Designed to help participating facilities reduce total energy consumption by up to 30% while maintaining reliability and uptime.
Integrates real-time monitoring with automated optimization to identify inefficiencies such as overcooling and underutilization.
Planned modular hardware and software installation to integrate with existing infrastructure and standard DCIM tools.
This proposal outlines the planned development of an intelligent, self-optimizing system to enhance data center network operations in the United States by reducing energy consumption and increasing performance.
Deploys sensors across servers, switches, cooling units, and power distribution to capture temperature, power use, and traffic signals.
Analyzes real-time conditions to detect inefficiencies and issue automated control actions.
Forecasts energy and workload spikes and adjusts operations in advance as the model learns over time.
Links server workloads and energy-efficiency metrics in real time—beyond cooling-only approaches.
Designed to integrate with standard data center infrastructure management tools for monitoring and control.
Uses historical performance data during an initial learning period to tailor optimization to each facility.
The planned system combines sensing, AI-driven control, and predictive optimization to improve energy efficiency while supporting data center performance and reliability.
Collects signals on temperature, power, congestion, and data flow to detect inefficiencies quickly.
Issues commands intended to reduce unnecessary cooling and optimize airflow during low demand.
Planned automated routing adjustments to reduce congestion and avoid overheating hotspots.
Redistributes tasks across underused segments to balance performance and energy demand.
Anticipates spikes and adjusts operations in advance to reduce strain and improve stability.
Designed to reduce energy without compromising uptime, using controlled automation and monitoring.
Planned to deploy with minimal disruption via modular hardware and software components.
Will be validated through controlled tests and pilot deployments with continuous improvement cycles.
A multi-phase plan: Year 1 research and systems design; Years 2–3 development and prototype testing; Years 4–5 implementation and commercial deployment; Year 5+ expansion and continuous innovation.
Planned execution across research, development, and deployment phases, with validation through controlled testing and pilot installations.
The initiative is led by Augusto Feliciano Santos and is planned to be advanced through collaboration with data center operators, public agencies, and sustainability partners.
Projected outcomes will be evaluated through controlled testing and pilot deployments, measuring energy savings, performance, and operational reliability.
We will evaluate energy consumption, thermal stability, and network performance before and after deployment.
Results will inform iterative improvements and integration guidance for broader rollout.
Algorithms will be tested in controlled environments and smaller data centers to validate automation logic.
Findings will guide compatibility enhancements and reliability safeguards.
Planned deployments will include training, monitoring, and support tools to help facilities adopt the system.
Performance and sustainability metrics will be tracked to support expansion decisions.
This executive plan projects measurable sustainability and economic benefits through energy savings, workforce development, and scalable deployment partnerships.
The system is designed to reduce total energy consumption by up to 30% in participating data centers while maintaining uptime and reliability.
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Within five years, the plan targets establishing a pilot operation in an economically depressed area, creating technical/support jobs and partnering with workforce programs for training.
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The long-term vision includes scaling nationwide, expanding into adjacent sectors, and licensing technology to support sustainable digital infrastructure projects.
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Interested in pilot collaboration, evaluation partnerships, or workforce training alignment? Send a message to start the conversation.
Salt Lake City, UT (pilot location planned)
+1 (000) 000-0000 (to be updated)
contact@edcs.com