Publication Date
Fall 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Navrati Saxena
Third Advisor
William B. Andreopoulos
Keywords
Named Data Networking, NDN Caching, Machine Learning.
Abstract
Named Data Networking (NDN) has a built-in caching capability that is enabled with the help of its Content Store. Caching in NDN has several benefits, such as reducing overhead on the producer side, avoiding a single point of failure, and reducing network load. The primary caching policy of the NDN architecture is to leave copies everywhere. However, this scheme induces significant cache redundancy. Existing advanced cache techniques either periodically share the entire list of cached content at a node or make a caching decision without knowing the cached content at other nodes in the network. We propose an intelligent cache policy that estimates cache distribution and topology through a collaborative network of NDN routers, guided by Interest and Data packets. Additionally, we develop an NDN simulator to facilitate the testing and evaluation of our machine learning caching strategy. This paper details the cache policy, the design and functionality of our NDN simulator, and the experiments comparing our approach to baseline caching strategies.
Recommended Citation
Shah, Deep Pradipbhai, "Intelligent Caching in Named Data Networking" (2024). Master's Projects. 1431.
https://scholarworks.sjsu.edu/etd_projects/1431
