Object stores are known for ease of use and massive scalability. Unlike other storage solutions like file systems and block stores, object stores are capable of handling data growth without increase in complexity or developer intervention. Apache Hadoop Ozone is a highly scalable Object Store which extends the design principles of HDFS while maintaining a 10-100x scale compared to HDFS. It can store billions of keys and hundreds of petabytes of data. With the massive scale there is a requirement for it to have very high throughput while maintaining low latency.

Apache Ozone is an object store which scales to tens of billions of objects, hundreds of petabytes of data and thousands of datanodes. Ozone not only supports high throughput data ingestion but also supports high throughput deletion with performance similar to HDFS. Further with massive scale the data can be non-uniformly distributed due to addition of new datanodes, deletion of data etc. Non-uniform distribution can lead to lower utilisation of resources and can affect the overall throughput of the cluster.

The most expensive factor in traditional archival storage is that it is not durable and, thus, over the years, it is necessary to do many migrations due to degradation and technology obsolescence. DNA reading technology, due to the immutable format of the DNA molecule, will not be obsolete, mitigating this obsolescence. However low cost DNA storage does come with some imperatives. Indeed DNA outside the cell, as with any biological molecule, will be subject to aggressive degradation factors, the main one being water.

Ransomware is an acknowledged threat, and protecting your data must be a security-in-depth exercise. We discusses how Intelligent Storage can detect and recover from an attack while maintaining administrative isolation from compromised servers. While this method is only a single layer of a defence-in-depth infrastructure, it can be implemented invisibly on existing workloads and storage which can gather the proper sets of metrics.

Enabling data storage on DNA relies on advancements in semiconductor technology to make DNA synthesis cheaper, which is a must-have for this field to emerge. The talk will introduce storage people to the concept of how semiconductors are used to create DNA and how the two are tied together, as well as how the advancements in semiconductors are crucial to bringing DNA data storage costs down.

Business requirements are not the only influencers of our technical solutions. Laws and Regulations transform the technical landscape in ways that require us to redefine our architecture, as well as our skill-set. This is especially true with Data Privacy. Since GDPR and CCPA, our industry is witnessing a new career path emerge: the Privacy Engineer. Where security started 10 years ago, so does privacy engineering. Join us as we look at Privacy by Design (PbD) and introduce some architecture patterns that align with privacy strategies.

Every organization today is in some state of digital transformation. While the understanding of security needs in the digital age has matured significantly in the last 2 decades, the implication for data privacy and in particular its interaction with technology solutions, are still not well understood. As data regulations and laws continue to evolve, globally, organizations require an increased understanding of privacy requirements and their impact on technology solutions.

Samba had experimental support for multi-channel for quite a while. SMB3 has a few concepts to replay requests safely. We now implement them completely (and in parts better than a Windows Server). The talk will explain how we implemented the missing features. With the increasing amount of network throughput, we'll reach a point where a data copies are too much for a single cpu core to handle. This talk gives an overview about how the io_uring infrastructure of the Linux kernel could be used in order to avoid copying data, as well as spreading the load between cpu cores.

Standardized computational storage services are frequently touted as the Next Big Thing in building faster, cheaper file systems and data services for large-scale data centers. However, many developers, storage architects and data center managers are still unclear on how best to deploy computational storage services and whether computational storage offers real promise in delivering faster, cheaper – more efficient – storage systems. In this talk we describe Los Alamos National Laboratory’s ongoing efforts to deploy computational storage into the HPC data center.

Almost everyone understands that systems and data both have lifecycles that typically include a disposal phase (i.e., what you do when you do not need something anymore). Conceptually, data needs to be eliminated either on a system or entirely (everywhere stored) as part of this disposal. Simply hitting the delete-key may seem like the right approach, but the reality is that eliminating data can be difficult. Additionally, failing to correctly eliminate certain data can result in costly data breach scenarios.