CommScope's Mylaraiah JN on How AI Impacts Data Centers

In an exclusive interview with Dataquest, Mylaraiah JN, Director of Sales, Enterprise Business, India & SAARC at CommScope, sheds light on the transformative impact of AI technologies on data center operations. 

He elaborates on how AI influences data center construction, deployment, and network architecture design. Mylaraiah also shares innovative approaches by CommScope to enhance data center efficiency, addressing the challenges posed by AI-driven workloads. 

DQ: How has the proliferation of AI technologies impacted data center operations? Specifically, how has it influenced data center construction, deployment, and network architecture design?

Mylaraiah JN: As we move through 2024, it has become increasingly clear that AI is not just about the hype, it is in fact a game changer dramatically affecting data center construction and deployments, and network architecture design in general.

AI workloads require significant computing power, necessitating hyperscalers to build out and develop. For AI deployments, speed and efficiency is crucial for the market, requiring competent data center planning, design, and construction. Powered by enormous data increases and intensified by the significantly higher computing of generative AI applications and workloads, organizations must broadly rethink how they plan, design, and construct new facilities (or refurbish existing locations) to meet today’s increased demand and anticipate how that will look in the years ahead. In 2023, a substantial concern for data center operations was supply chain delays, which led to a shortage of chips and other foundational products and raw materials. While those challenges have largely abated, they have now given way to labor shortages and power availability as the major hurdles in data center construction due to the immense size and power demands of AI facilities.

DQ: Share some innovative approaches and solutions developed by CommScope to enhance data center efficiency while managing the increasing demands of AI workloads.

Mylaraiah JN:  At CommScope, we tackle the challenge of data center efficiency in the face of demanding AI workloads with a two-pronged approach, focusing on infrastructure solutions and data management. We keep up with the surge in data transmission caused by AI through our high-density fiber optic assemblies providing the necessary bandwidth to handle this increased data flow efficiently. This also reduces bottlenecks and ensures the smooth operation of AI applications. Moreover, our solutions are easily scalable. This caters to the ever-growing demands of AI, allowing data centers to adapt their infrastructure as processing needs evolve.

CommScope also supports Leaf-Spine architectures that are known for their efficient data center network design, particularly for virtualized cloud applications often utilized with AI. These Leaf-Spine architectures can potentially reduce congestion and improve data center performance.

DQ: What challenges do AI-driven workloads pose to data center infrastructure design, and how does CommScope address these challenges?

Mylaraiah JN: AI-driven workloads introduce unique challenges to data center infrastructure design, the foremost being a demand for an increase in power consumption. The sheer processing power of AI workloads translates to significant demands in terms of power, which requires data center design to be efficient and avoid exceeding the power limitations. Now, as the pace of efficiency gains in electricity use slows and the AI revolution gathers steam, Goldman Sachs Research estimates that data center power demand will grow 160% by 2030.

Generally, across the globe, we continue to witness a tug-of-war between lawmakers and corporations over two key data center sticking points—sustainability (the land and energy needed to operate) and data sovereignty (where and how data is stored). 

CommScope helps data center operators optimize the process by leveraging energy-efficient data center solutions that utilize renewable energy sources and minimizing the environmental impact of high-power AI workloads. We have integrated AI into our systems that can help us identify and address power consumption hotspots within data centers, optimizing resource utilization and preventing wastage of power. 

DQ: Are there any specific best practices or strategies organizations should adopt to optimize power usage efficiency in their data centers?

Mylaraiah JN:  With heightened awareness in boardrooms of companies’ climate impacts, enterprises must recognize and implement highly efficient and sustainable practices across all three levels of the data center lifecycle—site location, construction, and operation—while avoiding material cost increases to day-to-day operations. 

First and foremost, efficient design is crucial. Focusing on optimizing power usage and power density to reduce the overall number of data centers needed will help lower construction and maintenance costs. "Plug-and-play" infrastructure products and architectures can streamline installation processes, reducing both time and reliance on highly skilled labor. 

Secondly, leveraging a mix of renewable energy sources such as wind, solar, geothermal, hydro/tidal, and safe nuclear power will significantly decrease dependency on local power grids. The integration of AI and machine learning technologies further enhances efficiency by identifying and addressing power-intensive compute hotspots. Transitioning from traditional cradle-to-grave product lifecycles to cradle-to-cradle approaches ensures that components like servers and switches are regularly upgraded and fed into a robust reuse/recycle market, minimizing waste. Maintaining an unwavering commitment to efficiency across all phases—location/discovery, construction/design, and operations/management—will ultimately support sustainable data center growth and expansion.