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.
Traditional Storage Node consists of Compute, Networking and Storage elements. In this case, the entire node is a single failure domain and as such both data and meta data are maintained in storage. Emergence of CXL allows us to re-think the traditional storage node architecture. In future, the storage (behind CXL IO) and metadata memory (behind a CXL memory) can be disaggregated locally or across a bunch of storage nodes to improve the availability of the data. Further, memory persistence can be achieved at a granular level using CXL memory devices.
Computational Storage continues to gain interest and momentum as standards that underpin the technology mature. Developers are realizing that moving compute closer to the data is a logical solution to the ever-increasing storage capacities. Data-driven applications that benefit from database searches, data manipulation, and machine learning can perform better and be more scalable if developers add computation directly to storage.
