Machine Learning on Named Data Network: A survey Routing and Forwarding Strategy

被引：6

作者：

Mayasari, Ratna ^{[1
,2
]}

Syambas, Nana Rachmana ^{[1
]}

机构：

[1] Bandung Inst Technol, Sch Elect Engn & Informat, Bandung, Indonesia

[2] Telkom Univ, Sch Elect Engn, Kota Bandung, Jawa Barat, Indonesia

来源：

PROCEEDING OF 14TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATION SYSTEMS, SERVICES, AND APPLICATIONS (TSSA) | 2020年

关键词：

NDN; Machine Learning; Routing; Forwarding; Deep Learning;

D O I：

10.1109/tssa51342.2020.9310909

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

NDN, a data-centric network which can reduce the load on the network (especially on the server side) is widely developed using Machine Learning (ML) recently. The main reason is the capability of ML to examine a huge data, for example in FIB. In FIB, to identify the longest prefix is expensive and hard to operate without precise optimization. The goal of this study is to serve as guidelines for future research by conducting surveys on current NDN development with ML, especially for routing and forwarding classification.

引用

页数：5

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