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

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices. key words: software-defined networking (SDN), network functions virtualisation (NFV), network virtualization, 5G, network slicing

Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing