As the rapid expansion of AI and analytics continues, storage system architecture and total cost of ownership (TCO) are undergoing significant transformation. Emerging technologies such as HAMR in rotating storage and high-capacity, data center-grade QLC in flash promise to redefine the landscape for both hyperscale and OEM data storage solutions. But what will that evolution look like?

Command Duration Limits (CDL) is a QoS protocol for SCSI and ATA HDDs that provides the host with a model of traffic classes and command execution policies that enable a drive to optimize execution of consumed commands. The standard has a two dimensional model.

HPC and AI workloads require processing massive datasets and executing complex computations at exascale speeds to deliver time-critical insights. In distributed environments where storage systems coordinate and share results, communication overhead can become a critical bottleneck. This challenge underscores the need for storage solutions that deliver scalable, parallel access with microsecond latencies from compute clusters. Caching can help reduce communication costs when implemented on either servers or clients.

The rise of Generative and Agentic AI has driven a fundamental shift in storage —from storing data to functioning as comprehensive knowledge management systems. Traditional model of storing data and system metadata and providing analytical capabilities on top of it is now inadequate. Agentic AI workflows require access to semantically enriched representations of data, including embeddings and derived metadata (e.g., classification, categorization). As data is ingested, storage systems must support real-time or near-real-time generation and association of such metadata.

"How hard can it be to copy files and objects from one storage system to another? I could write a script myself or use one of the free tools out there. Easy, right?"