Chamulteon
Coordinated Auto-Scaling of Micro-Services
André Bauer (Julius-Maximilians-Universität Würzburg)
Veronika Lesch (Julius-Maximilians-Universität Würzburg)
Laurens Versluis (Vrije Universiteit Amsterdam)
Alexey Ilyushkin (TU Delft - Data-Intensive Systems)
Nikolas Herbst (Julius-Maximilians-Universität Würzburg)
Samuel Kounev (Julius-Maximilians-Universität Würzburg)
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Abstract
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.