Data Center Design and Planning

29 Jan 2026 • 7 minute read

Data centers form the backbone of everything from EDA workloads to innovation and AI developments. Designing them properly to achieve near-zero downtime requires carefully navigating data center architecture choices and choosing the appropriate levels of resilience and redundancy throughout. Success in operations also relies on thorough white-space planning to achieve optimized rack layouts to utilize all resources effectively.

Silicon-aware data center design is another critical aspect of the modern efficient data center. As process nodes shrink and packaging techniques concentrate heat, the power density and thermal flux increase, creating new challenges for electrical distribution, cooling, and layout.

Data Center Design Principles

Efficient data center design is built on a foundation of core principles that guide every decision, from the physical location to the choice of equipment.

Reliability and Redundancy: Reliability is the foundation of data center operations. However, rather than depending on a single metric (N vs N+1 vs 2N), availability goals should guide redundancy at different levels, such as infrastructural, electrical pathways, and IT/software resilience.
Scalability and Flexibility: A data center must be able to grow with business demands. A forward-thinking approach prevents costly overhauls.
Security: Protecting physical and digital assets within the data center is paramount. This includes multi-layered physical security (fences, biometrics, surveillance) and robust cybersecurity measures to guard against breaches.
Efficiency: An efficient design minimizes energy consumption by optimizing cooling systems using modern techniques such as simulation and employing power-efficient hardware.

Understanding Tier Standards

When planning a facility, it's important to be aware of the concepts behind tier certification as defined by the Uptime Institute, which verifies infrastructure topology. However, it should be noted these tiers do not aim to improve data center performance beyond improving availability. Engineers should see tier standards as guidelines for infrastructural resilience, which is just one aspect of the goal of operational excellence.

Tier I offers basic capacity without redundancy, while Tier II adds partial backups for key components (cooling, power). Both may require shutdowns for maintenance and carry higher risks of unplanned outages.
Tier III (Concurrent Maintainability) has multiple independent power and cooling paths, with only one active at any given time. With N+1 redundancy, maintenance can be performed without downtime, reaching an average of 99.982% availability (roughly 1.6 hours of annual downtime).
Tier IV (Fault Tolerance) is for mission-critical operations, with fully redundant systems and compartmentalized setups to handle failures. It achieves 99.995% uptime, suitable for sensitive data environments such as finance and healthcare.

Redundancy Models

Engineers must understand "N+1 vs. 2N" comparisons to understand appropriate redundancy for specific workloads:

N+1: One extra component compared with the required amount (e.g., 4 instead of 3 cooling units). Often sufficient for general enterprise workloads where minor risks during maintenance windows are acceptable.
2N: Every critical component has a redundant partner. The standard for critical AI training clusters where interrupting a training run could cost millions.

Thermal Management: Rack Layout, Air Cooling, and Containment

Semiconductor thermal limits define performance thresholds, making cooling the primary challenge in data centers. Higher thermal design power (TDP) levels demand prioritized cooling strategies. Proper server rack arrangement is crucial for efficiency at densities of 15 – 20kW, where effective air cooling depends on strict airflow management, often within the framework of a hot-aisle/cold-aisle setup. This configuration, with racks facing front-to-front (cold) and back-to-back (hot), is a basic design philosophy to help prevent exhaust air recirculation.

Many data centers improve this with containment methods. Cold aisle containment (CAC) encloses the cold aisle to pool supply air. Hot aisle containment (HAC) captures exhaust air and returns it to the cooling units. However, for AI and high-performance computing (HPC) workloads exceeding 20kW, often over 100kW, air cooling alone is not enough to effectively remove the heat.

Hybrid Cooling (Rear-Door Heat Exchangers): Liquid-cooled doors at the back of racks absorb heat from air-cooled servers before it enters the room.
Direct-to-Chip (D2C) Liquid Cooling: Cold plates on CPUs/GPUs circulate dielectric fluid or water to remove heat directly at the source, coupling data center design even more closely to the thermal needs of IT equipment. Air cooling is still required to cool non-D2C-cooled components.
Immersion Cooling: For extreme densities, servers are fully submerged in non-conductive fluid for maximum cooling, requiring significant facility and hardware changes.

