Storage Developer Conference

In July 2012 the SNIA NVM Programming Model TWG was formed with just 10 participating companies who set out to create specifications to provide guidance for operating system, device driver, and application developers on a consistent programming model for next generation non-volatile memory technologies. To date, membership in the TWG has grown to over 50 companies and the group has published multiple revisions of The NVM Programming Model. Intel and Microsoft have been long time key contributors in the TWG and we are now seeing both Linux and Windows adopt this model in their latest storage stacks. Building the complete ecosystem requires more than just core OS enablement though; Intel has put considerable time and effort into a Linux based library, NVML, that adds value in multiple dimensions for applications wanting to take advantage of persistent byte addressable memory from user space. Now, along with Intel and HPE, Microsoft is moving forward with its efforts to further promote this library by providing a Windows implementation with a matching API. In this session you will learn the fundamentals of the programming model, the basics of the NVML library and get the latest information on the Microsoft implementation of this library. We will cover both available features/functions and timelines as well as provide some insight into how the open source project went from idea to reality with great contributions from multiple companies.

Learning Objectives: 1) NVM Programming Model Basics; 2) The NVM Libraries (NVML); 3) The Windows Porting Effort.

Data retention and preservation is rapidly becoming the most impacting requirement to data storage. Regulatory and corporate guidelines are causing stress on storage requirements. Cloud and Big Data environments are stressed even more by the growth of rarely touched data due to the need to improve margins in storage. There are many choices in the market for data retention, managing the data for decades must be as automated as possible. This presentation will outline the most effective storage for Long term data preservation, emphasizing Total Cost of Ownership, ease of use and management, and lowering the carbon footprint of the storage environment.

Over the past 3 years we’ve been collecting daily SMART stats from the 60,000+ hard drives in our data center. These drives have over one billion hours of operation on them. We have data from over 20 drive models from all major hard drive manufacturers and we’d like to share what we’ve learned. We’ll start with annual failure rates of the different drive models. Then we’ll look at the failure curve over time, does it follow the “bathtub curve” as we expect. We’ll finish by looking a couple of SMART stats to see if they can reliably predict drive failure.

Learning Objectives: 1) What is the annual failure rate of commonly used hard drives?; 2) Do hard drives follow a predictable pattern of failure over time?; 3) How reliable are drive SMART stats in predicting drive failure?

It is well-known that cache performance is non-linear in cache size and the benefit of caches varies widely by workload. Irrespective of whether the cache is in a storage system, database or application tier, no two real workload mixes have the same cache behavior! Existing techniques for profiling workloads don’t measure data reuse, nor do they predict changes in performance as cache allocations are varied.

Recently, a new, revolutionary set of techniques have been discovered for online cache optimization. Based on work published at top academic venues (FAST '15 and OSDI '14), we will discuss how to 1) perform online selection of cache parameters including cache block size and read-ahead strategies to tune the cache to actual customer workloads, 2) dynamic cache partitioning to improve cache hit ratios without adding hardware and finally, 3) cache sizing and troubleshooting field performance problems in a data-driven manner. With average performance improvements of 40% across large number of real, multi-tenant workloads, the new analytical techniques are worth learning more about.

Learning Objectives: 1) Storage cache performance is non-linear, benefit of caches varies widely by workload mix; 2) Working set size estimates don't work for caching Miss ratio curves for online cache analysis and optimization; 3) How to dramatically improve your cache using online MRC, partitioning, parameter tuning; 4) How to implement QoS, performance SLAs/SLOs in caching and tiering systems using MRCs.

Anyone managing a mass storage infrastructure for HPC, Big Data, Cloud, research, etc., is painfully aware that the growth, access requirements and retention needs for data are relentless. At the heart of that problem is the need to rationalize the way that data is managed, and create online access to all that data without maintaining it in a continuous, power-consuming state. The solution lies in creating an active archive that enables straight-from-the-desktop access to data stored at any tier for rapid data access via existing file systems that expand over flash, disk and tape library storage technologies. Active archives provide organizations with a persistent view of the data and make it easier to access files whenever needed, regardless of the storage medium being utilized.

Learning Objectives: 1) Understand how active archive technologies work and how companies are using them to enable reliable, online and efficient access to archived data; 2) Learn the implications of data longevity and planning considerations for long-term retention and data integrity assurance; 3) Learn why active archive solutions can achieve unmatched efficiency and cost savings as data continues to grow much faster than storage budgets.

One of the major barriers to adoption of persistent memory is preparing applications to make use of it's direct access capabilities. This presentation discusses a new user space file system for persistent memory and how it breaks these barriers. The presentation introduces the key elements to consider for a user space persistent memory file system and discuss the internals of this new file system. The discussion concludes with a presentation of current status and performance of this new persistent memory file system.

Learning Objectives: 1) Discussion of current barriers to persistent memory adoption; 2) Introduce how this new file system breaks down the barriers to adoption of persistent memory; 3) Introduce the SW internals of the this file system; 4) Present performance statistics and discussion of why this file system out-performs conventional, kernel based file systems.

This presentation reviews how classic storage architectures create barriers to throughput and scaleability for several of today's hot applications. Case studies and benchmarks are used to illustrate how a very high performance, NVMe networked storage solution allows these applications to break through these barriers.

Learning Objectives: 1) Understand how storage can limit scale out application performance; 2) Understand how new scale out apps apply different tiers of storage; 3) Understand barriers to application scale out; 4) Understand barriers to application throughput; 5) Understand how networked NVMe storage can dramatically improve app performance and scalability.

With many SMB3 servers in the industry, some with optional extensions, getting optimal configuration, and POSIX compliance can be confusing.

The Linux kernel client continues to improve with new performance and security features, and implementation of SMB3.1.1. This presentation will discuss recent enhancements to the Linux kernel client, as well as extensions to provide improved Apple (AAPL) interoperability and new POSIX extensions for Linux/Samba (see Jeremy Allison's presentation) and improved POSIX emulation to other servers.

New copy offload features will be demonstrated, as well as the current state of POSIX compatibility to different server types. Finally, a discussion of new protocol features under development in the Linux kernel client will be discussed.

Learning Objectives: 1) How POSIX compliant is access from Linux to SMB3 servers?; 2) How can I best configure access depending on server type: Windows or Mac or Samba or other NAS?; 3) What is the status of the Linux kernel client? What new features are available?; 4) How can I get optimal performance out of the Linux kernel client; 5) What are the advantages to using SMB3 for Linux?

Significant advances in throughput and latency for non-volatile media and networking require scalable and efficient software to capitalize on these advancements. This session will present an overview of the Storage Performance Development Kit, an open source software project dedicated to providing building blocks for scalable and efficient storage applications with breakthrough performance. There will be a focus on the motivations behind SPDK's userspace, polled-mode model, as well as details on the SPDK NVMe, CB-DMA, NVMe over Fabrics and iSCSI building blocks. http://spdk.io.

Learning Objectives: 1) Why use userspace drivers; 2) When polling is better than interrupts; 3) Applying shared-nothing architecture to storage.

Linux is usually at the edge of implementing new storage standards, and NVMe over Fabrics is no different in this regard. This presentation gives an overview of the Linux NVMe over Fabrics implementation on the host and target sides, highlighting how it influenced the design of the protocol by early prototyping feedback. It also tells how the lessons learned during developing the NVMe over Fabrics, and how they helped reshaping parts of the Linux kernel to support over Fabrics and other storage protocols better