Data bytes stored continues to grow at about 40% annually. This trend now exceeds the device capacity growth rate of all existing commercial scale media types including HDD, Flash, Tape and Optical, and the gap between growth rates is about 20%. That implies that the datacenter footprint for storage will be approximately doubling every 3.5 years just to keep up. However, the roadmaps for ongoing density improvement makes the situation much more stark.

The industry needs a new storage medium that is more dense, durable, sustainable, and cost effective to cope with the expected future growth of archival data. DNA, nature’s data storage medium, enters this picture at a time when synthesis and sequencing technologies for advanced medical and scientific applications are enabling the manipulation of synthetic DNA in ways previously unimagined. This session will provide and overview of why DNA data storage is compelling and what the DNA Data Storage Alliance is doing to help build an interoperable DNA Data Storage ecosystem.

Using synthetic DNA for data storage implies two complex steps of digital data processing, namely Encoding and Decoding, which are very different from what we are used to in the conventional Flash environment. Because of the intrinsic statistical behavior of the DNA storage errors (i.e. insertions, deletions, and substitutions), a simulator is required for figuring out the impact of different Encoding/Decoding strategies and algorithms on the storage capabilities of DNA.

Synthetic DNA-based data storage is a major attraction due to the possibility of storage over long periods. This technology is a solution for the current data centers, reducing energy consumption and physical storage space. Nowadays, the quantity of data generated has been growing exponentially, while the storage capacity does not keep up with the growth, caused by new technologies and globalization.

DNA molecules offer an alternative to storage information at extremely high spatial density. They remain stable for hundreds of years at a low energy cost, making them a natural information repository. As we approach the 10th anniversary of the Church’s publication, it is timely to look back and recall the recent main accomplishments. Although it was not the first time that information had been coded into DNA, their work showed the technology’s viability.

The ever-present connectivity in our lives, and the data storage demands that come with it, is growing exponentially. The projected material supply for silicon-based memory technologies is unable to satisfy future demand, therefore, alternative memory materials are being explored in academia and industry1. DNA is analogous to a biological hard drive. It carries and transfers information with exceptional density, stability, and energy efficiency, making it a compelling alternative to current non-volatile information storage technologies.

Google will provide an overview of their experience developing Redfish and Swordfish client applications. This session will provide an overview of lessons learned through the initial proof-of-concept through development phases and will include recommendations to both client and storage vendor implementers of areas that may require additional focus.

If you haven’t caught the new wave in storage management, it’s time to dive in. This presentation provides a broad look at the Redfish and Swordfish ReSTful hierarchies, maps these to some common applications, and provides an overview of the Swordfish tools and documentation ecosystem developed by SNIA’s Scalable Storage Management Technical Work Group (SSM TWG) and the Redfish Forum. It will also provide an overview of what’s new in ’22, including enhancements to NVMe support, storage fabric management, and capacity and performance metric management.

NVMe over IP is a technology able to provide complete and scalable SAN solutions. Security is of paramount importance for SANs and the fundamental methods to secure NMVe over IP fabrics (i.e., DH-HMAC-CHAP authentication and TLS secure channel) have been defined. However, the security provisioning of these methods does not scale yet to large fabrics.

Computational Storage offers near-data acceleration, and it is gaining popularity with recent commercialization and standardization efforts. In this talk, we present how Computational Storage can be used to scale the performance of a key-value storage engine and deep learning training workloads. We propose a new key-value storage engine, named RETINA, where Computational Storage, Samsung SmartSSD, accelerates its data processing and user-defined processing pipelines.