The SDXI TWG is working on a proposed standard for a memory to memory Data mover and acceleration interface.
SDXI TWG members are working to get its first version of the Smart Data Acceleration Interface Specification out for a public review.
Join the SNIA Computational Storage TWG and SIG for a Computational Storage update and discussion of the latest SNIA CS Architecture and Programming Model and API.
SNIA Green Storage TWG new specification work to complement the current SNIA Emerald system-level specification referenced by regulators. The session will review the value proposition to the supply chain, the testing tools, and moderate a discussion on test tools, test configurations and device types, IO workloads, and measurement metrics. The project stage is welcoming device vendors and stakeholders to join the effort.
The panel will discuss why interoperability in blockchain is important to the Storage Iindustry. What are the lessons learned. How companies can benefit.
NVMe and SNIA are both working on standards related to Computational Storage. The question that is continually asked is are these efforts are compatible or at odds with each other. The truth is that many of the same people are working on both of these standards efforts and are very interested in ensuring that they work together as opposed to conflicting with each other.
Azure Block Storage, also referred to as Azure Disks, is the persistent block storage for Azure Virtual Machines and a core pillar for Azure IaaS infrastructure. Azure offer unique block storage capabilities that differentiate it from other Cloud Block Storage offerings. In this talk, we will use a few of these capabilities as examples to reveal the technical designs behind and how they are tied to our XStore storage architecture.
Large-scale cluster storage systems contain hundreds-of-thousands of hard disk drives in their primary storage tier. Since the clusters are not built all at once, there is significant heterogeneity among the disks in terms of their capacity, make/model, firmware, etc. Redundancy settings for data reliability are generally configured in a “one-scheme-fits-all” manner assuming that this heterogeneous disk population has homogeneous reliability characteristics.
DPUs (data processing units) are an exciting new category of processor that complement CPUs and GPUs inside data centers. DPUs are fast at data-centric tasks, while they retain the full programmability of CPUs. DPUs have typically been used to offload networking and security functions from compute servers, but have not been used to build storage systems until recently. In this talk, we will briefly introduce DPUs, then highlight the use of DPUs to build storage systems. This is an emerging new use case for DPUs.
Large-scale data analytics, machine learning, and big data applications often require the storage of a massive amount of data. For cost-effective high bandwidth, many data centers have used tiered storage with warmer tiers made of flashes or persistent memory modules and cooler tiers provisioned with high-density rotational drives.
