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Added in additional use case of data ingestion load balancing for AI model training, inference, and fine-tuning services. Two sources cited. |
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===Failovers===
Load balancing is often used to implement [[failover]]—the continuation of service after the failure of one or more of its components. The components are monitored continually (e.g., web servers may be monitored by fetching known pages), and when one becomes unresponsive, the load balancer is informed and no longer sends traffic to it. When a component comes back online, the load balancer starts rerouting traffic to it. For this to work, there must be at least one component in excess of the service's capacity ([[N+1 redundancy]]). This can be much less expensive and more flexible than failover approaches where every single live component is paired with a single backup component that takes over in the event of a failure ([[dual modular redundancy]]). Some [[RAID]] systems can also utilize [[hot spare]] for a similar effect.<ref name="IBM">[https://www.ibm.com/support/knowledgecenter/en/SSVJJU_6.4.0/com.ibm.IBMDS.doc_6.4/ds_ag_srv_adm_dd_failover_load_balancing.html Failover and load balancing] ''IBM'' 6 January 2019</ref>
=== Data Ingestion for AI Model Training ===
Increasingly, load balancing techniques are being used to manage high-volume data ingestion pipelines that feed [[artificial intelligence]] [[AI training|training]] and [[inference]] systems—sometimes referred to as “[[AI Factory|AI factories]].” These AI-driven environments require continuous processing of vast amounts of structured and unstructured data, placing heavy demands on networking, storage, and computational resources.<ref>{{Cite web |title=Optimize Traffic Management for AI Factory Data Ingest |url=https://www.f5.com/company/blog/ai-factory-traffic-management-data-ingest |access-date=2025-01-30 |website=F5, Inc. |language=en-US}}</ref> To maintain the necessary high throughput and low latency, organizations commonly deploy load balancing tools capable of advanced TCP optimizations, connection pooling, and adaptive scheduling. Such features help distribute incoming data requests evenly across servers or nodes, prevent congestion, and ensure that compute resources remain efficiently utilized.<ref>{{Cite web |title=Optimize, Scale, and Secure AI Interactions |url=https://www.f5.com/solutions/use-cases/optimize-scale-and-secure-ai |access-date=2025-01-30 |website=F5, Inc. |language=en-US}}</ref>
When deployed in large-scale or high-performance AI environments, load balancers also mitigate bandwidth constraints and accommodate varying data governance requirements—particularly when sensitive training data cannot be sent to third-party cloud services. By routing data locally (on-premises) or across private clouds, load balancers allow AI workflows to avoid public-cloud bandwidth limits, reduce transit costs, and maintain compliance with regulatory standards. As AI models expand in size (often measured by billions or even trillions of parameters), load balancing for data ingestion has grown in importance for maintaining the reliability, scalability, and cost efficiency of AI factories.
==See also==
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