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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Big data analytics frameworks (BDAFs) have been widely used for dataprocessing applications. These frameworks provide a large number ofconfiguration parameters to users, which leads to a tuning issue thatoverwhelms users. To address this issue, many automatic tuning approaches havebeen proposed. However, it remains a critical challenge to generate enoughsamples in a high-dimensional parameter space within a time constraint. In thispaper, we present AutoTune--an automatic parameter tuning system that aims tooptimize application execution time on BDAFs. AutoTune first constructs asmaller-scale testbed from the production system so that it can generate moresamples, and thus train a better prediction model, under a given timeconstraint. Furthermore, the AutoTune algorithm produces a set of samples thatcan provide a wide coverage over the high-dimensional parameter space, andsearches for more promising configurations using the trained prediction model.AutoTune is implemented and evaluated using the Spark framework and HiBenchbenchmark deployed on a public cloud. Extensive experimental results illustratethat AutoTune improves on default configurations by 63.70% on average, and onthe five state-of-the-art tuning algorithms by 6%-23%.

Learning-based Automatic Parameter Tuning for Big Data Analytics Frameworks