Storage Developer Conference

Many server workloads, for example OLTP database workloads, require high I/O throughput and low latency. With the industry trend of moving high-end scale-up workloads to virtualization environment, it is essential for cloud providers and on-premises servers to achieve near native performance by reducing I/O virtualization overhead which mainly comes from two sources: DMA operations and interrupt delivery mechanism for I/O completions. The direct PCIe NVMe device assign techniques allow a VM to interact with HW devices directly and avoid using traditional Hyper-V para-virtualized I/O path.

To improve interrupt handling in the virtualization environment, Intel introduces Posted Interrupts (PI) as an enhanced method to mitigate interrupt delivery overhead in a virtualized environment, bypassing hypervisor involvement completely. In this talk, we will present Microsoft implementation and optimization of Intel PI and Hyper-V direct PCIe NVMe access on Windows platform. The results showed that we were able to achieve more than 10-Million IOPS from a single VM for the first time in the industry using an Intel Skylake based HPE commodity server with these techniques.

Have you heard of non-volatile/persistent memory but don’t know how to get started with this disruptive technology? Memory is the new Storage. Next generation storage tiered architectures are evolving with persistent memory and hardware delivering NVDIMMs. Are you a Linux or Windows application developer familiar with C, C++, Java, or Python, keen to develop the next revolutionary application or modify an existing application, but not sure where to start? Do you know what performance and analysis tools can be used to identify optimizations in your app to take advantage of persistent memory? Are you a software, server, or cloud architect that wants to get a jump start on this disruptive technology? This presentation will get you started on the persistent memory solution path. The future is in your hands. The future is now!

Learning Objectives: 1) We’ll deliver an introductory understanding of persistent memory, introduce the SNIA Programming Model, Direct Access (DAX) filesystems, and show where persistent memory fits in the storage hierarchy; 2) We’ll provide several options for creating development environments (you don’t need physical modules to get started!); 3) We’ll introduce application programming using the Persistent Memory Developers Kit (PMDK); 4) We’ll introduce and describe how to create and manage Persistent Memory Regions, Namespaces, and Labels; 5) Describe existing analysis tools to identify applications that are good candidates for persistent memory.

In-situ processing, in-storage processing, smart SSD, computational storage… Many names to define the same concept: a closer integration of computing capabilities and data storage in order to reduce data movement leading to better performance and lower power consumption. This is a new trend in storage and computing architectures. How can we define this new type of products: storage with embedded processing, or processing with embedded storage?

This talk presents an analysis of the in-storage processing trend, including a comparison of different architectures, the presentation of a computing and storage technologies roadmap, and a list of applications use cases.

Learning Objectives: 1) In-storage processing architectures; 2) Technology roadmap for integration; 3) Application use cases.

The SNIA NVMP Technical Workgroup (TWG) continues to make significant progress on defining the architecture for interfacing applications to PM. In this talk, we will focus on the important Remote Persistent Memory scenario, and how the NVMP TWG’s programming model applies. Application use of these interfaces, along with fabric support such as RDMA and platform extensions, are part of this, and the talk will describe how the larger ecosystem fits together to support PM as low-latency remote storage.

Learning Objectives: 1) Persistent Memory programming; 2) RDMA extensions; 3) SNIA PM initiatives.

Ceph protects data by making 2-3 copies of the same data but that means 2-3x more storage servers and related costs. It also means higher write latencies as data hops between OSD nodes. Customers are now starting to deploy Ceph using SSDs for high-performance workloads and for data lakes supporting real-time analytics. We describe a novel approach that eliminates the added server cost by creating Containerized, stateless OSDs and leveraging NVMe-over-fabrics to replicate data in server-less storage nodes. We propose redefining the boundaries of separation within SDS architectures to address disaggregation overheads. Specifically, we decouple control and data plane operations and transfer block ownership to execute on remote storage targets. It also dramatically reduces write latency to enable Ceph to be used for databases and to speed up large file writes. As part of the solution, we also describe how OSD node failover is preserved via a novel mechanism using standby stateless OSD nodes.

Learning Objectives: 1) Storage disaggregation; 2) NVMe over fabrics; 3) Ceph architecture.

NVMe and NVMe over Fabrics (NVMe-oF) protocols provide a highly efficient access to flash storage inside a server and over the network respectively. Current generation of distributed storage software stacks use proprietary protocols which are sub-optimal to deliver end to end low latency. Moreover it increases operational complexity to manage NVMe-oF managed flash storage and distributed flash storage in private cloud infrastructure. In this session, we present NVMe over Fabrics based high performance distributed block storage that combines the best of both worlds to deliver performance, elasticity and rich data services.

Learning Objectives: 1) NVMe, NVMe-oF for flash data path IO architecture; 2) Programming, architecture and optimization for flash; 3) Distributed storage, data services.

Intel Fellow Amber Huffman has been at the center of Intel’s development for SSDs, with emphasis on SSD storage interfaces and next generation form factors. In this talk, she will discuss the rationale behind decisions that were made in advancing storage architecture leading to the emergence of solid state only Data Centers. Amber will also discuss the key factors influencing the future of the Data Center and the important role storage continues to play.

Join Dr. Andy Walker for a wonderfully illustrated tour through 90 years of physics and materials science, leading to modern solid state memory technologies via “The Golden Thread of Tunneling.”

NVMe KV is a proposal for a new command structure to access data on an NVMe controller that is being developed within the NVMe technical working group. This proposed command set provides a key and a value to store data on the Non Volatile media and provides a key to retrieve data stored on the media. In addition to the work on the NVMe specification, the SNIA is also working on a KeyValue API. This presentation will describe the standardization efforts going on in both the NVMe working group and SNIA.

Learning Objectives: 1) What is Key Value Storage; 2) Where are we at in the NVMe standardization process of NVMe KV; 3) Where are we at in the SNIA standardization process of a KV API?

The DMTF’s Redfish is an open industry standard specification and schema that specifies a RESTful interface and utilizes JSON and OData to help customers integrate solutions within their existing tool chains.

Designed to meet the expectations of end users for simple, modern and secure management of scalable platform hardware, the Redfish method and approach has seen accelerated adoption across the IT infrastructure management standards community.

This session provides an overview of the Redfish standard as well as information about what’s new in the Redfish ecosystem, including PLDM for RDE (Redfish Device Enablement) and other DMTF updates as well as consideration and adoption in the broader standards community. This session will then cover the general data model including the base storage models and infrastructure that are used by the SNIA Swordfish extensions.

Learning Objectives: 1) Basic understanding of the Redfish data model hierarchy; 2) Knowledge of the problem areas to which the Redfish methodology is being applied; 3) Understand how RDE brings Redfish “inside the box” for network & other devices.