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 of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS)
Subtitle of host publicationWorkshop program
Number of pages11
Publication statusAccepted/In press - Jul 2019

    Research areas

  • cloud computing, auto-scaling, elasticity, workload forecasting, service demand estimation, container, benchmarking, metrics

ID: 53628705