KOHb - Getty Images
How an edge networking strategy optimizes AI workloads
Edge networking prioritizes managing data at the network edge. This network architecture reduces latency, enhances operational efficiency and enables real-time AI processing.
The network edge refers to the boundary between on-premises data centers and off-site resources. Organizations typically deploy edge networks to manage data, automate operations and monitor remote device performance. This infrastructure has become a critical link for delivering the real-time data processing and analysis that AI initiatives require.
These capabilities are critical for ensuring high-performing AI applications, from smart cities and industrial IoT to autonomous vehicles and remote healthcare. Edge networks build upon that foundational concept by prioritizing edge networking. This enables administrators to optimize the network experience and improve all aspects of AI, such as model training, bandwidth consumption and application performance.
This article explores the advantages of edge networks for improving bandwidth, enabling real-time AI communications and reducing latencies. Consider how an emphasis of an edge networking strategy can benefit organizations as they implement AI within their networks.
What is edge networking?
An edge network is a network architecture that manages data and processing closer to the network edge. Edge networking shouldn't be confused with edge computing, a process that pushes data closer to its original source location.
Edge networking pushes data to the network edge, the periphery where firewalls filter and secure data moving in and out of the network. By pushing remote switches and routers closer to the edge, networks can efficiently respond to service requests in real time. Networks that prioritize an edge networking strategy are likely better suited for AI deployments.
Edge networks use different hardware components compared to data center networks that include traditional hardware, cabling and switches. Edge networks, on the other hand, consist of the following:
- High-speed branch routers.
- Integrated access devices that relay data sets.
- Switches that can provide Power over Ethernet to drive devices and WAN connectivity for AI implementations.
Standard on-premises and edge networks used for AI deployments typically connect to a central cloud or data center for training large language models or for deploying them into production. This approach consists of a multi-cloud or hybrid deployment strategy where on-site data and information stored in the cloud are connected to the AI initiative.
AI use cases for edge networking
An edge network can effectively support AI initiatives in several ways. When a network prioritizes edge networking, organizations can benefit from operational efficiency, network infrastructure capabilities and other business benefits.
Operational efficiency
Edge network devices can distinguish between continuous operational data and KPIs to reveal network anomalies and alert IT teams. Automation and self-healing mechanisms are intrinsic to AI and further eliminate the need for IT intervention. Administrators can also use the increased visibility to monitor devices, processes and events that occur continuously across their edge networks.
Network infrastructure capabilities
Edge switches are important network infrastructure elements that support modern AI applications.
They are integral to traffic control, effectively functioning as gateways between AI devices and the network. Some switches provide plug-and-play capabilities, while others offer advanced management and configuration features along with high-density ports designed to handle many AI deployments.
Modular data centers represent another important edge networking component. They're easily deployed, cost-effective and scalable, offering ideal edge support for AI initiatives. The smaller footprint enables processing near an AI source, while eliminating the time required to transmit data to central cloud servers.
Portability also reduces bandwidth requirements necessary to relay data, minimizes latency and enables organizations to locally control sensitive data and maintain security. Moreover, enhanced communications ensure faster response times and optimize the network architecture to handle an exponential increase in the number of users, devices and applications.
Business benefits
Finally, cost savings are another benefit of edge networks. Organizations can reduce expenses through pay-as-you-go modular data centers, as well as lower bandwidth consumption and the elimination of cloud costs. As AI use cases evolve, edge networks offer businesses key advantages for optimizing network management to reduce latencies, ensure device resilience and improve end-user experience.
Benefits of edge networking for AI
Edge networking offers network administrators several benefits and improvements over traditional network management. An emphasis on edge networking consistently delivers these advantages to support network efficiency and reliability.
Benefits of edge networking include the following:
- Data management. Edge network devices manage data more efficiently.
- Data identification. Edge network devices distinguish between continuous operational data and KPIs.
- Automatic filtering. Edge compute devices automatically filter the feedback and identify anomalies that require attention.
- Autonomous operations. Edge devices automatically perform actions without intervention, which significantly reduces the burden on network teams.
In addition to these improvements, edge networking also provides increased visibility to network administrators. This helps teams more easily identify and monitor devices, processes and events that occur across networks.
The design of an edge network enables administrators to have greater control over data and infrastructure. It emphasizes a distributed computing model where processing power, algorithms, storage and networking reside closer to AI devices or applications.
This results in faster data processing, improved security and reduced latency. Minimal latency is critical in AI-powered industries that require nanosecond response times, such as autonomous vehicles, industrial robotics and smart cities. Edge compute and storage resources also lower operational costs by minimizing bandwidth usage and reducing the amount of data that travels across networks.
Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.
