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Autoscaling Performance Measurement Tool

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Published:02 April 2018Publication History

ABSTRACT

More companies are shifting focus to adding more layers of virtualization for their cloud applications thus increasing the flexibility in development, deployment and management of applications. Increase in the number of layers can result in additional overhead during autoscaling and also in coordination issues while layers may use the same resources while managed by different software. In order to capture these multilayered autoscaling performance issues, an Autoscaling Performance Measurement Tool (APMT) was developed. This tool evaluates the performance of cloud autoscaling solutions and combinations thereof for varying types of load patterns. In the paper, we highlight the architecture of the tool and its configuration. An autoscaling behavior for major IaaS providers with Kubernetes pods as the second layer of virtualization is illustrated using the data collected by APMT.

References

  1. Alexandros Evangelidis, David Parker, and Rami Bahsoon. 2017. Performance Modelling and Verification of Cloud-based Auto-Scaling Policies Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid '17). IEEE Press, Piscataway, NJ, USA, 355--364. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alexey Ilyushkin, Ahmed Ali-Eldin, Nikolas Herbst, Alessandro V. Papadopoulos, Bogdan Ghit, Dick Epema, and Alexandru Iosup. 2017. An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering (ICPE '17). ACM, New York, NY, USA, 75--86. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Autoscaling Performance Measurement Tool

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    • Published in

      cover image ACM Conferences
      ICPE '18: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering
      April 2018
      212 pages
      ISBN:9781450356299
      DOI:10.1145/3185768

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 April 2018

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