Sonstiges

The ever-expanding demands of data storage in today’s information-intensive landscape place a significant burden on IT professionals tasked with managing storage infrastructures.Manual processes for provisioning resources, troubleshooting issues, and performing routine maintenance can be time-consuming and resource-intensive, reducing operational efficiency and increasing the risk of human error. This IBM® Redpaper describes how to automate essential IBM FlashSystem® and IBM SAN Volume Controller tasks by using REST APIs,

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This IBM® Redpaper® Product Guide describes the evolution of IBM FlashCore Module (FCM) technology, provides a general overview of IBM FlashCore architectures, and explains how IBM continues to lead the industry in high-performance, low-latency enterprise storage technologies. IBM FlashCore Modules are used in modern IBM FlashSystem® solutions and combine advanced flash media, Non-Volatile Memory Express (NVMe) architectures, hardware-accelerated data reduction, intelligent flash management, and enterprise resiliency technologies within

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IBM Redbook:Just published.....

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This document presents IBM Storage Defender Sentinel as a comprehensive cyber‑resiliency solution that protects enterprise data by combining immutable storage snapshots with AI‑driven anomaly detection.It explains how the solution continuously creates and scans application‑consistent copies of data to identify ransomware, corruption, and other threats—even those that evade traditional defenses—and then enables rapid recovery by pinpointing the last known clean copy. It also outlines the architecture, workflows, and

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Executive Summary

As enterprises scale generative AI and agentic AI deployments, a critical infrastructure challenge is emerging:the rapid growth of inference state is outpacing GPU memory capacity, creating a bottleneck that directly impacts service quality and cost.Long-context workloads, including multi-turn assistants, retrieval-augmented generation (RAG) applications, and autonomous agent pipelines, generate large volumes of key-value (KV) cache data that must be retained across requests.When GPU memory is exhausted, inference platforms are forced to discard this cached context and recompute it from

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