Harnessing machine learning to make managing your storage less of a chore

As far as we know, none of the storage vendors using AI have gone <a href='https://arstechnica.com/science/2019/07/brains-scale-better-than-cpus-so-intel-is-building-brains/'>neuromorphic</a> yet—let alone biological.

Enlarge / As far as we know, none of the storage vendors using AI have gone neuromorphic yet—let alone biological. (credit: Aurich Lawson / Getty)

While the words “artificial intelligence” generally conjure up visions of Skynet, HAL 9000, and the Demon Seed, machine learning and other types of AI technology have already been brought to bear on many analytical tasks, doing things that humans can’t or don’t want to do—from catching malware to predicting when jet engines need repair. Now it’s getting attention for another seemingly impossible task for humans: properly configuring data storage.

As the scale and complexity of storage workloads increase, it becomes more and more difficult to manage them efficiently. Jobs that could originally be planned and managed by a single storage architect now require increasingly large teams of specialists—which sets the stage for artificial intelligence (née machine learning) techniques to enter the picture, allowing fewer storage engineers to effectively manage larger and more diverse workloads.

Storage administrators have five major metrics they contend with, and finding a balance among them to match application demands approaches being a dark art. Those metrics are:

Read 26 remaining paragraphs | Comments

Features – Ars Technica

Leave a Reply

Your email address will not be published. Required fields are marked *

*