In file systems, large sequential writes are more beneficial than small random writes, and hence many storage systems implement a log structured file system. In the same way, the cloud favors large objects more than small objects. Cloud providers place throttling limits on PUTs and GETs, and so it takes significantly longer time to upload a bunch of small objects than a large object of the aggregate size. Moreover, there are per-PUT calls associated with uploading smaller objects. In Netflix, a lot of media assets and their relevant metadata is generated and pushed to cloud.

Cloud storage footprint is in exabytes and exponentially growing and companies pay billions of dollars to store and retrieve data. In this talk, we will cover some of the space and time optimizations, which have historically been applied to on-premise file storage, and how they would be applied to objects stored in Cloud. Deduplication and compression are techniques that have been traditionally used to reduce the amount of storage used by applications.

Blockchain has revolutionized decentralized finance, and with smart-contracts has enabled the world of Non-Fungible Tokens, set to revolutionize industries such as art, collectibles and gaming. Blockchains, at the very core, are distributed chained hashes. They can be leveraged to store information in a decentralized, secure, encrypted, durable and available format. However, some of the challenges in Blockchain stem from the bloat of storage.

"There has been tremendous growth in the use of smartphones. Today, there are more than 130 million Android users in the world. Android smartphones leverage flash storage. Flash is great for supporting fast random I/O workloads but due to wear leveling, there is an impact on the life of the underlying flash device. There are strategies that can be used to improve the lifespan of flash storage on smartphones.

As Machine Learning continues to forge its way into diverse industries and applications, optimizing computational resources, particularly memory, has become a critical aspect of effective model deployment. This session, ""Memory Optimizations for Machine Learning,"" aims to offer an exhaustive look into the specific memory requirements in Machine Learning tasks and the cutting-edge strategies to minimize memory consumption efficiently.