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2019 | OriginalPaper | Buchkapitel

FaaStest - Machine Learning Based Cost and Performance FaaS Optimization

verfasst von : Shay Horovitz, Roei Amos, Ohad Baruch, Tomer Cohen, Tal Oyar, Afik Deri

Erschienen in: Economics of Grids, Clouds, Systems, and Services

Verlag: Springer International Publishing

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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.

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Titel: FaaStest - Machine Learning Based Cost and Performance FaaS Optimization
verfasst von: Shay Horovitz
Roei Amos
Ohad Baruch
Tomer Cohen
Tal Oyar
Afik Deri
Verlag: Springer International Publishing
Buch: Economics of Grids, Clouds, Systems, and Services
Print ISBN: 978-3-030-13341-2

Electronic ISBN: 978-3-030-13342-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-13342-9_15

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