Microsoft System Center Virtual Machine Manager(SCVMM) automates the complete end to end discovery and integration to leverage replication capabilities provided by our enterprise storage partners using SMI-S. Windows Server provides native support for SMIS providers that SCVMM can utilize. Building on top of SCVMM primitives, Azure Site Recovery provides the end to end disaster recovery and orchestration solution automating the creation and management of all target objects including storage and compute.

Persistent memory offers new ways to program with long-lived data. Java is a popular language for data center applications such as databases. This session will describe ways to access persistent memory from Java and relate some of our application experience, including development of a persistent memory storage engine for the Cassandra database.

Cloud native deployment has become one of the major trends for large scale Big Data analytics. Compared to on-premise data center, cloud offers much stronger scalability and higher elasticity to Big Data applications. However, cloud is also considered to be less performant than on-premise alternatives due to virtualization and cluster resource disaggregation.

Modern Software Defined Storage systems use heterogeneous storage media including disks, SSDs, and (soon) persistent memory. It’s important for these systems to identify, segregate and optimally utilize such storage media to gain advantages in performance, capacity and cost. Gluster's Data Tiering feature aims to help tackle these problems by segregating fast and slow storage into separate tiers, and providing intelligent movement of data across tiers according to dynamically observed current usage patterns.

Problem Description: In object store systems objects are identified using a unique identifier. Objects on disk are immutable. Any modification to an existing object, results in new object with different timestamp to be created on disk. A common method to distribute objects to storage nodes is using consistency hashing method. A storage node can have multiple disks to store the objects assigned to it and based on the application usage patterns some hash bucket grow faster than others resulting in some disks getting more used than others.

Increasing proliferation of Artificial Intelligence, E-commerce, Big Data and Cloud applications is leading to highly diversified workloads and use cases in hyperscale data centers, which poses new challenges to solid state storage in terms of performance, flexibility and TCO optimizations. Moreover, there are increasing demands for software/hardware co-optimization and more control over I/O path from applications. Standard SSDs that are tuned for a few generic workloads cannot meet these challenges, resulting in suboptimal performance and TCO.

The DMTF’s Scalable Platforms Management Forum (SPMF) is working to create and publish an open industry standard specification, called “Redfish” for simple, modern and secure systems management using RESTful methods and JSON formatting. This session will cover the design tenets, protocol and payload, expected deliverables and time frames.

Learning Objectives

Understanding the Redfish goals
Understanding how Redfish applies to storage topics
Developing Redfish support
Understanding the Redfish Open Source efforts

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.

Swordfish™ is an extension of the DMTF Redfish specification developed by the Storage Networking Industry Association (SNIA) to provide a unified approach for the management of storage equipment and services in converged, hyper-converged, hyperscale and cloud infrastructure environments, making it easier for IT administrators and DevOps to integrate scalable solutions into their data centers.

All flash arrays incorporate a number of data reduction techniques to increase effective capacity and reduce overall storage costs. Compression and deduplication are two commonly employed techniques, each with multiple different strategies for implementation. Because compression and data reduction are only part of a greater data reduction strategy, one must also understand their codependent interactions with the rest of a storage system. This talk presents a structured overview of multiple different compression and deduplication technologies.