Volume 11, Number 18, November 2021

LFO₂: An Enhanced Version of Learning-From-OPT Caching Algorithm

  Authors

Yipkei Kwok and David L. Sullivan, Missouri Western State University, USA

  Abstract

Recent machine learning-based caching algorithm have shown promise. Among them, Learning-FromOPT (LFO) is the state-of-the-art supervised learning caching algorithm. LFO has a parameter named Window Size, which defines how often the algorithm generates a new machine-learning model. While using a small window size allows the algorithm to be more adaptive to changes in request behaviors, experimenting with LFO revealed that the performance of LFO suffers dramatically with small window sizes. This paper proposes LFO2, an improved LFO algorithm, which achieves high object hit ratios (OHR) with small window sizes. This results show a 9% OHR increase with LFO2. As the next step, the machine-learning parameters will be investigated for tuning opportunities to further enhance performance.

  Keywords

Content Delivery Network, Cache, Machine Learning, Supervised learning.