Chameleon
Update: This tool is no longer maintained and supported.
Chameleon
Chameleon is a new hybrid auto-scaling mechanism combining multiple different proactive methods coupled with a reactive fallback. Chameleon reconfigures the deployment of an application in a way that the supply of resources matches the current and estimated future demand for resources as closely as possible according to the definition of elasticity.
Links:
- The code is currently under construction. Chameleon source coud will be published soon
- Measurement data is available here
Mailing List
Publications
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Chamulteon: Coordinated Auto-Scaling of Micro-Services. in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
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Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. (2019, Juni).
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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. (2019, Juni).
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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.
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Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments. Thesis; University of Würzburg, Germany. (2018, Juli).
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On the Value of Service Demand Estimation for Auto-Scaling. in Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) (2018). (Bd. 10740) 142–156.
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Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
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Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. (2016, November).
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Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. (2016, Oktober).