The Need For Speed: ADC For Efficient AI Data Management

Partner Content Exponential growth in AI data has led to increasingly complex storage environments where data must be stored, retained and maintained securely.

Traditional storage methods are being upgraded to manage the massive influx of data. A key pillar to this enhancement is load balancing and intelligent traffic management in the form of Application Delivery Controller, the data being the application.

Faced with this challenge and new demands of client-side applications, organizations are shifting from traditional storage to scalable cloud-based object storage solutions (typically S3 protocol) in their quest for high-performance, secure and scalable AI data management. Cloud-based object storage is geared to be a reliable, efficient and affordable way of storing, archiving, backing up and managing vast volumes of static or unstructured data.

Additionally, Organizations are shifting workloads to build an AI-ready infrastructure that puts data closer to the AI models they plan to leverage. This data movement creates complex traffic patterns and requirements for Application Delivery that span on-premise, hybrid and cloud infrastructures. In many cases, data is constantly moved between these disparate locations.

To support Al and the entrenched hybrid IT model, organizations must create an application delivery platform capable of supporting delivery needs in any environment. Organizations relying on AI for data analyses across business and operational functions demand efficient, scalable, high-speed data access solutions. The application delivery controller (ADC) must be deployable anywhere they deploy applications. One pivotal solution is an ADC platform incorporating global server load balancing (GSLB), load balancing (LB), and data or application delivery.

Load balancers enhance object storage

An effective improves responsiveness and increases the of applications by distributing network or application traffic across a cluster of servers.

One key benefit of object storage is the ease of replicating data within and among distributed data centres for backups off-site and even across geographies. Load balancing ensures the storage system runs smoothly even if a disk or cluster node fails. Load balancers allow storage vendors to distribute and store data in multiple locations to facilitate in case of failover.

Load balancing end-to-end workflows among storage nodes and clusters, different ecosystems, and client-side applications helps drive scalability in object storage systems and retain smooth data access and analyses in AI data management.

ADCs offer one strategic point of control through which traffic flows, enabling enterprises to optimize, secure and scale AI applications. With only one interface and API, organizations need not create siloed teams to handle application delivery and security.

AI's demand for storage and processing power has significant implications for data availability required to harness AI's capabilities, from machine learning algorithms to real-time analytics.

Adding load balancers into an object storage infrastructure and running it concurrently in the same environment as the application resources . Enhancing the workflows of data management applications delivers reliable runtime in analytics, machine learning (ML) and AI.

Based on pre-established metrics, GSLB can route users to the closest server available. Whether in a physical, virtual, or cloud environment, this improves reliability and failover by directing traffic to servers hosted in other locations if the primary server is down or compromised. Content is delivered from a server closer to the requesting user to minimize network latency and the likelihood of network issues. Availability services span data centres and cloud-hosted applications.

Load balancers use a myriad of Access Control Lists (ACLs), rules and topology information to direct users to the correct location to access storage. For multi-site deployments, the GSLB's topology feature can be used to match the source subnets to locations, helping users access their resources locally unless a failover occurs.

The need to optimize AI data workflows

F5 BIG-IP, F5 Distributed Cloud Services, and F5 NGINX provide the security, network connectivity, deployment flexibility, and traffic management needed to connect, secure, and manage AI/ML apps and workloads in the cloud at the edge or in the F5 Global Network.

F5 BIG-IP provides scalable, high-performance traffic load balancing with layer 4/7 throughput of over 3 Tbps on 32 blades of F5 VELOS. These capabilities support modern AI deployments and workloads in large-scale data infrastructures by facilitating optimized data flow, robust security and seamless hybrid and multi-cloud networking.

To enhance AI workloads, especially at exascale levels, F5 has melded MinIO's high-performance Kubernetes-native object storage solutions with its secure multi-cloud networking and high-throughput management expertise.

S3 compatibility translates to seamless integration with tools and services in the AI ecosystem, allowing for smooth data flow and interoperability. The ability to operate consistently across public, private, and hybrid cloud environments optimizes performance and resource utilization regardless of the underlying infrastructure. S3-compatible storage is popular for AI applications because it enables the repatriation of data from the cloud to on-prem for greater scalability and performance in scenarios where the amount of data faced is large.

The F5 and MinIO partnership aims to deliver the high-performance load balancing and high-volume throughput needed to support AI model training and fine-tune workloads in AI factories. The F5 BIG-IP solution in front of MinIO's S3-compatible storage and AI object store scales bandwidth to hundreds of gigabits per second for data-intensive operations. It optimizes data flow for AI and enables the scalability required for storing and processing large datasets used in 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 computing resources that use, process, and analyze the data for optimal performance. F5 Distributed Cloud Customer Edge deployed across multiple MinIO locations paves the way for seamless data mobility and breaks down data silos.

Supporting exascale AI data management

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

Such exascale data collection and management are critical for industries increasingly relying on AI modelling and vast amounts of data generated from sensors, cameras and other telemetry systems to foster autonomous capabilities.

In the rapidly evolving landscape of data management, ADCs have become a cornerstone for managing vast amounts of unstructured data.

F5's partnerships with innovative storage solutions like MinIO and NetApp StorageGRID and collaborations with NVIDIA for AI infrastructure optimization highlight its commitment to pushing the boundaries of data management. As data grows in volume and importance, F5 aims to address current data management challenges and support future AI and multi-cloud environments.

As industries embrace AI at scale, F5 continues to deliver the tools needed to optimize workflows, protect data integrity, and unlock the full potential of modern applications in an ever-evolving digital landscape.

Read the blog 'A New Generation of ADCs for the AI Era' by clicking here.

Contributed by F5.