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short-paper

K8-scalar: a workbench to compare autoscalers for container-orchestrated database clusters

Authors:

Dimitri Van Landuyt,

Wouter JoosenAuthors Info & Claims

SEAMS '18: Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems

Pages 33 - 39

https://doi.org/10.1145/3194133.3194162

Published: 28 May 2018 Publication History

Abstract

Although a considerable amount of research exists on auto-scaling of database clusters, the design of an effective auto-scaling strategy requires fine-grained tailoring towards the specific application scenario.

This paper presents an easy-to-use and extensible workbench exemplar, named K8-Scalar (Kube-Scalar), which allows researchers to implement and evaluate different self-adaptive approaches to auto-scaling container-orchestrated services. The workbench is based on Docker, a popular technology for easing the deployment of containerized software that also has been positioned as an enabler for reproducible research. The workbench also relies on a container orchestration framework: Kubernetes (K8s), the de-facto industry standard for orchestration and monitoring of elastically scalable container-based services. Finally, it integrates and extends Scalar, a generic testbed for evaluating the scalability of large-scale systems with support for evaluating the performance of autoscalers for database clusters.

The paper discusses (i) the architecture and implementation of K8-Scalar and how a particular autoscaler can be plugged in, (ii) sketches the design of a Riemann-based autoscaler for database clusters, (iii) illustrates how to design, setup and analyze a series of experiments to configure and evaluate the performance of this autoscaler for a particular database (i.e., Cassandra) and a particular workload type, and (iv) validates the effectiveness of K8-Scalar as a workbench for accurately comparing the performance of different auto-scaling strategies.

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cover image ACM Conferences

SEAMS '18: Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems

May 2018

244 pages

ISBN:9781450357159

DOI:10.1145/3194133

General Chair:
Jesper Andersson
Linnaeus University, Sweden
,
Program Chair:
Danny Weyns
Katholieke Universiteit Leuven, Belgium | Linnaeus University, Sweden

Copyright © 2018 ACM.

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Published: 28 May 2018

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ICSE '18: 40th International Conference on Software Engineering

May 28 - 29, 2018

Gothenburg, Sweden

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Overall Acceptance Rate 17 of 31 submissions, 55%

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Cited By

Venkat Marella (2024)Comparative Analysis of Container Orchestration Platforms: Kubernetes vs. Docker SwarmInternational Journal of Scientific Research in Science and Technology10.32628/IJSRST2410525411:5(526-543)Online publication date: 31-Oct-2024
https://doi.org/10.32628/IJSRST24105254
Jeong JPremsankar GGhaddar BTarkoma S(2024)A robust optimization approach for placement of applications in edge computing considering latency uncertaintyOmega10.1016/j.omega.2024.103064126(103064)Online publication date: Jul-2024
https://doi.org/10.1016/j.omega.2024.103064
Truyen EXie HJoosen W(2023)Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud FederationsFuture Internet10.3390/fi1502006315:2(63)Online publication date: 1-Feb-2023
https://doi.org/10.3390/fi15020063
Wohlrab RCámara JGarlan DSchmerl B(2023)Explaining quality attribute tradeoffs in automated planning for self-adaptive systemsJournal of Systems and Software10.1016/j.jss.2022.111538198:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111538
Kruliš MBureš THnětynka PSchmerl BMaggio MCámara J(2022)SimdexProceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3524844.3528078(167-173)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.1145/3524844.3528078
Topfer MAbdullah MKrulis MBures THnetynka P(2022)ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOSC56246.2022.00033(66-69)Online publication date: Sep-2022
https://doi.org/10.1109/ACSOSC56246.2022.00033
Wu HCai ZLei YXu JBuyya R(2022)Adaptive processing rate based container provisioning for meshed Micro-services in Kubernetes CloudsCCF Transactions on High Performance Computing10.1007/s42514-022-00096-x4:2(165-181)Online publication date: 6-Apr-2022
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Baresi LQuattrocchi G(2020)A Simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications2020 IEEE International Conference on Web Services (ICWS)10.1109/ICWS49710.2020.00020(94-101)Online publication date: Oct-2020
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Raibulet CArcelli Fontana FCarettoni S(2020)A preliminary analysis of self-adaptive systems according to different issuesSoftware Quality Journal10.1007/s11219-020-09502-528:3(1213-1243)Online publication date: 1-Sep-2020
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Truyen EVan Landuyt DPreuveneers DLagaisse BJoosen W(2019)A Comprehensive Feature Comparison Study of Open-Source Container Orchestration FrameworksApplied Sciences10.3390/app90509319:5(931)Online publication date: 5-Mar-2019
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