Amazon AWS S3 storage is widely deployed to store everything from customer data, server logs, software repositories and so on. Poorly secured S3 buckets have resulted in many publicized data breaches. The cloud service provider's shared responsibility model places responsibility on customers for protecting the confidentiality, availability and integrity of their data. Thales Cipher Trust Encryption Cloud Object Storage for S3 secures S3 objects by enabling advanced encryption along with dual end point access controls.

Zoned Namespace SSDs are SSDs that implement the ZNS command set as specified by the NVM Express organization. ZNS SSDs provide an interface to the host such that the host/applications can manage the data placement on these SSDs directly. This presentation plans to cover the basics of ZNS SSDs and demonstrate the software stack through which MySQL can be run on ZNS SSDs. MySQL integrated with RocksDB is called MyRocks, this presentation discusses about ZNS support in RocksDB and MySQL and evaluate the performance of and QoS benefits for MySQL on a ZNS SSD viz-a-viz a conventional SSD.

EDSFF (Enterprise and Data Center SSD Form Factor) SSD has been widely adopted in hyper-scale data centers because of superior manageability, serviceability, and power/thermal characteristics. The new form factor provides high performance (support up to PCIe Gen6) and high capacity (up to 128TB), and allows to support new type of devices like NIC, accelerator, and CXL DRAM device in a system. However, adopting of EDSFF in NVMe-oF system is non-trivial. For example, with EDSFF SSD, the capacity of single storage server can easily have Petabyte in total.

The ransomware attacks raised the need of an appropriate defense, bringing back the mature idea of WORM. Having pre-existing WORM capability in the disk storage alone is not enough for the backup area due to multiple problems like usage complexity, and potential for backup application mishandling of WORM partitions. Instead, integrated WORM support delivered by cooperation of the backup application with the disk storage delivers a complete WORM solution addressing these problems.

Opening day of SDC 2021.

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.