Artificial Intelligence - SNIA on Data, Networking & Storage

What types of storage are needed for different aspects of AI? That was one of the many topics covered in our SNIA Networking Storage Forum (NSF) webcast “Storage for AI Applications.” It was a fascinating discussion and I encourage you to check it out on-demand. Our panel of experts answered many questions during the live roundtable Q&A. Here are answers to those questions, as well as the ones we didn’t have time to address.

Q. What are the different data set sizes and workloads in AI/ML in terms of data set size, sequential/ random, write/read mix?

A. Data sets will vary incredibly from use case to use case. They may be GBs to possibly 100s of PB. In general, the workloads are very heavily reads maybe 95%+. While it would be better to have sequential reads, in general the patterns tend to be closer to random. In addition, different use cases will have very different data sizes. Some may be GBs large, while others may be <1 KB. The different sizes have a direct impact on performance in storage and may change how you decide to store the data.

Everyone enjoys having storage that is fast, reliable, scalable, and affordable. But it turns out different applications have different storage needs in terms of I/O requirements, capacity, data sharing, and security. Some need local storage, some need a centralized storage array, and others need distributed storage—which itself could be local or networked. One application might excel with block storage while another with file or object storage. For example, an OLTP database might require small amounts of very fast flash storage; a media or streaming application might need vast quantities of inexpensive disk storage with extra security safeguards; while a third application might require a mix of different storage tiers with multiple servers sharing the same data. This SNIA Networking Storage Forum “Storage for Applications” webcast series will cover the storage requirements for specific uses such as artificial intelligence (AI), database, cloud, media & entertainment, automotive, edge, and more. With limited resources, it’s important to understand the storage intent of the applications in order to choose the right storage and storage networking strategy, rather than discovering the hard way that you’ve chosen the wrong solution for your application.

Tag: Artificial Intelligence

Storage for AI Q&A

Storage for Applications Webcast Series