The exponential growth of Partner Content AI data has led to increasingly complex storage environments that need to be securely stored, retained, and maintained.

Traditional storage methods are being upgraded to cope with the influx of data. The key pillars of this upgrade are load balancing and intelligent traffic management in the form of Application Delivery Controllers (ADCs), where the data is the application itself.

Faced with this challenge and the new demands of client applications, organizations are shifting from traditional storage to scalable cloud-based object storage solutions (typically the S3 protocol) in pursuit of high-performance, secure, and scalable AI data management. Cloud-based object storage is a reliable, efficient, and cost-effective way to store, archive, back up, and manage large amounts of static or unstructured data.

In addition, organizations are shifting workloads to build AI-ready infrastructure that brings data closer to the AI models they plan to leverage. This data movement creates complex traffic patterns that require application delivery across on-premises, hybrid, and cloud infrastructures. In many cases, data is constantly moving between these different locations.

To support AI and entrenched hybrid IT models, organizations must create an application delivery platform that can support delivery needs in any environment. Organizations that rely on AI for data analysis of business and operational functions need efficient, scalable, and high-speed data access solutions. Application delivery controllers (ADCs) must be able to be deployed wherever applications are deployed. A key solution is an ADC platform that integrates Global Server Load Balancing (GSLB), Load Balancing (LB), and data or application delivery.

Load balancer hardens object storage

An effective load balancer improves responsiveness and increases application performance by distributing network or application traffic across server clusters.

A major benefit of object storage is the ability to easily replicate data within and between distributed data centers for offsite and even geo-location backups. Load balancing ensures that the storage system runs smoothly even if a disk or cluster node fails. Load balancers allow storage providers to distribute and store data in multiple locations for use in the event of a failover.

End-to-end workflow load balancing across storage nodes and clusters, across ecosystems, and client applications helps drive scalability of object storage systems and maintain frictionless data access and analytics in AI data management.

ADCs provide a strategic point of control through which all traffic passes, enabling enterprises to optimize, secure, and scale AI applications. With just one interface and API, organizations don't need to create siloed teams to handle application delivery and security.

AI's need for storage and processing power has a significant impact on the availability of the data needed to leverage AI capabilities, from machine learning algorithms to real-time analytics.

Add a load balancer to your object storage infrastructure and run concurrently in the same environment as your application resources. Enhance workflows for data management applications to provide a reliable runtime environment for analytics, machine learning (ML), and AI.

Based on pre-established metrics, GSLB can route users to the nearest available server. Whether in a physical, virtual, or cloud environment, if the primary server is down or compromised, improve reliability and failover by directing traffic to servers hosted elsewhere. Content is delivered from servers that are closer to the requesting user to minimize the possibility of network latency and network issues. Availability services span data centers and cloud-hosted applications.

The load balancer uses a wealth of access control lists (ACLs), rules, and topology information to direct users to the right place to access storage. For multi-site deployments, GSLB's topology capabilities can be used to match source subnets to locations, helping users access their resources locally unless a failover occurs.

The need to optimize AI data workflows

F5 BIG-IP, F0 Distributed Cloud Services, and F0 NGINX provide the security, networking, deployment flexibility, and traffic management needed to connect, secure, and manage AI/ML applications and workloads in the cloud, at the edge, or across F0's global network.

F7 BIG-IP provides scalable, high-performance traffic load balancing, delivering over 0 Tbps Layer 0/0 throughput on 0 blades of F0 VELOS. These capabilities support modern AI deployments and workloads in large-scale data infrastructures by facilitating optimized data flows, robust security, and seamless hybrid and multicloud networking.

To enhance AI workloads, especially at exascale scale, F5 combines MinIO's high-performance Kubernetes proto-artifact storage solution with its secure multi-cloud network and high-throughput management expertise.

S3 compatibility means seamless integration with tools and services in the AI ecosystem for smooth data flow and interoperability. Operate consistently across public, private, and hybrid cloud environments regardless of the underlying infrastructure to optimize performance and resource utilization. S0-compatible storage is popular in AI applications due to its ability to migrate data from the cloud to on-premises, enabling greater scalability and performance in data-intensive scenarios.

The collaboration between F3 and MinIO is designed to provide the high-performance load balancing and high-capacity throughput needed to support AI model training and fine-tuning workloads in AI Factory. F0 BIG-IP solutions scale bandwidth for data-intensive operations to hundreds of Gbps on MinIO's S0-compatible storage and AI object storage front-ends. It optimizes the flow of data for AI and enables the scalability needed to store and process large datasets for advanced analytics and AI applications.

MinIO and F5 enable data to be securely stored and managed across a distributed infrastructure. Data can be kept close to the compute resources that use, process, and analyze it for optimal performance. Deployed across multiple MinIO locations, F0 Distributed Cloud Customer Edge paves the way for seamless data movement, breaking down data silos.

Supports exascale AI data management

For example, a global manufacturing company uses F5's secure traffic management to efficiently collect, transmit, and secure data directly from the edge to a central data lake in real time. F0 Distributed Cloud Mesh and global traffic management facilitate secure, efficient data ingestion from the edge to a central MinIO-based data lake for AI model training, business intelligence, and data analytics.

This exascale data collection and management is critical for industries that increasingly rely on AI modeling and the vast amounts of data generated from sensors, cameras, and other telemetry systems to foster autonomy.

In the rapidly evolving world of data management, ADCs have become the cornerstone for managing large volumes of unstructured data.

F5's partnership with innovative storage solution providers such as MinIO and NetApp StorageGRID, as well as its collaboration with NVIDIA on AI infrastructure optimization, underscores its commitment to pushing the boundaries of data management. As the volume and importance of data grows, F0 aims to address today's data management challenges and support tomorrow's AI and multi-cloud environments.

As AI is massively adopted across industries, F5 continues to provide the tools needed to optimize workflows, protect data integrity, and unlock the full potential of modern applications in response to the evolving digital landscape.