Storage Developer Conference

While originally designed for disk drives, the read/write I/O model has provided a common storage abstraction for decades, regardless of the type of storage medium.

Devices are becoming increasingly complex, however, and the constraints of the old model have compelled the standards bodies to develop specialized interfaces such as the Object Storage Device and the Zoned Block Commands protocols to effectively manage the storage.

While these protocols have their place for certain workloads, there are thousands of filesystems and applications that depend heavily on the old model. It is therefore compelling to explore how the read/write mechanism can be augmented using hints and stream identifiers to communicate additional information that enables the storage to make better decisions.

The proposed model is applicable to all types of storage devices and alleviates some of the common deficiencies with NAND flash and Shingled Magnetic Recording which both require careful staging of writes to media.

Cloud block storage implementations, such as Ceph RADOS Block Device (RBD) and Microsoft Azure Page Blobs, are considered flexible, reliable and relatively performant.

Exposing these implementations for access via an embedded USB storage gadget can solve a number of factors limiting adoption, namely:

Interoperability - Cloud storage can now be consumed by almost any system with a USB port

Ease of use - Configure once, then plug and play

Security - Encryption can be performed on the USB device itself, reducing reliance on cloud storage providers

This presentation will introduce and demonstrate a USB cloud storage gateway prototype developed during SUSE Hack Week, running on an embedded Linux ARM board.

Learning Objectives: 1) Knowledge of existing Ceph and Azure block storage implementations; 2) Awareness of problems limiting cloud storage adoption; 3) Evaluate a USB cloud storage gateway device as a solution for factors limiting adoption.

The SPEC SFS subcommittee is currently finalizing an industry-standard workload that simulates the storage access patterns of large-scale EDA environments. This workload is based upon dozens of traces from production environments at dozens of companies, and it will be available as an addition to the SPEC SFS benchmark suite. Join us to learn more about the storage characteristics of real EDA environments, how we implemented the EDA workload in the SPEC SFS benchmark, and how this workload can help you evaluate the performance of storage solutions.

Cloud computing frameworks like Kubernetes are designed to address containerized application management using "service" level abstraction for delivering smart data center manageability. Storage management intelligence and interfaces need to evolve to support "service" oriented abstraction. Having every computing framework reinvent the storage integration makes the storage management more complex from end user perspective. Moreover it adds significant burden on storage vendors to write drivers and certify for every orchestration stack which is least desirable. In this session, we present industry wide effort to develop unified storage management interfaces that work across traditional and cloud computing frameworks and eliminate the need to reinvent storage integration.

Learning Objectives: 1) Storage integration in container frameworks; 2) Unified storage management interface; 3) Open source community work.

BetrFS is a new file system that outperforms conventional file systems by orders of magnitude on several fundamental operations, such as random writes, recursive directory traversals, and metadata updates, while matching them on other operations, such as sequential I/O, file and directory renames, and deletions. BetrFS overcomes the classic trade-off between random-write performance and sequential-scan performance by using new "write-optimized" data structures.

This talk explains how BetrFS's design overcomes multiple file-system design trade-offs and how it exploits the performance strengths of write-optimized data structures.

NVMe is catching fire in the market and after years of incubation it is poised to become a major player in server, storage and networking implementations. In this session, G2M Research will discuss the development, deployment models, uses cases and market opportunity for NVMe across enterprise, Telco, Cloud, IoT and embedded applications. NVMe will be used for more than just accelerating SSD, it will become a major player in new computing models for compute, fabrics, analytics, application acceleration, systems management and more. Come see how NVMe will evolve and be used in more ways than you ever thought possible.

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.