Exploring Slot Assignment Methods in Flexible-Grid Optical Networks
Faculty Sponsor
Asegul Yayimli
College
Arts and Sciences
Discipline(s)
Computing and Information Sciences
ORCID Identifier(s)
https://orcid.org/0000-0003-4822-724X
Presentation Type
Poster Presentation
Symposium Date
Spring 5-3-2019
Abstract
Every day, large volumes of digital traffic are sent and received over networks. In this research, I focus on large-scale optical networks, which are networks that span cities that transmit data at light speed through waves of light on fiber optic cables. These light waves can be modulated to different frequencies, or slots, so that multiple connections can be sent along a single fiber at the same time. Methods for routing network traffic more efficiently are still being developed so that networks can handle more traffic. Dijkstra's shortest path algorithm is the standard for determining the shortest path for a connection to travel from its source to destination along the network. However, in a network prone to congestion, where every path is not guaranteed to be available, there are other decisions to be made. For example, there is no standard method for assigning a set of frequencies, or slots, to each connection. Simulation programs are helpful for studying the impacts of these decisions. I built and tested a simulation program that allows me to compare different slot assignment methods for dynamically-generated network traffic that is routed over large-scale optical networks. Performance of each slot assignment method is measured by comparing rates of rejected connections under different network scenarios.
Recommended Citation
Orr, Kimberly, "Exploring Slot Assignment Methods in Flexible-Grid Optical Networks" (2019). Symposium on Undergraduate Research and Creative Expression (SOURCE). 815.
https://scholar.valpo.edu/cus/815
Biographical Information about Author(s)
Kimberly Orr is a senior at Valparaiso University majoring in computer science and statistics. She enjoys using data to tackle interesting problems and gain insights about the world around her. After graduating this spring, Kimberly plans to work as a data scientist or software engineer in Austin Texas.