RDOF: deployment optimization for function as a service
File(s)
Author(s)
Zhu, Lulai
Giotis, G
Tountopoulos, V
Casale, Giuliano
Type
Conference Paper
Abstract
Function as a service (FaaS) simplifies the runtime
resource management of cloud applications and enables finegrained scaling and billing at the function level, thus becoming
the most widespread serverless paradigm today. Cost-effective
use of FaaS entails appropriately deploying individual functions.
We propose in this paper RDOF1
, a model-driven approach to
deployment optimization for FaaS. RDOF predicts the performance of a FaaS-based application by instantiating a layered
queueing network and finds the optimal configuration of each
function such that the total operating cost is minimized under the
specified performance requirements. We have validated RDOF on
Amazon Web Services (AWS) and implemented it in an online
tool that operates on TOSCA metamodels.
resource management of cloud applications and enables finegrained scaling and billing at the function level, thus becoming
the most widespread serverless paradigm today. Cost-effective
use of FaaS entails appropriately deploying individual functions.
We propose in this paper RDOF1
, a model-driven approach to
deployment optimization for FaaS. RDOF predicts the performance of a FaaS-based application by instantiating a layered
queueing network and finds the optimal configuration of each
function such that the total operating cost is minimized under the
specified performance requirements. We have validated RDOF on
Amazon Web Services (AWS) and implemented it in an online
tool that operates on TOSCA metamodels.
Date Issued
2021-11-13
Date Acceptance
2021-07-03
Citation
2021, pp.508-514
Publisher
IEEE
Start Page
508
End Page
514
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/document/9582257
Grant Number
825040
Source
IEEE CLOUD 2021
Subjects
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
deployment optimization
function as a service
layered queueing networks
TOSCA
Publication Status
Published
Start Date
2021-09-05
Finish Date
2021-09-10
Coverage Spatial
Online virtual
Date Publish Online
2021-11-13