Organizations are rapidly increasing their investment in AI infrastructure, but traditional storage systems struggle to keep pace with the demands of modern workloads. HPE addresses this challenge with a new disaggregated storage platform designed to support the scalability, performance, and accessibility required for AI and cloud-native applications.
Yet it's not always clear that everyone has grasped just how fundamental their storage choices are to the success of their AI initiatives.
As HPE's vice president of product management Sanjay Jagad explains"Storage has gone from a repository of data value to where value gets created through data." "That means you are going to now go do a forklift upgrade, or you have to do a rip and replace and do costly migrations," says Jagad. That all adds up to constant firefighting just to keep things stable, with an inevitable firestorm of upgrades every three to five years. This model was clumsy enough when it came to those legacy applications. It becomes even more problematic when it comes to modern applications, including AI systems.
The architecture has been designed to liberate"ownership" of application data from a specific controller. Because they are stateless, each controller can see every disk beneath it, which means no silos. As a disaggregated system, if admins want to add more performance or more capacity, new controllers or new media or both can be added directly. That echoes the sort of iterative, rapid deployment we see in cloud native or DevOps paradigms.
The B10000 was built to meet a range of deployment need across hybrid cloud – including on-prem, a software defined solution for the public cloud as well as a disconnected air-gapped option for highly regulated environments. That ties in with the third pillar of HPE's strategy, its HPE GreenLake cloud, which automates management of compute, networking and storage across those on prem, colo and cloud environments, with a cloud-like experience from a single console.
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