Keep-Alive Caching for the Hawkes process

Sushirdeep Narayana, Ian A. Kash
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1499-1509, 2023.

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

Cite this Paper

BibTeX
@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} }
Endnote
%0 Conference Paper %T Keep-Alive Caching for the Hawkes process %A Sushirdeep Narayana %A Ian A. Kash %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-narayana23a %I PMLR %P 1499--1509 %U https://proceedings.mlr.press/v216/narayana23a.html %V 216 %X 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
APA
Narayana, S. & Kash, I.A.. (2023). Keep-Alive Caching for the Hawkes process. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:1499-1509 Available from https://proceedings.mlr.press/v216/narayana23a.html.

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