Today, information is being digitized on a massive scale, by servers in datacenters, by mobile devices, and by networks of sensors everywhere around us. Artificial intelligence techniques and ubiquitous processing power are making it possible to mine this massive ocean of data; however, integral to harnessing this data as knowledge is the ability to store it for long periods of time.

CPU performance improvements based on Dennard scaling and Moore's Law have already reached their limits, and domain-specific computing has been considered as an alternative to overcome the limitations of traditional CPU-centric computing models. Domain-specific computing, seen in early graphics cards and network cards, has expanded into the field of accelerators such as GPGPUs, TPUs, and FPGAs as machine learning and blockchain technologies become more common.

Although quantum technology can be leveraged to do many amazing things, it is not able to provide a general replacement for the storage capabilities we have today with HDDs and SSDs. However, there are a few things where quantum can be leveraged to provide some capabilities that are related to storage and this presentation will cover them. The presentation will start with a quick overview of some of the basic concepts of quantum technology and the reasons why quantum computing may potentially provide significant performance improvements over classical computing for certain applications.

When data processing engines are using more and more log semantics, it’s natural to extend Zoned Namespace to provide a native log interface by introduce variable size, byte append-able, named zones. Using the newly introduced ZNSNLOG interface, not only the storage device enables less write amplification/more log write performance, but more flexible and robust naming service. Given the trends towards a compute-storage disaggregation paradigm and more capable computational storage, ZNSNLOG extension enables more opportunities for near data processing.

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.

Two veterans of the storage industry discuss the impact of hardware innovation to the common deployment methods used in SDS.

At scale, embedded storage solutions fall away in favor of distributed approaches that encapsulate and aggregate the many previous steps taken, bringing us to the common SDS methods we use today. The session speakers were present for many of these baby-steps and major milestones, providing a unique perspective to our current “state of the art.”