NetGraf: An End-to-End Learning Network Monitoring Service

2021 ◽  
Author(s):  
Bashir Mohammed ◽  
Mariam Kiran ◽  
Bjoern Enders
Keyword(s):  
Network Monitoring ◽  
Learning Network ◽  
End To End ◽  
Monitoring Service

scholarly journals General Identifiability Condition for Network Topology Monitoring with Network Tomography

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4125 ◽  
Author(s):  
Shengli Pan ◽  
Zongwang Zhang ◽  
Zhiyong Zhang ◽  
Deze Zeng ◽  
Rui Xu ◽  
...  
Keyword(s):  
Network Topology ◽  
Network Monitoring ◽  
Time Varying ◽  
Network Tomography ◽  
Accurate Knowledge ◽  
Cross Traffic ◽  
End To End ◽  
The Cross

Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be “zoomed in or out” by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.

A deep learning network based end-to-end image composition

2021 ◽  
pp. 116570
Author(s):  
Xiaoyu Zhu ◽  
Haodi Wang ◽  
Zhiyi Zhang ◽  
Xiuping Wu ◽  
Junqi Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Composition ◽  
Learning Network ◽  
End To End ◽  
Deep Learning Network

scholarly journals End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network

2021 ◽  
Author(s):  
Sivaji Satrasupalli ◽  
Ebenezer Daniel ◽  
Sitaramanjaneya Reddy Guntur ◽  
Shaik Shehanaz
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Learning Network ◽  
End To End ◽  
Deep Learning Network

scholarly journals Multi-Domain Network Monitoring System for End-to-End Connectivity

2020 ◽  
Vol 9 (7) ◽  
pp. 1191-1195
Keyword(s):  
Network Management ◽  
Monitoring System ◽  
Web Application ◽  
Network Monitoring ◽  
Integrated Network ◽  
Message Queue ◽  
Remote Authentication ◽  
End To End ◽  
Network Technologies ◽  
Apache Tomcat

With the growing scale size of networks and the complexities that follow, there is an increasing need for an end-to-end network management system for efficient monitoring of the system and having the best-in-class security, performance, and management features. This paper proposes an integrated network monitoring system that provides a single-window operation for end-to-end network management. The system is operated as a web application for stand-alone functioning. This eliminates most the dependencies, chances of failure and additional requirements that may lead to increased cost and decreased performance. The proposed system uses JavaScript for frontend and DHTMLx3.0 for the User Interface. Backend works on Java, Ajax, Hibernate, Apache Tomcat (webserver), Apache ActiveMQ (message queue), MTOSI (Multi-Technology Operations System Interface), CORBA TMF 814 and WebSwing. The different network technologies used and supported by the system for functioning are RADIUS (Remote Authentication Dial-In User Service), MPLS-TP, SDH, SONET, ONT, and GPON

Satellite selection with an end-to-end deep learning network

GPS Solutions ◽  
2018 ◽  
Vol 22 (4) ◽  
Author(s):  
Panpan Huang ◽  
Chris Rizos ◽  
Craig Roberts
Keyword(s):  
Deep Learning ◽  
Learning Network ◽  
Satellite Selection ◽  
End To End ◽  
Deep Learning Network
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