How Artificial Intelligence Is Impacting the Data Center Industry

As the artificial intelligence (AI) industry grows by leaps and bounds, how we manage and configure data centers is changing. Traditional models often struggle to meet the unique computing challenges of AI, including extensive heat generation and power consumption.

Moving forward, building and configuring data centers to address the needs of AI will be a crucial element of supporting AI, ranging from mission-critical applications to large-scale consumer adoption. Data centers can also use AI to support management efforts and facilitate exceptional efficiency levels. Let’s take a closer look at the impact of artificial intelligence on data centers and how you can prepare for it in your facility.

The Impact of AI on Data Center Strategies

From a broad perspective, AI’s impact on data centers starts with changing priorities. AI encompasses several technologies, including those that require low levels of latency, like robotics. Edge computing will likely become a more prominent strategy, particularly near cities that may benefit from fast response times during user interactions.

These low-latency applications often address the inference stage of AI, where it puts its knowledge to work, but the training stage also requires unique data center environments. While current data center models focus heavily on resilience and redundancy, AI training prioritizes aspects like cost, efficiency and power usage effectiveness (PUE).

With AI growing so rapidly, the overall demand for this technology is also increasing the demand for data centers. Of course, space is limited, so aspects like density and new configurations will likely be priorities moving forward.

New Technologies for AI in Data Centers

To meet these developments and demands, many data centers are looking more closely at the technology available and whether the new benefits of AI outweigh the previous challenges of using advanced strategies.

Cooling Technologies

More demand means more servers to run AI applications. Increasing density means increasing heat, so more sophisticated data center cooling mechanisms become essential to accommodate the AI-driven workload. Some techniques are gaining traction, such as:

• Liquid cooling
• Immersive cooling
• Direct-to-chip liquid cooling

Previous workloads and density requirements may have only required these technologies in specific circumstances, but as AI proliferates, improved cooling may become a mainstay of most data centers.

Dynamic PUE and Cooling Systems

While data centers shift to meet customers’ AI needs, they can also use AI to support their own operations, namely through real-time optimization. Power is the greatest driver of operating costs for most data center operators by a large margin. AI can tailor the parameters of cooling units and power distribution to match real-time demands in various units and zones. It dynamically optimizes delivery for conditions to improve efficiency, reduce costs and boost performance.

In the traditional model, data centers typically deliver power and cooling uniformly, even though it isn’t efficient because the costs of real-time adjustments are much higher. These processes are time-consuming and resource-intensive, but AI’s monitoring capabilities can significantly reduce the costs of dynamic optimization systems. AI can also monitor humidity and temperature to keep a facility running smoothly.

Evolving Configurations

New technology and new priorities naturally lead to new data center configurations. Some aspects driving these configurations include:

• Space utilization: With increased density and low-latency applications, many data centers focus on configurations that maximize a small footprint and expand capabilities without sprawl.
• Sustainability: Environmental concerns should drive planning for AI and data centers. Optimizing power consumption, improving the life span of equipment and implementing alternative or innovative energy systems are a few ways data centers can prioritize eco-friendly practices.
• Redundancies: Newer configuration tactics may reduce the space required for data halls, server racks and other components, altering how data centers deliver redundant services.

Evolving configurations can also affect the criteria developers use for finding data center locations, making elements like proximity and cooling technology more important.

Staffing Supports

Staffing remains one of the biggest challenges for data center operators, with over half of respondents to an Uptime Institute survey citing difficulties finding qualified candidates. AI could ease these challenges by filling in gaps, such as 24/7 monitoring, which is costly when it requires a highly trained employee.

While AI likely won’t replace staff members anytime soon, it could be an incredibly helpful addition. For example, a monitoring AI tool could mitigate human error, flag urgent problems and support round-the-clock insights. The survey also found that over half of respondents would trust operational decisions to an adequately trained machine-learning model.

Challenges for AI and Data Centers

Of course, AI isn’t just about adding new technologies where available. It will also lead to challenges for data center operators, particularly in the evolving regulatory and economic landscape.

Regulatory Concerns

Regulations are still developing around AI, and data centers must be prepared. These regulations may vary widely by industry and region, and compliance will be paramount to successfully supporting AI applications. Data center operators must maintain flexibility to adapt to updates.

Regulations might affect areas like testing, infrastructure and definitions of risk. Data centers may also face complications in data storage and intellectual property concerns.

Implementation Delays

AI’s growth is so fast that many data centers can’t keep up with demand. Building facilities, solving space and power constraints and obtaining hard-to-find resources all contribute to delays that slow down the implementation of AI in data centers — and could affect the development of AI technologies.

While data center operators may have more control over the problem-solving processes, supply chain challenges introduce issues that are harder to address. For example, many data center operators meet AI demands with high-performance computing (HPC) clusters, which use graphics processing units (GPUs). GPU shortages have increased the difficulty of installing efficient HPCs. Long lead times for other technologies can also contribute.

Another concern comes from the power demands of AI, which can be multitudes higher than traditional workloads. Improved efficiency and alternative power generation options can be valuable in limiting constraints from and dependency on power grids.

Accommodating these delays may call for creative, unique solutions to address the goals and needs of each facility.

Prepare for the Future of AI and Data Centers

Although the industry faces many unknowns, AI will certainly change the landscape of data center operations. Adapting to these demands will be challenging but highly rewarding and necessary for continued success.

At DataSpan, we stay on the pulse of artificial intelligence and data centers. We’ve been around for 50 years and are familiar with adapting to new technologies. Our end-to-end data center services include comprehensive support with expert consulting, industry-leading equipment and a wide range of power and cooling solutions. Reach out to an expert today to learn more about how we can help you prepare for the complex impact of AI on data centers.