As large language models (LLMs) like ChatGPT and machine learning (ML) generate seemingly endless media and industry buzz around the promise of generative AI, the growth of these technologies brings with them an enormous number of challenges for the data center sector.

Along with AI, technologies like ML and the Internet of Things (IoT) will require data centers that have extensive bandwidth and innovative power solutions. As the need for computing capacity and increased connectivity grows, so does system operators’ dependence on data centers to efficiently process and store enormous quantities of information while sustaining exceptional performance.

Edge computing systems that utilize advanced AI algorithms can execute real-time data processing and decision-making within a network to maximize overall performance. Edge computing can also help enable real-time data processing that’s required to power AI. With generative AI, LLMs, IoT, cloud adoption, real-time data processing, and the demand for low-latency apps driving computing capacity to unprecedented levels, the Edge computing marketplace is projected to surge from roughly $34 billion in 2024 to more than $700 billion in 2033.

Given the meteoric rise of generative AI, the data center industry has been tasked with adapting quickly to accommodate the surge in computing capacity and address the increased demand for effective power solutions to facilitate it all.

The key to unlocking AI’s potential will likely hinge on the efficacy of Edge computing. Using a distributed information technology (IT) architecture, Edge computing processes data at the network's periphery, as close as possible to its original source. This can reduce latency and improve performance while also reducing energy consumption – a longtime adversary of large-scale data center operators.

Addressing the challenges of implementing Edge models

To successfully design and implement Edge computing models, the data center sector will need to address some challenges. As organizations begin to rely less on traditional centralized data centers to collect, process, and store data, Edge computing has the ability to reshape the way business gets done. For example, with energy grids becoming more widely distributed, Edge computing can provide the low-latency data processing needed to facilitate real-time power generation and distribution decisions to meet demands optimally.

A less centralized data center ecosystem also provides benefits at the consumer level. However, consumer reliance on apps that process high volumes of fast-moving data is also driving the need for data center developers to address logistical challenges. These include establishing data centers near neighborhoods, facilities, and densely populated areas that are closer to data sources. This is particularly important for apps that require very low latencies, such as live streaming, augmented reality (AR), and autonomous vehicles.

With these applications creating more demand on Edge computing infrastructure, another challenge comes into play – mitigating the heat generated by the IT equipment and power supplies within these facilities. To contend with the elevated levels of heat being generated, developers are tasked with implementing efficient power systems that include features like high-efficiency conduction or liquid-cooled rectifiers, immersion cooling, and heat containment solutions. Mitigating heat is critical to the data center’s operational efficiency and to preventing equipment damage or, even worse, system failures.

The consequences of damaged equipment and outages can be significant. According to a survey by the Uptime Institute, more than half of surveyed data center operators suffered an outage between 2020 and 2023. And the cost of the most severe outages remains high, as 54 percent of those surveyed said their most serious outage cost them more than $100,000. Furthermore, nearly one in six operators said they experienced an outage that cost them more than $1,000,000.

These sobering statistics underscore the criticality of resilient data center power systems and the need for potential issues to be monitored for and addressed remotely and at a moment’s notice. As Edge computing drives the need for more widely dispersed data centers, a premium has been placed on remote monitoring and diagnostics. In addition, the need for experienced, readily available service technicians to help maintain uptime in these critical Edge networks has never been higher. This requires data center operators to establish a clear, strategic approach to managing the complex process of scaling resources as needed.

Lastly, data security is another critical aspect of Edge computing processes, as multiple Edge locations can create heightened susceptibility to cyberattacks. Operators need to ensure that their Edge locations align with industry-standard data privacy and compliance measures. Security protocols, including access control, threat detection, and data encryption must also be implemented. It’s also crucial to continuously monitor and audit Edge devices and networks to ensure timely detection of threats or attacks.

Taking a holistic approach to nationwide Edge connectivity

The stakeholders in the Edge computing ecosystem, which include governments, service providers, and application developers, need to work together to fully realize the benefits of Edge computing. Supporting the extensive computing capacity that AI demands and addressing the challenges the technology presents will require a holistic and collaborative approach to network architecture, infrastructure design, and systems management.