Chameleon

Update: This tool is no longer maintained and supported.

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

2019[ to top ]

Chamulteon: Coordinated Auto-Scaling of Micro-Services. A. Bauer; V. Lesch; L. Versluis; A. Ilyushkin; N. Herbst; S. Kounev; in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{BaLeVeIlHeKo2019-ICDCS-Chamulteon, author = {Bauer, Andr{\'e} and Lesch, Veronika and Versluis, Laurens and Ilyushkin, Alexey and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS)}, keywords = {SPEC}, month = {07}, title = {Chamulteon: Coordinated Auto-Scaling of Micro-Services}, year = 2019 }

%0 Conference Paper %1 BaLeVeIlHeKo2019-ICDCS-Chamulteon %A Bauer, André %A Lesch, Veronika %A Versluis, Laurens %A Ilyushkin, Alexey %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) %D 2019 %T Chamulteon: Coordinated Auto-Scaling of Micro-Services
Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. N. Herbst; (2019, Juni).
- [ BibTeX ]
- [ EndNote ]
@misc{talk-Herbst-GIAwardNomination-Dagstuhl, author = {Herbst, Nikolas}, howpublished = {GI Dissertationspreis: Seminar f{\"u}r Nominierte Doktoranden, Dagstuhl, Germany, June 28, 2019}, keywords = {t_bookchapter}, month = {06}, title = {{Methoden und Messverfahren f{\"u}r Automatisches Skalieren in Elastischen Cloud Umgebungen}}, year = 2019 }

%0 Generic %1 talk-Herbst-GIAwardNomination-Dagstuhl %A Herbst, Nikolas %D 2019 %T Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen
Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; (2019, Juni).
- [ BibTeX ]
- [ EndNote ]
@misc{talk-Bauer-HotCloudPerf2019, author = {Bauer, Andr{\'e}}, howpublished = {Talk at HotCloudperf 2019}, keywords = {Chameleon}, month = {06}, title = {{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}}, year = 2019 }

%0 Generic %1 talk-Bauer-HotCloudPerf2019 %A Bauer, André %D 2019 %T Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field
Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; N. Herbst; S. Spinner; A. Ali-Eldin; S. Kounev; in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.
- [ BibTeX ]
- [ EndNote ]
@article{BaHeSpAEKo2019-TPDS-ChameleonAutoscaler, author = {Bauer, Andr{\'e} and Herbst, Nikolas and Spinner, Simon and Ali-Eldin, Ahmed and Kounev, Samuel}, journal = {IEEE Transactions on Parallel and Distributed Systems}, keywords = {Self-adaptive-systems}, month = {04}, number = 4, pages = {800–813}, publisher = {IEEE}, title = {{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}}, volume = 30, year = 2019 }

%0 Journal Article %1 BaHeSpAEKo2019-TPDS-ChameleonAutoscaler %A Bauer, André %A Herbst, Nikolas %A Spinner, Simon %A Ali-Eldin, Ahmed %A Kounev, Samuel %D 2019 %I IEEE %J IEEE Transactions on Parallel and Distributed Systems %N 4 %P 800--813 %T Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field %V 30

2018[ to top ]

Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments. N. Herbst; Thesis; University of Würzburg, Germany. (2018, Juli).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@phdthesis{Herbst2018-phd, author = {Herbst, Nikolas}, keywords = {SPEC}, month = {07}, note = {<b>{Distinction, SPEC Kaivalya Dixit Distinguished Dissertation Award 2018}</b>}, school = {University of W{\"u}rzburg, Germany}, title = {Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments}, year = 2018 }

%0 Thesis %1 Herbst2018-phd %A Herbst, Nikolas %D 2018 %T Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/16431
On the Value of Service Demand Estimation for Auto-Scaling. A. Bauer; J. Grohmann; N. Herbst; S. Kounev; in Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) (2018). (Bd. 10740) 142–156.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
- [ DOI ]
In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. This is shown by testing two approaches in three different scenarios. Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. Our results encourage further research on the application of service demand estimates for resource management in data centers.

@inproceedings{BaGrHeKo2018-MMB-ServiceDemand, abstract = {In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. This is shown by testing two approaches in three different scenarios. Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. Our results encourage further research on the application of service demand estimates for resource management in data centers.}, address = {Cham}, author = {Bauer, Andr{\'e} and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018)}, keywords = {Virtualization}, month = {02}, pages = {142–156}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {{On the Value of Service Demand Estimation for Auto-Scaling}}, volume = 10740, year = 2018 }

%0 Conference Paper %1 BaGrHeKo2018-MMB-ServiceDemand %A Bauer, André %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) %C Cham %D 2018 %I Springer %P 142--156 %R 10.1007/978-3-319-74947-1_10 %T On the Value of Service Demand Estimation for Auto-Scaling %U https://doi.org/10.1007/978-3-319-74947-1_10 %V 10740 %X In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. This is shown by testing two approaches in three different scenarios. Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. Our results encourage further research on the application of service demand estimates for resource management in data centers. %@ 978-3-319-74947-1

2017[ to top ]

Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. A. Bauer; N. Herbst; S. Kounev; in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{BaHeKo2017-ICPE-Auto-Scaler-Tutorial, author = {Bauer, Andr{\'e} and Herbst, Nikolas and Kounev, Samuel}, booktitle = {{Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017)}}, keywords = {LIMBO}, month = {04}, title = {{Design and Evaluation of a Proactive, Application-Aware Auto-Scaler}}, year = 2017 }

%0 Conference Paper %1 BaHeKo2017-ICPE-Auto-Scaler-Tutorial %A Bauer, André %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) %D 2017 %T Design and Evaluation of a Proactive, Application-Aware Auto-Scaler

2016[ to top ]

Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, November).
- [ BibTeX ]
- [ EndNote ]
@misc{talk-Bauer-SSP2016, author = {Bauer, Andr{\'e}}, howpublished = {Experience Talk at SSP 2016}, keywords = {BUNGEE}, month = 11, title = {{Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism}}, year = 2016 }

%0 Generic %1 talk-Bauer-SSP2016 %A Bauer, André %D 2016 %T Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism
Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, Oktober).
- [ BibTeX ]
- [ EndNote ]
@misc{talk-Bauer-InvSpec16, author = {Bauer, Andr{\'e}}, howpublished = {Invited Talk, Spec Research - Cloud Working Group}, keywords = {Application-aware}, month = 10, title = {{Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism}}, year = 2016 }

%0 Generic %1 talk-Bauer-InvSpec16 %A Bauer, André %D 2016 %T Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism

Chameleon

Update: This tool is no longer maintained and supported.

Chameleon

Mailing List

Publications

Hinweis zum Datenschutz

Hinweis zum Datenschutz

Bildnachweise

Your E-mail address:
Your Name (optional):