@inproceedings{bc9ae25adcc04f3d97f8496655b3d534,
title = "FaaStest - Machine learning based cost and performance FaaS optimization",
abstract = "With the emergence of Function-as-a-Service (FaaS) in the cloud, pay-per-use pricing models became available along with the traditional fixed price model for VMs and increased the complexity of selecting the optimal platform for a given service. We present FaaStest - an autonomous solution for cost and performance optimization of FaaS services by taking a hybrid approach - learning the behavioral patterns of the service and dynamically selecting the optimal platform. Moreover, we combine a prediction based solution for reducing cold starts of FaaS services. Experiments present a reduction of over 50% in cost and over 90% in response time for FaaS calls.",
keywords = "Function as a service, Machine learning, Serverless",
author = "Shay Horovitz and Roei Amos and Ohad Baruch and Tomer Cohen and Tal Oyar and Afik Deri",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2018 ; Conference date: 18-09-2018 Through 20-09-2018",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-13342-9_15",
language = "English",
isbn = "9783030133412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "171--186",
editor = "Daniele D{\textquoteright}Agostino and J{\"o}rn Altmann and Massimo Coppola and Emanuele Carlini and Ba{\~n}ares, {Jos{\'e} {\'A}ngel}",
booktitle = "Economics of Grids, Clouds, Systems, and Services - 15th International Conference, GECON 2018, Proceedings",
address = "Germany",
}