Cost minimization for deploying serverless functions

Name: Cost minimization for deploying serverless functions
Author: Sedefoğlu, Ö., Sözer, Hasan

İsim	Cost minimization for deploying serverless functions
Yazar	Sedefoğlu, Ö., Sözer, Hasan
Basım Tarihi:	2021-03
Basım Yeri	- ACM
Konu	Cloud computing, Cost minimization, Function as a service, Industrial case study, Serverless computing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-145038104-8
Kayıt Numarası	083d9fea-5581-4300-a89e-28bb166a018e
Lokasyon	Computer Science
Tarih	2021-03
Örnek Metin	The costs of serverless functions increase proportional to the amount of memory reserved on the deployed server. However, increasing the amount of memory decreases the function execution time, which is also a factor that contributes to cost. We propose an automated approach for optimizing the amount of memory reserved for serverless functions. First, we measure the running time of a given function in various memory settings and derive a regression model. Then, we define an objective function and a set of constraints based on this regression model and the configuration space. Finally, we determine the optimal memory setting for minimizing cost. Our industrial case study shows that significant cost reductions can be achieved by accurate estimations of the impact of memory settings on runtime performance.
DOI	10.1145/3412841.3442069

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Cost minimization for deploying serverless functions

Yazar Sedefoğlu, Ö., Sözer, Hasan

Basım Tarihi 2021-03

Basım Yeri - ACM

Konu Cloud computing, Cost minimization, Function as a service, Industrial case study, Serverless computing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-145038104-8

Kayıt Numarası 083d9fea-5581-4300-a89e-28bb166a018e

Lokasyon Computer Science

Tarih 2021-03

Örnek Metin The costs of serverless functions increase proportional to the amount of memory reserved on the deployed server. However, increasing the amount of memory decreases the function execution time, which is also a factor that contributes to cost. We propose an automated approach for optimizing the amount of memory reserved for serverless functions. First, we measure the running time of a given function in various memory settings and derive a regression model. Then, we define an objective function and a set of constraints based on this regression model and the configuration space. Finally, we determine the optimal memory setting for minimizing cost. Our industrial case study shows that significant cost reductions can be achieved by accurate estimations of the impact of memory settings on runtime performance.

DOI 10.1145/3412841.3442069