AI applications rely heavily on storing vast quantities of data for training and inference purposes, which underscores the importance of storage networking for these applications.
Storage networking facilitates efficient management practices for organizing, storing and accessing data sets securely, while ensuring consistency and integrity throughout the process. Low-level, latency-based data access works best for highly intensive training tasks that require handling huge amounts of stored information with high efficiency rates that translate into seamless scalability.
The importance of using scale-up storage systems for AI data sets highlights the need for distributed file systems or object-based storage systems. They enable meeting workloads' performance demands using multinode platforms without compromising overall efficiency rates during data consolidation phases.
The key to harnessing maximum potential from an organization's AI applications lies in optimizing its workflow processes. Invest appropriately into storage networking infrastructure to help achieve that goal as the organization improves overall performance levels and facilitates seamless model development and deployment. Hence, this becomes a critical factor that holds significant importance toward unlocking relevant AI opportunities.
AI storage networking requirements
AI workloads come with unique storage networking requirements that differ from other applications because of their specific characteristics.
High-bandwidth and low-latency connections are essential to enable quick data transfer between storage systems and computational resources during AI workload processing. This helps to reduce data access bottlenecks and enables greater efficiency during training and inference.
AI applications deal with massive data sets that grow over time, which require scalable storage, such as distributed file systems or object storage. Typically, horizontally scalable infrastructures are necessary to provide both capacity and performance scalability.
Parallel data access is a requirement for AI workloads since they often benefit from parallel processing techniques and distributed computing. Storage networking should seamlessly handle data access by multiple storage devices or nodes simultaneously to improve throughput and efficiency during large-scale operations, like model training.
Efficient mechanisms are essential for data migration, replication or synchronization between different storage platforms or locations to ensure seamless transfer of data among on-premises storage systems, cloud-based environments or hybrid setups. Data protection mechanisms, such as snapshots and backup, protect valuable data sets used for model training in the event of a service interruption or catastrophic loss.
Support is also required for different kinds of data types -- structured, as well as unstructured, multimedia and sensor -- involved in AI applications. It's essential that the storage networking system supports a broad range of different file formats and meets requirements according to each specific type of stored content. This comprehensive approach accommodates large file sizes and supplies efficient ways to store multimedia content specifically tailored toward industry standards, such as image or video codecs.
Collaborative efforts incorporating machine learning algorithms require seamlessly accessing shared information. As a result, storage networking infrastructure must support features required when team members concurrently use shared data sets.
Many AI applications operate across a combination of on-premises infrastructure, cloud resources and hybrid or multi-cloud architectures. Storage networking is important for AI applications to enable seamless integration between these various environments. It should also enable reliable data synchronization that incorporates well-developed governance measures in a compatible manner across all these deployments.
Compare storage arrays and disaggregated storage architectures
For AI applications that require efficient storage architecture, it is essential to understand the key differences between storage arrays and disaggregated storage. These different approaches offer specific advantages and characteristics.
Storage arrays are centralized storage systems composed of multiple drives or disk enclosures connected to a storage controller. The controller efficiently manages the storage resources and provides access to multiple servers or compute nodes. It is designed for optimum performance offering fast data access, low latency and high IOPS. Storage arrays simplify management by providing centralized control over configuration, monitoring and administration of the storage infrastructure.
Storage arrays offer a comprehensive range of data services that enable efficient protection and management of data within the system. These services include RAID configurations, snapshots, replication, backup capabilities, deduplication and compression. Storage arrays can scale up by adding more drives, expansion shelves or modules to increase both capacity and performance. However, it is important to consider the limitations imposed by the specific model's capacity and performance capabilities.
Arrays can create resource silos, where storage is exclusively assigned to servers or compute nodes. This can restrict the flexibility and sharing of storage resources among different AI applications or compute nodes, potentially causing the underuse of storage capacity. Additionally, in storage array systems, data access is mostly reliant on the capacity and performance of the storage controller or specific drives employed. Consequently, this may lead to performance bottlenecks particularly in AI applications that necessitate parallel processing and high-speed data access.
Disaggregated storage separates the storage resources from compute resources through the creation of a separate storage layer that multiple servers or compute nodes can share. By accessing storage resources over a network, it effectively decouples storage from individual servers.
The main advantages of disaggregated storage are its greater scalability and flexibility. With this approach, storage capacity and performance can scale independently of compute resources. This enables efficient resource allocation and dynamic adjustment based on AI workload requirements. It also optimizes resource utilization by ensuring that organizations use storage in the most effective way possible.
Disaggregated storage enables efficient sharing of resources among multiple compute nodes or AI applications, which eliminates resource silos and promotes better collaboration among different teams or projects. Disaggregated storage also offers improved performance and bandwidth capabilities. By using high-speed networking technologies, like remote direct memory access, it provides fast data access and parallel data processing capabilities. This is particularly crucial for AI workloads that require intensive data processing.
The disaggregated approach enables flexibility in choosing hardware components for storage. It gives the freedom to select storage drives or devices, networking infrastructure and storage controllers that best suit the needs of the organization in terms of performance, capacity or cost considerations.
Moreover, disaggregated storage facilitates dynamic resource allocation based on workload demands. Storage capacity and performance can be adjusted on the fly to match the changing needs of AI applications, improving overall resource efficiency and agility. Lastly, disaggregated storage optimizes hardware utilization by enabling shared access to storage resources across multiple compute nodes. This mitigates overprovisioning within individual servers and maximizes the use of available storage capacity and performance.
Put it all together
AI applications require high-performance storage networking to handle large data sets and intensive data processing. Look for storage networking platforms that offer high-bandwidth connections, low latency and parallel data access to ensure fast and efficient data retrieval.
As AI data sets and workloads can grow rapidly, it becomes necessary to have scalable storage networking architectures. Consider storage that supports scalability, such as distributed file systems or object storage, which can accommodate the increasing data volume and performance demands of AI applications.
Ensuring data accessibility for AI apps is vital when it comes to storage networking. Look for features like high-speed data retrieval, parallel data access, caching mechanisms and support for distributed storage systems. These capabilities enhance data accessibility and enable faster AI processing.
AI applications have unique data management requirements. It is advisable to consider storage networking that provides efficient data movement, data virtualization capabilities, support for data replication and backup, and integration with collaboration tools. This helps streamline data management processes and facilitate collaboration among AI teams.
Many AI applications adopt hybrid or multi-cloud architectures. Therefore, choose storage networking that seamlessly integrates with both on-premises infrastructure and cloud storage services. This integration enables efficient data movement, synchronization and collaboration.
Since AI data sets often contain sensitive or valuable information, select storage networking that incorporates strong security measures. Look for features, such as encryption and access controls, and mechanisms, like replication, snapshots and backup, which ensures confidentiality, integrity and availability of the stored information.
About the author
Saqib Jang is founder and principal of Margalla Communications, a market analysis and consulting firm with expertise in cloud infrastructure and services. He is a marketing and business development executive with over 20 years' experience in setting product and marketing strategy and delivering infrastructure services for cloud and enterprise markets.