Storage Developer Conference

As storage devices get faster, data management tasks rob the host of CPU cycles and DDR bandwidth. In this session, we examine a new interface to storage devices that can leverage existing and new CPU and DRAM resources to take over data management tasks like availability, recovery, and migrations. This new interface provides a roadmap for device-to-device interactions and more powerful storage devices capable of providing in-store compute services that can dramatically improve performance. We call such storage devices “eusocial” because we are inspired by eusocial insects like ants, termites, and bees, which as individuals are primitive but collectively accomplish amazing things.

Today’s exponential growth of Big-Data makes lossless compression one of the most important operations in a data center as it expands storage capacity, reduces storage costs and speeds up data access. Off-loading compression from processors to FPGAs can free up valuable CPU time while reducing compression time and power consumption. Moreover, sharing accelerators across a data center can further improve resource utilization and lower operation costs.

Here, we present an efficient FPGA implementation of a GZIP/ZLIB accelerator which can compress the data at up to 1.7GB/s using about 30KLUTs on a Virtex-7 device. This accelerator is presented as an NVMe namespace via the Eideticom NoLoad platform which allows users to access it locally or across the network using standard NVMe and NVMe over Fabrics drivers available in major operating systems. We also present the software API to access this accelerator which relies on the high-performance inbox NVMe drivers and uses a user-space framework to provide both local and remote access to this accelerator.

Our human brain can be modeled as two distinctly different systems: a real time intuition system, and a background reasoning system. As we move into the AI compute era, Emerging Memory technologies play an increasingly important role in overcoming the limitations of DRAM and NAND. The commercialization of Emerging Memory will therefore accelerate our realization of powerful AI systems.

In this talk, the speaker will:

• Demonstrate the human brain has two distinct systems

• Discuss the best fit compute architecture to model each AI system

• Articulate how DRAM and NAND are applied to the compute architectures

• Identify the key limitations of DRAM and NAND

• Present and classify some Emerging Memory alternatives

• Discuss the Emerging Memory system enhancements

• Identify who is doing what (by when) in the Emerging Memory landscape

• Articulate the unique challenges in realizing an AI intuition system

• Propose how Emerging Memory may solve some intuition problems

• Point to the future of AI systems based on Emerging Memory

This presentation will consider the various architectures for All Flash Solutions. Considering the limitations in PCIe lanes, tradeoffs need to be made depending on the customer’s application. These tradeoffs will be discussed.

Learning Objectives: 1) What limits an all flash solution?; 2) What are the tradeoffs that can be made?; 3) How do these tradeoffs impact the customer’s application?

Kubernetes streamlines the deployment and orchestration of clustered workloads, both on-premise and in the cloud. The Container Storage Interface (CSI) is a newly-defined model for defining templates for mountable volumes and file systems, instantiating them, and binding them to individual containers. In this talk, we will present an overview of the CSI and how it enables rapid migration of workloads to cloud. We will also discuss future directions for extending Kubernetes storage interfaces.

Learning Objectives: 1) How Container Storage Interface (CSI) enables container workloads to easily migrate from on-premise to cloud; 2) How to control volume attributes such as redundancy and location; 3) Opportunities to extend Kubernetes to support new storage technologies and use cases.

Serverless computing is becoming increasingly popular, enabling users to quickly launch thousands of short-lived tasks in the cloud with high elasticity and fine-grain billing. While these properties make serverless computing appealing for interactive data analytics, a key challenge is managing intermediate data shared between tasks. Since communicating directly between short-lived serverless tasks is difficult, the natural approach is to store such ephemeral data in a common remote data store. However, existing storage systems are not designed to meet the demands of serverless applications in terms of elasticity, performance and cost. We present Pocket, a distributed data store that elastically scales to automatically provide applications with desired performance at low cost. Pocket dynamically rightsizes storage cluster resource allocations across multiple dimensions (storage capacity, network bandwidth and CPU cores) as application load varies. We show that Pocket cost-effectively rightsizes the type and number of resources such that applications are not bottlenecked on I/O.

Learning Objectives: 1) We identify the key characteristics of ephemeral data in serverless analytics and synthesize requirements for storage platforms used for sharing such data among serverless tasks; 2) We introduce Pocket, a storage platform targeting the important use case of efficient data sharing in serverless analytics workloads; 3) We present an evaluation of Pocket on AWS Lambda for serverless analytics workloads, demonstrating its effectiveness in terms of performance and resource utilization.

PCIe devices continue to get faster and more powerful. At this point building systems where all DMA traffic must pass through the system memory of the host CPU is becoming problematic. For this reason, there has been considerable work done on both hardware standardization and software frameworks to enable Peer-2-Peer (P2P) DMAs between PCIe End Points (EPs). In this talk we will present an update on the P2PDMA ecosystem and include performance results gathered from systems that have been designed to utilize P2PDMA. We will show how such systems can outperform their conventional counterparts and lead to lower-cost and lower-power designs that still obtain optimal performance. We will also discuss the future for P2PDMA and what work is ongoing to extend the framework.

Learning Objectives: 1) Learn new types of data flow within PCIe based storage systems; 2) NVMe CMBs and how they can be used in interesting ways; 3) How software frameworks like SPDK and Linux are being updated to enable these new data flows; 4) The progress of software enablement for new types of data movement in PCIe/NVMe based storage systems.

Building on the concepts presented in the Introduction to Swordfish sessions, this session will go into more detail on the new Swordfish Scalable Storage Management API specification, including details of the Swordfish Class of Service concepts, structure and usage. It will also cover details of constructing file vs block systems. The deep-dive will also provide a look at the schema, and the ability to support both RESTful and OData clients.

The SNIA’s Scalable Storage Management Technical Work Group (SSM TWG) has developed an open industry standard specification that provides a unified approach for the management of storage systems and data services. This specification extends the DMTF’s Redfish specification using RESTful methods and JSON formatting. This session will present an overview of the specification. This session will also describe the positioning of the specification developed by the SSM TWG vs SMI-S as well as the base Redfish specification.

Many software infrastructures used in modern data centers and storage appliances rely on key value abstraction to access underlying storage and memory systems. However, the existing devices only provide a block interface via storage protocols. As a result, an S/W translation layer such as memcached, LevelDB, RocksDB, extent layer, etc is used to bridge this gap. However, the high overhead of such software layers impact on performance, reliability, resource utilization, and scalability of infrastructure. In this special session of key value SSD, we introduce a comprehensive introduction to key value SSD, covering the concept, device, software ecosystem, system proof point, performance, and standard activity.