Document Open Access Logo

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

PDF
Thumbnail PDF

Artifact Description

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

Document Identifiers

Author Details

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

Cite AsGet BibTex

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Cornel Barna, Hamzeh Khazaei, Marios Fokaefs, and Marin Litoiu. Delivering Elastic Containerized Cloud Applications to Enable DevOps. Proceedings - 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017, pages 65-75, 2017. URL: http://dx.doi.org/10.1109/SEAMS.2017.12.
  2. Carl Boettiger. An introduction to docker for reproducible research. SIGOPS Oper. Syst. Rev., 49(1):71-79, January 2015. URL: http://doi.acm.org/10.1145/2723872.2723882, URL: http://dx.doi.org/10.1145/2723872.2723882.
  3. Apache Cassandra. Understanding the architecture. URL: http://docs.datastax.com/en/cassandra/3.0/cassandra/architecture/archTOC.html, accessed 2018-01-29, 2018.
  4. Ioannis Konstantinou Dimitrios Tsoumakos and Christina Boumpouka. Automated, Elastic Resource Provisioning for NoSQL Clusters Using TIRAMOLA. IEEE, 2013. Conference paper at Delft, Netherlands. Google Scholar
  5. Docker. Docker engine. https://docs.docker.com/engine/, accessed 2018-01-24, 2018.
  6. Neil J. Gunther. Guerrilla Capacity Planning. Springer-Verlag, Heidelberg, Germany, 2007. Google Scholar
  7. Neil J. Gunther. A general theory of computational scalability based on rational functions. arXiv preprint arXiv:0808.1431, 2008. Google Scholar
  8. Thomas Heyman, Davy Preuveneers, and Wouter Joosen. Scalar: Systematic scalability analysis with the universal scalability law. In 2014 International Conference on Future Internet of Things and Cloud, pages 497-504, 8 2014. URL: https://lirias.kuleuven.be/handle/123456789/460752.
  9. Kubernetes. Heapster. URL: https://github.com/kubernetes/heapster/blob/master/docs/storage-schema.md, accessed 2018-01-26, 2017.
  10. Kubernetes. The kubernetes package manager. URL: https://github.com/kubernetes/helm, accessed 2018-01-26, 2018.
  11. Kubernetes. Production-grade container orchestration. URL: https://kubernetes.io/, accessed 2018-01-23, 2018.
  12. Kubernetes. Running kubernetes locally via minikube. URL: https://kubernetes.io/docs/getting-started-guides/minikube/, accessed 2018-03-16, 2018.
  13. Kubernetes. StatefulSets. https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/, 2018. Accessed: January 23 2018.
  14. Kubernetes. Using kubeadm to Create a Cluster. URL: https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/, accessed 2018-01-29, 2018.
  15. John DC Little and Stephen C Graves. Little’s law. In Building intuition, pages 81-100. Springer, 2008. Google Scholar
  16. Ron Miller. 36 companies agree to a kubernetes certification standard. https://techcrunch.com/2017/11/13/the-cncf-just-got-36-companies-to-agree-to-a-kubernetes-certification-standard/, 2017.
  17. Ansar Rafique, Dimitri Van Landuyt, and Wouter Joosen. Persist: Policy-based data management middleware for multi-tenant saas leveraging federated cloud storage. Journal of Grid Computing, Mar 2018. URL: https://doi.org/10.1007/s10723-018-9434-6, URL: http://dx.doi.org/10.1007/s10723-018-9434-6.
  18. Riemann. Riemann monitors distributed systems. URL: http://riemann.io/, accessed 2017-08-08, 2017.
  19. Prateek Sharma, Lucas Chaufournier, Prashant Shenoy, and Y C Tay. Containers and Virtual Machines at Scale: A Comparative Study. In Proceedings of the 17th International Middleware Conference, Middleware '16, pages 1:1 - -1:13, New York, NY, USA, 2016. ACM. URL: http://doi.acm.org/10.1145/2988336.2988337, URL: http://dx.doi.org/10.1145/2988336.2988337.
  20. Macro Netto et al. Vincent Emeakaroha. Towards autonomic detection of SLA violations in Cloud infrastructures. Future Generation Computer Systems, 2012. CVolume 28, Issue 7, July 2012, Pages 1017-1029. Google Scholar
  21. Miguel Gomes Xavier, Marcelo Veiga Neves, and Cesar Augusto Fonticielha De Rose. A Performance Comparison of Container-Based Virtualization Systems for MapReduce Clusters. 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pages 299-306, 2014. URL: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6787290, URL: http://dx.doi.org/10.1109/PDP.2014.78.
  22. L. Zhao, S. Sakr, and A. Liu. A framework for consumer-centric sla management of cloud-hosted databases. IEEE Transactions on Services Computing, 8(4):534-549, July 2015. URL: http://dx.doi.org/10.1109/TSC.2013.5.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact