BUNGEE
Update: This tool is no longer maintained and supported.
BUNGEE Cloud Elasticity Benchmark
BUNGEE is a Java based framework for benchmarking elasticity of IaaS cloud platforms. The tool automates the following benchmarking activities:
- A system analysis evaluates the load processing capabilities of the benchmarked platform at different scaling stages.
- The benchmark calibration uses the system analysis results and adjusts a given load intensity profile in a system specific manner.
- The measurement activity exposes the platform to a varying load according to the adjusted intensity profile.
- The elasticity evaluation measures the quality of the observed elastic behavior using a set of elasticity metrics.
At the moment, BUNGEE supports to analyse the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally.
Links:
- Download
- License
- Quick Start Guide (.pdf)
For more information, please contact Nikolas Herbst
The BUNGEE presentation at SEAMS2015 is available here.
Mailing List
Publications
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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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Systematic Search for Optimal Resource Configurations of Distributed Applications. in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 120–125.
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Integrating Docker into the BUNGEE cloud elasticity benchmark. Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).
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Quantifying Cloud Performance and Dependability: Taxonomy, Metric Design, and Emerging Challenges. in ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS) (2018). 3(4) 19:1–19:36.
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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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Elasticity Measurement in CaaS Environments - Extending the Existing BUNGEE Elasticity Benchmark to AWS’s Elastic Container Service. Thesis; Dublin Institute of Technology; Kevin Street, Dublin 2, D08 X622, Ireland. (2018, Juni).
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Extending BUNGEE Elasticity Benchmark for Multi-Tier Cloud Applications. (2018, April).
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FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds. in Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018) (2018).
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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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An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
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Metrics and Benchmarks for Self-Aware Computing Systems. in Self-Aware Computing Systems, S. Kounev, J. O. Kephart, A. Milenkoski, X. Zhu (Hrsg.) (2017).
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Modeling and Extracting Load Intensity Profiles. in ACM Transactions on Autonomous and Adaptive Systems (TAAS) (2017). 11(4) 23:1–23:28.
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Elasticity Benchmarking for Multi-Tier Cloud Applications. Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, Juni).
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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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Scalability Analysis of Cloud Software Services. in Companion Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017), Self Organizing Self Managing Clouds Workshop (SOSeMC 2017) (2017).
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Self-Aware Multidimensional Auto-Scaling. Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, September).
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Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. (2016, November).
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Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, September).
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Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics. (SPEC-RG-2016-01), (2016).
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BUNGEE: An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments. in Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015) (2015).
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Load Testing Elasticity and Performance Isolation in Shared Execution Environments. in Proceedings of the 4th International Workshop on Large-Scale Testing (LT 2015), co-located with the 6th ACM/SPEC International Conference on Performance Engineering (ICPE 2015) (2015).
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Quantitative Evaluation of Service Dependability in Shared Execution Environments (Extended Abstract of Keynote Talk). in Proceedings of the 11th International Conference on Quantitative Evaluation of SysTems (QEST 2014), Florence, Italy (2014).
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Resource Elasticity Benchmarking in Cloud Environments. Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2014, August).
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Towards a Resource Elasticity Benchmark for Cloud Environments. in Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability (HotTopiCS 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014). 5:1–5:8.
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Elasticity in Cloud Computing: What it is, and What it is Not. in Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013) (2013).