Emerging and existing applications with cloud computing, 5G, IoT, automotive, and high-performance computing are causing an explosion of data. This data needs to be processed, moved, and stored in a secure, reliable, available, cost-effective, and power-efficient manner. Heterogeneous processing, tiered memory and storage architecture, accelerators, and infrastructure processing units are essential to meet the demands of this evolving compute, memory, and storage landscape.

The hyperscale cloud innovates through relentless optimization. Cloud providers are always looking for ways to maximize the efficiency of every hardware and software component they deploy. To help them achieve that goal, KIOXIA released the open source Software-Enabled Flash™ API, which redefines the relationship between the host and flash devices, and allows cloud-scale users to unlock the most value from their flash.

In this talk you will learn about the reasons for building a specialized storage system for databases, and how Aurora databases are built using such a storage system in cloud.

Blockchain has revolutionized decentralized finance, and with smart-contracts has enabled the world of Non-Fungible Tokens, set to revolutionize industries such as art, collectibles and gaming. Blockchains, at the very core, are distributed chained hashes. They can be leveraged to store information in a decentralized, secure, encrypted, durable and available format. However, some of the challenges in Blockchain stem from the bloat of storage.

Machine learning referred to as ML, is the study and development algorithms that improves with use of data -As it deals with the training data, the machine algorithm changes and grows. Most machine learning models begin with “training data” which the machine processes and begins to “understand” statistically. Machine learning models are resource intensive. To anticipate, validate, and recalibrate millions of times, they demand a significant amount of processing power. Training an ML model might slow down your machine and hog local resources.

Abstract: DNA data storage is an attractive option for digital data storage because of its extreme density, durability and eternal relevance. This is especially attractive when contrasted with the exponential growth in world-wide digital data production. In this talk we will present our efforts in building an end-to-end system, from the computational component of encoding and decoding to the molecular biology component of random access, sequencing and fluidics automation.

The Open Data Framework (ODF) unifies data and storage management from the core, to cloud and to edge. In this talk, we will show how ODF simplifies Kubernetes storage management, provides data protection for applications, and connect data on-prem to clouds. We will also be introducing how ODF can be extended with other SODA projects such as DAOS - a distributed asynchronous object storage for HPC, ZENKO - a multicloud data controller with search functionality, CORTX - an object storage optimized for mass capacity storage and others (YIG, LINSTOR, OpenEBS).

HPE will provide an overview of their experience developing an initial Swordfish implementation. This session will provide an overview of lessons learned through the initial proof-of-concept through development phases and will include recommendations to other implementers of areas that may require additional focus.

In this talk we present FlexAlloc: a lean block allocator focused on bridging the gap between raw block device access in user space and data management through monolithic file systems.

As you know, the Storage Performance Development Kit (SPDK) provides a set of tools and libraries for writing high performance, scalable, user-mode storage applications. Kalray’s MPPA® manycore architecture proposes a unique 80-cores system.