Storage Developer Conference

Storage virtualization is gaining popularity as an efficient way to increase the flexibility and the consolidation of data centers. Virtualization is commonly used to provide fast and reliable storage access and therefore constitute an important factor to fulfill customer’s demand for a minimum latency in cloud storage applications. This work introduces a new approach for implementing the iSCSI extensions for RDMA protocol (iSER) using block virtualization techniques. The iSER protocol is implemented in the user-space using the common Libiscsi library and serve as the transportation layer protocol instead of the common TCP protocol. A unique implementation of a virtual block device using Quick Emulator (QEMU) is proposed to carry out block virtualization delivered over iSCSI.

The benefit of using iSCSI/iSER protocol over the common iSCSI/TCP is clearly demonstrated. Experiments results demonstrate average improvement factor of about 200% over the common iSCSI/TCP in term of average I/O operations per second while using the conventional Flexible I/O storage benchmark. The proposed approach is compared to two commonly iSER implementations used for block virtualization. Both methods use common iSER kernel modules. The first method uses a virtual-function while the second technique uses iSER block device virtualization. The proposed user-space iSER approach shows significant performance improvement over the two existing methods for different typical workloads. The current research results are promising and applicable also to future upcoming protocols such as NVMe over Fabrics implementations.

Learning Objectives: Storage virtalization, RDMA, iSER, QEMU.

For years, enterprises have desired a way to ensure that an application will operate properly at scale on a particular storage array and network infrastructure, and increasingly, ensure that multiple applications can safely run at capacity on an all-flash or hybrid architecture. Companies need an approach that enables users to reference detailed workload analysis profiles as they build and share workload models, and simulate performance across a variety of vendors, products and technologies.

Learning Objectives: 1) How to determine what the traffic related to an enterprise application implementation looks like from the perspective of the network infrastructure between application servers and storage arrays; 2) How the application I/O workload can be modeled to accurately represent the application in production using storage analytics or vendor performance statistics to generate a synthetic workload model; 3) How a statistical application model can be used to validate both array and network performance at varying traffic levels and rates to rapidly test if/then scenarios; 4) How multiple application models can be combined to determine appropriate throughput and response time levels on a given array; 5) How using these models can enable engineers and architects to cost-effectively test and confidently deploy new networked storage infrastructure.

NVMe SSDs are becoming increasingly popular choice in scale out storage for latency sensitive workloads like databases, real time analytics, video streaming. NVMe SSDs provide significant performance throughput and lower latency compared to SATA, SAS SSDs. It is not unrealistic to expect these devices providing close to million random IOs per second. However scale out software stacks have significant amount of software overhead limiting the immense potential of NVMe SSDs. In this session, we present all flash scale out cluster performance, analysis on data path I/O overhead and programming techniques to systemically address software performance barriers.

Learning Objectives: 1) Scale out storage software data path flows; 2) Performance profiling with NVMe SSDs; 3) User mode v/s kernel mode NVMe SSD integration; 4) Optimization techniques.

A new class of ultra low latency remote storage is emerging - nonvolatile memory technology can be accessed remotely via high performance storage protocols such as SMB3, over high performance interconnects such as RDMA. A new ecosystem is emerging to "light up" this access end-to-end. This presentation will explore one path to achieve it, with performance data on current approaches, analysis of the overheads, and finally the expectation with simple extensions to well-established protocols.

With a design started in 2006, OpenIO is a new flavor among the dynamic object storage market segment. Beyond Ceph and OpenStack Swift, OpenIO is the last coming player on that space. The product relies on an open source core object storage software with several object APIs, file sharing protocols and applications extensions. The inventors of the solution took a radical new approach to address large scale environment challenges. Among them, the product avoids any rebalance like consistent hashing based systems always trigger. The impact is immediate as new machines contribute immediately without any extra tasks that impact the platform service. OpenIO also introduces the Conscience, an intelligent data placement service, that optimizes the location of the data based on various criteria such nodes workload, storage space. OpenIO is fully hardware agnostic, running on commodity x86 servers promoting a total independence.

Standardization efforts have continued for features to improve SSD performance and endurance. NVMe, SCSI, and SATA have completed standardization of streams and background operation control. Standardization is beginning on how SSDs may implement Key Value Storage (KVS) and In Storage Compute (ISC). This effort is progressing in the SNIA Object Drive Technical Work Group (TWG), and may involve future work in the protocol standards (NVMe, SCSI, and SATA). Currently the Object Drive TWG is defining IP based management for object drives in utilizing the DMTF RedFish objects. Future Object Drive TWG work will include APIs for KVS and ISC. This presentation will discuss the standardization of streams, background operation control, KVS, and ISC and how each of these features work.

Learning Objectives: 1) What is streams; 2) What is background operation control; 3) Progress of standardization in NVMe, SCSI, SATA, and SNIA.

A tutorial on using the open source persistent memory (PMEM) programming library offered at pmem.io; including examples providing power-fail safety, while storing data on PMEM. Programming examples will zero in on pmem.io’s transactional object store (i.e. “libpmemobj”) library, which is layered on the SNIA NVM Programming Model. The examples covered will demonstrate proper integration techniques, macros, C API, key theory concepts, terminology, and present in depth overview of what the library offers for PMEM programming initiatives.

Learning Objectives: 1) Overview of persistent memory programming; 2) Close examination of the transactional object store library (libpmemobj); 3) Code integration walkthrough.

The SNIA NVM Programming model enables applications to consume emerging persistent memory technologies through step-wise evolution to greater and greater value. Starting with an overview of the latest revision of the NVM programming model specification this session summarizes the recent work of the NVM programming TWG in areas of high availability and atomicity. We take an application view of ongoing technical innovation in a persistent memory ecosystem.

Learning Objectives: 1) Learn what the SNIA NVM programming TWG has been working on; 2) Learn how applications can move incrementally towards greater and greater benefit from persistent memory; 3) Learn about the resources available to help developers plan and implement persistent memory aware software.

Building on the concepts presented in the Introduction to Swordfish (and Redfish) sessions, this session will go into more detail on the new Swordfish API specification. Learning Objectives: 1) Introduction to the specifics of the Swordfish, API, 2) Working with the Swordfish Schema.

The SNIA’s Scalable Storage Management Technical Work Group (SSM TWG) is working to create and publish an open industry standard specification for storage management that defines a customer centric interface for the purpose of managing storage and related data services. This specification builds on the DMTF’s Redfish specification’s using RESTful methods and JSON formatting. This session will present an overview of the specification being developed by SSM including the scope targeted in the initial (V1) release in 2016 vs later (2017). This session will also provide the positioning of the specification developed by the SSM TWG vs SMI-S as well as the base Redfish specification.