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

Multipath transport protocols enable the concurrent use of different networkpaths, benefiting a fast and reliable data transmission. The scheduler of amultipath transport protocol determines how to distribute data packets overdifferent paths. Existing multipath schedulers either conform to predefinedpolicies or to online trained policies. The adoption of millimeter wave(mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks(WLANs) introduces time-varying network conditions, under which the existingschedulers struggle to achieve fast and accurate adaptation. In this paper, wepropose FALCON, a learning-based multipath scheduler that can adapt fast andaccurately to time-varying network conditions. FALCON builds on the idea ofmeta-learning where offline learning is used to create a set of meta-modelsthat represent coarse-grained network conditions, and online learning is usedto bootstrap a specific model for the current fine-grained network conditionstowards deriving the scheduling policy to deal with such conditions. Usingtrace-driven emulation experiments, we demonstrate FALCON outperforms the beststate-of-the-art scheduler by up to 19.3% and 23.6% in static and mobilenetworks, respectively. Furthermore, we show FALCON is quite flexible to workwith different types of applications such as bulk transfer and web services.Moreover, we observe FALCON has a much faster adaptation time compared to allthe other learning-based schedulers, reaching almost an 8-fold speedup comparedto the best of them. Finally, we have validated the emulation results inreal-world settings illustrating that FALCON adapts well to the dynamicity ofreal networks, consistently outperforming all other schedulers.

FALCON: Fast and Accurate Multipath Scheduling using Offline and Online Learning