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

The web serving protocol stack is constantly changing and evolving to tackletechnological shifts in networking infrastructure and website complexity. As aresult of this evolution, the web serving stack includes a plethora ofprotocols and configuration parameters that enable the web serving stack toaddress a variety of realistic network conditions. Yet, today, most contentproviders have adopted a "one-size-fits-all" approach to configuring thenetworking stack of their user facing web servers (or at best employ moderatetuning), despite the significant diversity in end-user networks and devices. Inthis paper, we revisit this problem and ask a more fundamental question: Arethere benefits to tuning the network stack? If so, what system design choicesand algorithmic ensembles are required to enable modern content provider todynamically and flexibly tune their protocol stacks. We demonstrate throughsubstantial empirical evidence that this "one-size-fits-all" approach resultsin sub-optimal performance and argue for a novel framework that extendsexisting CDN architectures to provide programmatic control over theconfiguration options of the CDN serving stack. We designed ConfigTron adata-driven framework that leverages data from all connections to identifytheir network characteristics and learn the optimal configuration parameters toimprove end-user performance. ConfigTron uses contextual multi-arm bandit-basedlearning algorithm to find optimal configurations in minimal time, enabling acontent providers to systematically explore heterogeneous configurations whileimproving end-user page load time by as much as 19% (upto 750ms) on median.

ConfigTron: Tackling network diversity with heterogeneous configurations