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 TCP (MPTCP) has been widely used as an efficient way forcommunication in many applications. Data centers, smartphones, and networkoperators use MPTCP to balance the traffic in a network efficiently. MPTCP isan extension of TCP (Transmission Control Protocol), which provides multiplepaths, leading to higher throughput and low latency. Although MPTCP has shownbetter performance than TCP in many applications, it has its own challenges.The network can become congested due to heavy traffic in the multiple paths(subflows) if the subflow rates are not determined correctly. Moreover,communication latency can occur if the packets are not scheduled correctlybetween the subflows. This paper reviews techniques to solve theabove-mentioned problems based on two main approaches; non data-driven(classical) and data-driven (Machine Learning) approaches. This paper comparesthese two approaches and highlights their strengths and weaknesses with a viewto motivating future researchers in this exciting area of machine learning forcommunications. This paper also provides details on the simulation of MPTCP andits implementations in real environments.

A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches