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It is well known that storage sensor data on storage systems can detect abnormal symptoms that can lead to failures. With the abnormal sensor data and machine learning techniques, we can predict a storage component failure ahead of time and proactively remove it, before it can impact the remaining system or interrupt customer’s operations. A successful predictive maintenance model must make trade off in detection rate, false positive and lead time.

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The recent AI explosion is reshaping storage architectures in data centers, where GPU servers increasingly need to access vast amounts of data on network-attached disaggregated storage servers for more scalability and cost-effectiveness. However, conventional CPU-centric servers encounter critical performance and scalability challenges. First, the software mechanisms required to access remote storage over the network consume considerable CPU resources.

Citigroup Inc. analysts quote, "Enterprise data is expected to continue to grow at over 40% CAGR as AI becomes an incremental driver for data creation, storage, and data management."

Today's AI ecosystem require fundamental shifts in the requirements of every datacenter infrastructure component. The predominant AI infrastructure strategy tends to currently focus on the most drastically impactful infrastructure components, as in GPUs, CPUs and Memory.​

As data growth continues to explode, the need for secure, simple to manage, and cost-effective data archiving and backup solutions has become increasingly pressing. In response, IBM is proud to announce the launch of IBM S3 Deep Archive, a game-changing on-premises cloud solution that leverages S3 Glacier Flexible Retrieval storage classes to provide up to 27PB of data archiving at up to 80% savings than comparable cloud storage.

The SNIA TC AI Taskforce is working on a paper on AI workloads and the storage requirements for those workloads. This presentation will be an introduction to the key AI workloads with a description of how they use the data transports and storage systems. This is intended to be a foundational level presentation that will give participants a basic working knowledge of the subject.

Join us for an in-depth exploration of how Azure Blob Storage (Azure's object storage service) has innovated and scaled to meet the demands of supercomputer AI training efforts. Using OpenAI as a case study, we will dive into the internal architecture and new capabilities that enable blob storage to deliver tens of terabits per second (Tbps) of consistent throughput across a multi-exabyte (EiB) scale hierarchical namespace. Attendees will gain insights into workload patterns, best practices, and the implementation strategies that make such high performance possible.

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