For the past 10 years, Backblaze has collected SMART statistics and failure data for over 250,000 hard drives and SSDs. Over that time, for over 100+ drive models, we have reported on drive failure rates as it relates to drive size, manufacturer, temperature, drive class, drive type and more. For this session will deliver our latest research on these attributes including any relevant trends and observations.
Amazon S3 is one of the oldest and most widely used object storage services, and its API has become the industry standard for object storage. Ensuring compatibility with Amazon S3 API is crucial for object storage developers who want to benefit from the wide ecosystem of the existing applications. However, achieving 100% compatibility can be challenging due to the complexity and variety of S3 APIs, access control mechanisms, and performance and scalability requirements.
This talk describes a set of desired distributed location and data motion patterns that optimize storage/data placement for hybrid/multicloud and/or edge-to-core-to-cloud outcomes. What capabilities do you need to expect from your storage platforms to execute on a strategy that mitigates or completely eliminates cloud egress costs, yet also maximizes performance for workloads wherever they need to reside? How do you include sovereignty and governance into your foundational architecture?
The Storage Performance Development Kit (SPDK) provides a set of tools and libraries for writing high performance, scalable, user-mode storage applications. It's an ideal open source software framework for building IPU/DPU based storage solutions including initiators, targets and local SSD virtualization. This presentation talks about recent SPDK enhancements for IPU/DPU support, how SPDK is used to enable IPU/DPU based storage solutions, and the potential of SPDK to help drive toward standard based IPU/DPU storage solutions.
Flexible Data Placement (FDP) represents the latest development in mainstream data placement technology for NVMe. Although its use-cases resemble those of other NVMe features, such as Streams and ZNS, the differences have significant implications for the implementation within host storage stacks and applications. As host stacks adopt various data placement technologies, the risk of bloated codebases and redundant implementations rises, increasing maintenance costs for large mainline projects.
High-performance workloads demand on-package integration of heterogeneous processing units, on-package memory, and communication infrastructure to meet the demands of the emerging compute landscape. Applications such as artificial intelligence, machine learning, data analytics, 5G, automotive, and high-performance computing are driving these demands to meet the needs of cloud computing, intelligent edge, enterprise, and client computing infrastructure.
Rust for Linux has brought in Rust as a second programming in the Linux Kernel. The Rust for Linux project is making good progress towards building a general framework for writing Linux kernel device drivers in safe Rust. The Rust NVMe driver is an effort to implement a PCI NVMe driver in safe Rust, for use in the Linux Kernel. The purpose of the driver is to provide a vehicle for development of safe Rust abstractions for the Linux kernel, and to prove feasibility of Rust as an implementation language for high performance device drivers.
The EDSFF E3 form factors bring more options and commonality that enterprises and hyperscalers desire compared to traditional U.2, but also bring new challenges. With power envelopes increasing in many cases, it is difficult to fit more components on board to maximize capabilities. With a lack of E3 enclosures on the market, there many uncertainties like enclosure geometries and airflow profiles, and the uncertainty of total system power delivery capability.
According to IDC forecast, global data will almost double in 3 years and 90% of the data generated is unstructured data which comes in many forms such as documents, videos, images, audio, IOT data, etc. The difficulty of managing the different forms of unstructured data exacerbated by myriads of interfaces and technologies in hybrid multi-cloud environments lead to data sprawl which is one of the major pain points for any organization.
SDXI v1.0 is a standard for a memory-to-memory data mover and acceleration interface that is extensible, forward-compatible, and independent of I/O interconnect technology. Among other features, SDXI standardizes an interface and architecture that can be abstracted or virtualized with a well-defined capability to quiesce, suspend, and resume the architectural state of a per-address-space data mover. This specification was developed by SNIA’s SDXI Technical Working Group, comprising of 89 individuals representing 23 SNIA member companies.
