| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
|
| Initial Amendment Date: | August 30, 2013 |
| Latest Amendment Date: | August 30, 2013 |
| Award Number: | 1321164 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Darleen Fisher
CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | October 1, 2013 |
| End Date: | September 30, 2017 (Estimated) |
| Total Intended Award Amount: | $319,999.00 |
| Total Awarded Amount to Date: | $319,999.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1664 N VIRGINIA ST # 285 RENO NV US 89557-0001 (775)784-4040 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
NV US 89557-0001 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
Networking Technology and Syst, EPSCoR Co-Funding |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
As the largest man-made complex network, the Internet grows with no central authority. Thousands of small and medium size Autonomous Systems connect individuals, businesses, universities, and agencies while focusing on optimizing their own communication efficiency and economic objectives. Each network is built by operators with different technical expertise and range a from small local organization network to a large transcontinental backbone. This integrated research and education plan aims to develop an Internet Topology Mapping System that will provide an underlay for improved communications in various application domains. The project attempts to provide significant improvements at multiple levels of Internet topology measurements and transforms the resulting measurement data into useful information for modeling and extracting knowledge from the Internet topology. This project: (i) builds upon novel ideas in approaching the challenges in large-scale topology data handling to capture Internet characteristics and dynamics, (ii) provides valuable longitudinal Internet topology measurements and a customizable graph indexing tool for large scale graph databases, (iii) integrates complex network theories to understand and suggest ways to improve the Internet backbone, and (iv) closely integrates K-12, college, and graduate education into its research activities.
Intellectual Merit: The motivation of the project is to develop a comprehensive system that will capture the Internet topology at fine granularity and periodically provide snapshots of the Internet backbone. The mapping system will then be utilized to investigate topological characteristics of the Internet and provide an underlay for applications to optimize their communications. Compared to the existing Internet topology measurement platforms, the system will (i) build Internet topology graphs with higher accuracy as the system integrates several mechanisms to efficiently handle large-scale measurement data; (ii) work at higher level of granularity by providing backbone topology maps at link layer; (iii) periodically release annotated network topologies in addition to the raw measurement data so that the community can utilize them in their experiments and optimize network communications; (iv) help in understanding Internet topology dynamics and providing network enhancements; and (v) provide a graph indexing tool to process and analyze large-scale networks.
Broader Impacts: Understanding the topological characteristics of the Internet is an important issue for various communities including the government, academia and industry. Network research community depends on such Internet mapping systems to understand characteristics of the Internet so that better protocols and services are developed. Moreover, new network paradigms such as cloud farms and content distribution networks require knowledge of the underlying networks. The mapping system will help these communities to conduct topography analysis and study large-scale characteristics of the Internet. The project integrates research to all levels of education including science projects, seminars, and summer camps for K-12 students and curriculum development and mentorship of college and graduate students. The services developed within the project will be available to researchers and practitioners. Similarly, the developed tools, data, and course material will be available to public via open-source distribution.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Even though communication protocols and networking devices have been analyzed in depth, the Internet as a whole has not been well characterized. Each AS builds its network for possibly different purposes, e.g., small local campus to large transcontinental backbone provider, based on local economic and technical objectives. Understanding the topological characteristics of the Internet is an important issue for various communities including the government, academia and industry. Network research community depends on such Internet mapping systems to understand characteristics of the Internet so that better protocols and services are developed. Moreover, new network paradigms such as cloud farms and content distribution networks require knowledge of the underlying networks. The developed mapping system will help these communities to conduct topography analysis and study large-scale characteristics of the Internet.
