Today, storage and memory hierarchies are manually tuned and sized at design time. But tomorrow’s workloads are increasingly dynamic, multi-tenant and variable. Can we build autonomous storage systems that can adapt to changing application workloads? In this session, we demonstrate how breakthroughs in autonomous storage systems research can deliver impressive gains in cost, performance, latency control and customer out-of-the-box experience.

In mechanical engineering, CAD has enabled engineers, architects, and construction to create fully-featured designs so that they can visualize the construction which enables the development, modification, and optimization of the design process. Why is this missing from the world of performance engineering? Until now, it has been seen as an intractable problem to build for the exponential difficulty of complex storage and memory hierarchies. That’s no longer the case.

Extreme Compute needs Extreme IO. The convergence of HPC and AI are using GPUs in wider range of applications than ever before on multitude of platforms ranging from edge devices, commodity hardware to high performance supercomputers. Larger datasets enable more accurate AI models which gathers deeper information enabling enterprises to collect more and more data. This virtuous cycle is enabling the explosive demands in processing larger amounts of data and the need to reduce IO bottlenecks is greater than ever.

In DellEMC Enterprise Server/Storage Validation Organization, We perform Load testing using different workloads (Web, File, FTP, Database, Mail, etc.) on Servers to identify the performance of the Systems under heavy load. Knowing how DellEMC Enterprise Systems perform under heavy load (% CPU, % Memory, % Network, % Disk) is extremely valuable and critical. This is achieved with the help of a Load Testing Tools. Load testing tools available in market comes with its own challenges like Cost, Learning Curve and Workloads Support.

It is broadly known that in an operating system, if any file is deleted, Discard will be issued to underlying storage device. When user deletes file through Operating system, it is not physically deleted from the storage medium, as a matter of fact, this file data is marked as Invalid but remains in the unmapped address space. In another instance, when host performs over write on the previously written logical space and then this previously written memory space can be invalidated by discard operation.

Current enterprise storage devices have to service many diverse and continuously evolving application workloads (For e.g., OLTP, Big Data/Analytics and Virtualization). These workloads combined with additional enterprise storage services like deduplication, compression, snapshots, clones, replication, tiering etc. result in complex I/Os to the underlying storage. Traditional storage system tests make use of benchmarking tools, which generate a fixed and constant workload, comprised of a single or few I/O access patterns and are not sufficient for enterprise storage testing.

Current workloads at data centers are changing at a blistering pace and fast becoming data-centric. Modern cloud-native applications are written as microservices distributed across network connected servers and many of these applications need to process large amounts of data quickly—data that cannot fit in a single server and therefore needs to be “sharded” or spread across many servers.

Polled mode applications such as the Storage Performance Development Kit (SPDK) NVMe over Fabrics target can demonstrate higher performance and efficiency compared to applications with a more traditional interrupt-driven threading model. But this performance and efficiency comes at a cost of increased CPU core utilization when the application is lightly loaded or idle. This talk will introduce a new SPDK scheduler framework which enables transferring work between CPU cores for purposes of shutting down or lowering frequency on cores when under-utilized.

Data has become the new source code and data storage and I/O are becoming the major inhibitor for deriving actionable intelligence as well as proving data management capabilities for managing data - DataOps, is becoming paramount for the success of AI projects, particularly in Capital markets. This session will discuss the trends, solutions and benchmarks involved for successful AI imprementation for historic and realtime datasets.

The Storage Performance Development Kit (SPDK) provides a high-performance NVMe/TCP target that scales very well. As the SPDK NVMe/TCP target gains broader adoption, providing strong error detection using the data digest to protect the NVMe/TCP Protocol Data Units (NVMe/TCP PDUs) is very important. Can the SPDK community implement the data digest at high network throughputs efficiently?