English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper explores a prevailing trend in the industry: migratingdata-intensive analytics applications from on-premises to cloud-nativeenvironments. We find that the unique cost models associated with cloud-basedstorage necessitate a more nuanced understanding of optimizing performance.Specifically, based on traces collected from Uber's Presto fleet in production,we argue that common I/O optimizations, such as table scan and filter, andbroadcast join, may lead to unexpected costs when naively applied in the cloud.This is because traditional I/O optimizations mainly focus on improvingthroughput or latency in on-premises settings, without taking into account themonetary costs associated with storage API calls. In cloud environments, thesecosts can be significant, potentially involving billions of API calls per dayjust for Presto workloads at Uber scale. Presented as a case study, this paperserves as a starting point for further research to design efficient I/Ostrategies specifically tailored for data-intensive applications in cloudsettings.

Rethinking the Cloudonomics of Efficient I/O for Data-Intensive Analytics Applications