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oai:drops-oai.dagstuhl.de:8711 2024-03-06T11:05:02Z ddc:004 open_access

K8-Scalar: a workbench to compare autoscalers for container-orchestrated services (Artifact) Delnat, Wito Heyman, Thomas Joosen, Wouter Preuveneers, Davy Rafique, Ansar Truyen, Eddy Van Landuyt, Dimitri Container orchestration autoscalers experimentation exemplar This artifact is an easy-to-use and extensible workbench exemplar, named K8-Scalar, which allows researchers to implement and evaluate different self-adaptive approaches to autoscaling 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 associated scholarly paper presents (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, (iv) and validates the effectiveness of K8-scalar as a workbench for accurately comparing the performance of different auto-scaling strategies. Future work includes extending K8-Scalar with an improved research data management repository. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Wito Delnat and Thomas Heyman and Wouter Joosen and Davy Preuveneers and Ansar Rafique and Eddy Truyen and Dimitri Van Landuyt 2018 Is Part Of DARTS, Volume 4, Issue 1, Special Issue of the 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2018) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DARTS.4.1.2 urn:nbn:de:0030-drops-87118 https://drops.dagstuhl.de/entities/document/10.4230/DARTS.4.1.2 eng https://creativecommons.org/licenses/by/3.0/de/legalcode