Keep-Alive Caching for the Hawkes process

@InProceedings{pmlr-v216-narayana23a,
  title = 	 {Keep-Alive Caching for the {H}awkes process},
  author =       {Narayana, Sushirdeep and Kash, Ian A.},
  booktitle = 	 {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {1499--1509},
  year = 	 {2023},
  editor = 	 {Evans, Robin J. and Shpitser, Ilya},
  volume = 	 {216},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {31 Jul--04 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v216/narayana23a/narayana23a.pdf},
  url = 	 {https://proceedings.mlr.press/v216/narayana23a.html},
  abstract = 	 {We study the design of caching policies in applications such as serverless computing where there is not a fixed size cache to be filled, but rather there is a cost associated with the time an item stays in the cache.  We present a model for such caching policies which captures the trade-off between this cost and the cost of cache misses. We characterize optimal caching policies in general and apply this characterization by deriving a closed form for Hawkes processes. Since optimal policies for Hawkes processes depend on the history of arrivals, we also develop history-independent policies which achieve near-optimal average performance. We evaluate the performances of the optimal policy and approximate polices using simulations and a data trace of Azure Functions, Microsoft’s FaaS (Function as a Service) platform for serverless computing}
}