Research Article
Model-Based and Machine Learning Approaches for Designing Caching and Routing Algorithms
@INPROCEEDINGS{10.1007/978-3-030-67369-7_2, author={Adita Kulkarni and Anand Seetharam}, title={Model-Based and Machine Learning Approaches for Designing Caching and Routing Algorithms}, proceedings={Ad Hoc Networks. 12th EAI International Conference, ADHOCNETS 2020, Paris, France, November 17, 2020, Proceedings}, proceedings_a={ADHOCNETS}, year={2021}, month={1}, keywords={Caching Routing}, doi={10.1007/978-3-030-67369-7_2} }- Adita Kulkarni
Anand Seetharam
Year: 2021
Model-Based and Machine Learning Approaches for Designing Caching and Routing Algorithms
ADHOCNETS
Springer
DOI: 10.1007/978-3-030-67369-7_2
Abstract
In this paper, we compare and contrast model-based and machine learning approaches for designing caching and routing strategies to improve cache network performance (e.g., delay, hit rate). We first outline the key principles used in the design of model-based strategies and discuss the analytical results and bounds obtained for these approaches. By conducting experiments on real-world traces and networks, we identify the interplay between content popularity skewness and request stream correlation as an important factor affecting cache performance. With respect to routing, we show that the main factors impacting performance are alternate path routing and content search. We then discuss the applicability of multiple machine learning models, specifically reinforcement learning, deep learning, transfer learning and probabilistic graphical models for the caching and routing problem.
