Monitoring
       2. Using Robots
       3. Cybersecurity

Communication:

       1. Satellite
       2. Ground wiring
       3. Connectivity and Network Latency Issues
       4. Virtual Edge Computing with Machine Learning

Services:

       1. Specialty servers based on the business and demands
       2. Data Storage
       3. Continuous updating of all the services
       4. Virtual Edge Computing with Machine Learning

Optimum Size:
What is the size of a typical data center?
Average onsite data center: is between 2,000 and 5,000 servers.
It's square footage: could vary from between 20,000 square feet and 100,000 square feet.
Energy draw: Around 100 MW.

In a nutshell, the optimum size is a balance of:

       1. Power supply
       2. Performance
       3. Cost
       4. Scalability
       5. Sustainability

We believe the optimum size should be based on:

       1. Cooling System
       2. Self-sufficient power generation
       3. Water purification plant using AI data and development center head production

Distributed Network of AI Data Centers and Virtual Edge Computing with Machine Learning:
A distributed network of AI data centers refers to a system where AI workloads are processed and data is stored across multiple geographically dispersed data centers, rather than relying on a single centralized facility.

Benefits of a distributed AI data center network:

       1. Reduced Latency
       2. Increased Resilience
       3. Improved Scalability
       4. Enhanced Data Sovereignty
       5. Optimized Cost

As for Virtual Edge Computing with Machine Learning, which would help in remote support of businesses with their own onsite facilities. For example, a bank branch would have its own Virtual Edge Computing with Machine Learning as an added processing and updating processing power and update support.

Note:
We are not sure if our proposed Distributed Network of AI Data Centers and Virtual Edge Computing with Machine Learning would be able to replace GoldenGate services???

Government Involvement:
According to Google search:
Government involvement with AI data centers encompasses a range of activities, including policy setting, funding, regulation, and even direct development, all aimed at fostering AI innovation, ensuring national security, and promoting economic growth.

In summary:
Government involvement in AI data centers is multifaceted, ranging from establishing broad policy frameworks and providing financial support, to regulating AI applications and even engaging in direct development and deployment. This involvement aims to foster responsible innovation, address potential risks, and ensure that the benefits of AI are widely shared.