SSD Virtualization – The Past, Present and Future
Abstract
In the age of AI and the ever-important focus on data center efficiency, sharing SSDs across multiple independent tenants is fueling a demand for a wide range of PCI Express® and NVM Express® capabilities. This includes established capabilities such as data placement techniques for mitigating noisy neighbors, and multi-function PCI Express® devices (e.g., SR-IOV) for logical device partitioning. New capabilities are already enabling more efficient data and controller state migration (i.e., live migration), while emerging capabilities such as quality of service for deterministic resource allocation and full NVM Subsystem virtualization promises to bring yet another leap in this space.
Related Sessions
SSD Virtualization: Enabling New Possibilities for SSD-Based Ecosystems
A new set of NVM Express® (NVMe®) and Open Compute Project® (OCP) features is revolutionizing the virtualization landscape, enabling a new SSD-supported virtualization ecosystem. This presentation will explore these innovative features and their potential applications in host systems. We will describe an example virtual machine (VM) setup and discuss how the features can be utilized together to create a robust, secure, and performant virtualized environment. Specifically, we will cover the use of SR-IOV to expose individual functions with child controllers, Exported NVM Subsystems for building virtualized subsystems, and OCP’s security extensions to Caliptra for maintaining security.
We will also delve into advanced topics, including Quality of Service (QoS) parameter setting across varied VMs with differing Service Level Agreement (SLA) processes, and live migration of VMs from one SSD to another. The presentation will highlight how Tracking Allocation Status and Granularity facilitates live migration. Finally, we will touch on extended SSD-supported virtualization examples, including single-port vs dual-port variations, AI enablement through direct GPU access, storage use cases, application container use cases, and extensible integration to Flexible Data Placement. These examples may utilize subsets of the features in novel configurations or explore entirely new applications.
SMR and HAMR Advancing HDD Areal Density
This talk reflects on 18 years of SMR evolution —covering physical layouts, filesystems, garbage collection algorithms, device drivers, and simulators. Furthermore, the talk will also discuss how SMR disks integrated with data storage solutions like RAID and deduplication, including real-world use cases of SMR disks by hyperscalers.
We will also discuss how SMR and HAMR technology interact in the context of AI workloads to provide intriguing new possibilities for HDDs.
Fibre Channel Update - What you need to know about the Dawn of 256GFC (Gen 9)
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Global Distributed Client-side Caching for HPC/AI Storage Systems
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. Servers, in this context, refer to the data servers that provide file system and object store functionalities, while clients denote the storage clients running on compute nodes in HPC/AI clusters that access and retrieve data from these servers.
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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. Industry’s initial response involved deploying separate document and embedding stores alongside conventional storage. However, the disaggregation of data and its semantic representations across multip
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