Strategic Planning: Brownfield vs. Greenfield

Organizations often face a fundamental choice: upgrading an existing facility or building a new one.

Brownfield Projects: Involve retrofitting existing data centers, which can be quicker and cheaper initially, but face significant constraints. For example, structural columns may hinder optimal rack placement, and floor loading limits might not support heavy liquid-cooled AI racks. Additionally, upgrading power capacity is often restricted by the local utility.
Greenfield Projects: Involve designing and constructing a new data center from the ground up. While requiring larger upfront investment and longer timelines, this offers complete freedom. Engineers can optimize the facility to meet modern requirements, such as reinforced slab floors for heavy equipment, high ceilings for heat stratification, and purpose-built infrastructure for liquid-cooling loops.
White Space Planning: Refers to the usable floor area dedicated to IT equipment. Effective planning prevents stranded capacity – meaning resource utilization is synchronized across power, space, cooling, and network.
High-Density Rack Planning: The rapid adoption of AI and HPC is increasing data center density, causing logistical issues as more compute and network connectivity are squeezed into smaller spaces. High-density AI-compute racks require new approaches to floor loading, power, and cooling.

AI-Ready Data Center Design

AI-ready design necessitates a shift from general-purpose infrastructure to specialized HPC environments. These facilities must support the massive parallel processing capabilities required for training and inferencing. Key requirements include:

Liquid Cooling Integration: Air cooling is physically limited in its ability to remove heat from next-generation AI silicon. Facility designs must include liquid cooling networks with Coolant Distribution Units to provide D2C cooling. Liquid removes 80-95% of the heat generated at the server.
Enhanced Power Density: AI racks today can draw upwards of 100kW. The electrical infrastructure must be robust enough to deliver this power safely and efficiently.
Co-Design of Supporting Infrastructure: Network topology now affects physical layout because high-speed copper cables (such as 800G or 1.6T) have limited reach, requiring switches to be close to all compute nodes.
Structural Reinforcement: The density of AI hardware results in significantly higher floor loading, requiring reinforced slab or raised floor structures.

Designing for AI requires a forward-looking approach that anticipates thermal and power densities far exceeding historical norms.

Limitations of Traditional Methods

Modern data centers are too complex to be planned with simple blueprints or spreadsheets. The interaction between airflow, thermodynamics, electrical distribution, and network cabling requires sophisticated modeling. Manual methods cannot predict how a failure in one cooling unit will affect the temperature of a specific server rack across the room, nor can they accurately model the complex heat distribution patterns in a facility with mixed air and liquid cooling.

Data Center Design Software

To address these challenges, engineers are turning to advanced data center design software. Cadence offers solutions that enable engineers to address planning needs comprehensively through Multiphysics simulation incorporating Computational Fluid Dynamics (CFD). The Cadence Reality Digital Twin Platform enables the creation of precise digital twin models of the data center. It encompasses white space and external environment modelling with liquid flow networks, power networks, and data networks, creating a unified solution for efficient design.

The Cadence Reality Digital Twin Platform generates detailed 3D models to simulate power usage and temperature distributions. By running "what-if" scenarios, engineers can identify potential hot spots and improve cooling effectiveness before a single server is installed. This predictive capability allows for verification and optimization of the integrated data center design so that it is able to handle the energy demands of high-density AI silicon.

See How Your Data Center Will Perform Before You Build or Modify It

Planning a new data center or scaling an existing facility for higher rack densities, liquid cooling, or changing workloads? Connect with Cadence for a data center design assessment or live product demo. Our collaborative approach helps you visualize airflow patterns, uncover thermal risk zones, assess cooling effectiveness, and understand capacity constraints—so you can make confident, data-driven decisions earlier in the design process.

Discover Cadence Data Center Solutions

Cadence Reality Digital Twin Platform to simulate and optimize data center behavior across both design and operational phases.
Cadence Celsius Studio to analyze and manage thermal performance from the rack level up to the full facility.

Data Center Cooling: Thermal Management, CFD, & Liquid Cooling for AI Workloads