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K8-Scalar: a workbench to compare autoscalers for container-orchestrated services (Artifact)

Authors Wito Delnat, Thomas Heyman, Wouter Joosen, Davy Preuveneers, Ansar Rafique, Eddy Truyen, Dimitri Van Landuyt



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DARTS.4.1.2.pdf
  • Filesize: 404 kB
  • 6 pages

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Author Details

Wito Delnat
Thomas Heyman
Wouter Joosen
Davy Preuveneers
Ansar Rafique
Eddy Truyen
Dimitri Van Landuyt

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Wito Delnat, Thomas Heyman, Wouter Joosen, Davy Preuveneers, Ansar Rafique, Eddy Truyen, and Dimitri Van Landuyt. K8-Scalar: a workbench to compare autoscalers for container-orchestrated services (Artifact). In Special Issue of the 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2018). Dagstuhl Artifacts Series (DARTS), Volume 4, Issue 1, pp. 2:1-2:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/DARTS.4.1.2

Artifact

Abstract

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.
Keywords
  • Container orchestration
  • autoscalers
  • experimentation exemplar

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References

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