Chamulteon: Coordinated Auto-Scaling of Micro-Services

André Bauer, Veronika Lesch, Laurens Versluis, Alexey Ilyushkin, Nikolas Herbst, Samuel Kounev

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

23 Citations (Scopus)
280 Downloads (Pure)


Nowadays, in order to keep track of the fast-changing requirements of Internet applications, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources to the resource demand. The straightforward approach is to deploy a set of common and opensource single-service auto-scalers for each service independently. However, this deployment leads to problems such as bottleneckshifting and increased oscillations. Existing auto-scalers that scale applications consisting of multiple services are kept closed-source. To face these challenges, we first survey existing auto-scalers and highlight current challenges. Then, we introduce Chamulteon, a redesign of our previously introduced mechanism, which can scale applications consisting of multiple services in a coordinated manner. We evaluate Chamulteon against four different wellcited auto-scalers in four sets of measurement-based experiments where we use diverse environments (VM vs. Docker), real-world traces, and vary the scale of the demanded resources. Overall, Chamulteon achieves the best auto-scaling performance based on established user-oriented and endorsed elasticity metrics.
Original languageEnglish
Title of host publicationProceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
Subtitle of host publicationWorkshop program
EditorsJ.E. Guerrero
Place of PublicationPiscataway
Number of pages11
ISBN (Electronic)978-1-7281-2519-0
ISBN (Print)978-1-7281-2520-6
Publication statusPublished - 2019
EventICDCS: The 2019 39th IEEE International Conference on Distributed Computing Systems - Richardson, United States
Duration: 7 Jul 20199 Jul 2019

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Country/TerritoryUnited States


  • Auto-Scaling
  • Benchmarking
  • Cloud Computing
  • Container
  • Elasticity
  • Metrics
  • Service Demand Estimation
  • Workload Forecasting

Cite this