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.
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Notes
- 1.
LibReDE: https://descartes.tools/librede/.
- 2.
Retailrocket Source: https://www.kaggle.com/retailrocket/ecommerce-dataset.
- 3.
Apache CloudStack: https://cloudstack.apache.org/.
- 4.
Citrix Netscaler: https://www.citrix.de/products/netscaler-adc/.
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Acknowledgements
This work was co-funded by the German Research Foundation (DFG) under grant No. (KO 3445/11-1) and by Google Inc. (Faculty Research Award).
Arif Merchant, Google Inc., contributed with helpful ideas and feedback.
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Bauer, A., Grohmann, J., Herbst, N., Kounev, S. (2018). On the Value of Service Demand Estimation for Auto-scaling. In: German, R., Hielscher, KS., Krieger, U. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2018. Lecture Notes in Computer Science(), vol 10740. Springer, Cham. https://doi.org/10.1007/978-3-319-74947-1_10
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