Why Scale-Out Data Center Architecture Falls Short in the Age of AI
3 Min Read
The speed and efficiency of data movement, both within and between data centers, have become critical factors for scalability.Alamy
For decades, data center architects relied on a straightforward playbook to address growing workload demands: they scaled out by adding servers. This approach provided a reliable way to increase compute, memory, and storage capacity to meet evolving needs.
However, the rise of AI has fundamentally changed the equation. The era of the scale-out data center might be coming to an end – or, at the very least, scaling out no longer suffices to keep pace with the unique demands of AI-driven workloads. Here’s the reason:
The Problems With Scale-Out Data Center Architecture in the Age of AI
Scale-out data center architecture accommodates growing workload requirements by expanding the amount of IT equipment housed within a facility. The approach has dominated data center design for decades, even if operators don’t explicitly label their facilities as “scale-out.”
In practice, architects have achieved scaling out through strategies such as:
- Upgrading Hardware: Replacing older servers with new models that offer greater compute, memory, and storage capacity.
As long as power and cooling capacity were sufficient, businesses could scale their infrastructure out as needed.
For software developers and IT teams, scaling out was a given. They operated under the assumption that data centers could provide enough compute and memory resources to support their applications.
Modern AI workloads pose significant challenges to the traditional scale-out model.
AI applications often require access to vast amounts of data at extremely high speeds, creating the most pressing issue. Simply adding more servers or infrastructure doesn’t always address this need. Network bottlenecks within the data center or slow I/O rates on individual devices may prevent data from moving quickly enough.
In other words, the primary limitation for data center scalability is no longer just total compute, memory, and storage capacity. It’s also the speed and efficiency with which workloads can access and use these resources.
New Approaches to Data Center Scalability
While scale-out data center architectures remain relevant, the scale-out model cannot ensure that data centers accommodate newer types of workloads, especially those driven by AI. Scaling by adding infrastructure will continue to play a role, as more demanding workloads require increased compute and memory resources. However, data center architects must go beyond conventional scale-out strategies to address the unique challenges posed by AI.
To complement traditional scale-out approaches, architects should adopt practices such as:
Smart Rack Design: Enhancing rack configurations to optimize data movement between individual servers within a rack, reducing latency and improving performance.
Network Acceleration Devices: Deploying technologies such as data processing units to accelerate data movement within facilities and alleviate network congestion.
High-Speed Interconnects: Implementing advanced interconnects to facilitate faster data transfer between multiple data centers, particularly for workloads that span geographically distributed facilities.
The Growing Importance of Networking
These strategies collectively highlight the increasing importance of networking within and between data centers. Historically, data center architects could assume that networking devices would reliably deliver packets to their intended destinations. However, AI workloads, which require the near-instantaneous movement of terabytes of data, have made this assumption obsolete. Networking must now be a central focus of scalability efforts.
The future of data center scalability involves more than increasing server counts or capacity. To keep pace with AI-driven workloads, data centers must also scale at the network level. This requires smarter network design strategies and the deployment of more advanced network hardware. Only by combining traditional scale-out methods with modern networking innovations can data centers truly meet the demands of AI and other emerging technologies.
About the Author
