Towards a cognitive network management and control system

Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with heterogeneous session sizes and strict time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the network management and control system, require timely and judicious collection of network state information. Within this context we will focus on the problem of shortest path routing, and identify pragmatic schemes that allow a central controller to collect relevant delay statistics from various links and nodes within the network. We present Significant Sampling as an adaptive monitoring technique to collect and disseminate network state information when it can be of significant value to the optimal operation of the network, and in particular when it can help in identifying the shortest routes.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Rezaee, Arman.

Advisor dc:contributor.advisor

• Vincent W.S. Chan.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128329

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128329