In this project, we developed a comprehensive mapping system that captures the Internet topology at fine granularity and periodically provides snapshots of the Internet backbone up to link layer. The system processes large scale data-sets collected from distributed vantage points in addition to data provided by public measurement platforms. The project also transforms the resulting measurement data into useful information for modeling and extracting knowledge from the Internet topology. Utilizing the data generated by the system, we performed in depth analysis of network path characteristics, individual AS topologies, and global Internet. We also developed a network topology model and simulator based on the measurements. The developed software, collected data and processed graphs are available at https://im.cse.unr.edu
Following tools and platforms were developed;
- Internet Measurement Platform
- Analytical Subnet and IP Alias Resolution (ASIAR) tool
- Unresponsive router resolution tool
- Autonomous System Mapper system
- Subnet-Oriented NEtwork Topology (SONET) generator
Following publications were produced as part of the research under this project;
- Muhammed Abdullah Canbaz, Khalid Bakhshaliyev, and Mehmet Hadi Gunes, Router Level Topologies of Autonomous Systems, International Conference on Complex Networks (CompleNet), Boston, MA, 5-8 Mar 2018
- Muhammed Abdullah Canbaz, Khalid Bakhshaliyev, and Mehmet Hadi Gunes, Analysis of Path Stability within Autonomous Systems, IEEE International Workshop on Measurements and Networking (M&N), Naples, Italy, 27-29 Sep 2017
- Ahmet Aksoy, Sushil Louis and Mehmet Hadi Gunes, OS Fingerprinting of Network Traffic using Genetic Algorithm and Machine Learning, IEEE Congress on Evolutionary Computation, Donostia - San Sebastián, Spain, June 5-8, 2017
- Ahmet Soran, Murat Yuksel, and Mehmet Hadi Gunes, Multiple Graph Abstractions for Parallel Routing over Virtual Topologies, Ninth IEEE International Workshop on Network Science for Communication Networks (NetSciCom 2017), Atlanta, GA, 1 May 2017
- Muhammed Abdullah Canbaz, Jay Thom, and Mehmet Hadi Gunes, Comparative Analysis of Internet Topology Datasets, 20th IEEE Global Internet Symposium, Atlanta, GA, 1 May 2017
- Ibrahim Ethem Coskun, Muhammed Abdullah Canbaz, and Mehmet Hadi Gunes, Efficient Network Topology Measurement Based on Ingress to Subnet Reachability, The 10th IEEE Workshop on Network Measurements, Dubai, United Arab Emirates, 7-10 November 2016
- Ahmet Aksoy and Mehmet Hadi Gunes, Operating System Classification Performance of TCP/IP Protocol Headers, The 10th IEEE Workshop on Network Measurements, Dubai, United Arab Emirates, 7-10 November 2016
- Ahmet Aksoy and Mehmet Hadi Gunes, SILEA: a System for Inductive LEArning, The 7th International Conference on Information, Intelligence, Systems and Applications (IISA 2016), Chalkidiki, Greece, 13-15 July, 2016
- Eric Klukovich, Mehmet Hadi Gunes, Lee Barford, and Frederick C. Harris, Jr., Accelerating BFS Shortest Paths Calculations Using CUDA for Internet Topology Measurements, High Performance Computing Simulation, Innsbruck, Austria, July 18 – 22, 2016
- Muhammed Abdullah Canbaz and Mehmet Hadi Gunes, Data-Driven Large Scale Network-Layer Internet Simulation, INFOCOM 2016 Student Workshop, San Francisco, CA, April 10-15, 2016
- Engin Arslan, Murat Yuksel, and Mehmet Hadi Gunes, Training Network Administrators in a Game-Like Environment, Journal of Network and Computer Applications, 53:14-23, 2015
- Hakan Kardes, Mehmet Hadi Gunes and Kamil Sarac, Graph Based Induction of Unresponsive Routers in Internet Topologies, Elsevier Computer Networks, 81:178-200, 2015
Following PhD Dissertation and MS thesis were conducted along the research of this project;
- Muhammed Abdullah Canbaz. Internet Topology Mining: From Big Data to Network Science. (2018). PhD Dissertation, University of Nevada, Reno.
- Jay Thom. Collection and Analysis of Internet Topology Data. (2017). MS Thesis, University of Nevada, Reno.
- Ibrahim Ethem Coskun. Efficient Large Scale Network Topology Measurement. (2015). MS Thesis, University of Nevada, Reno
- Mehmet Burak Akgun. Dual Layer Scale Free Network Topology Synthesis. (2014). PhD Dissertation, University of Nevada, Reno
Last Modified: 01/01/2018
Modified by: Mehmet H Gunes
Please report errors in award information by writing to: awardsearch@nsf.gov.