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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- 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/.
References
Lazowska, E.D., Zahorjan, J., Graham, G.S., Sevcik, K.C.: Quantitative System Performance: Computer System Analysis Using Queueing Network Models. Prentice-Hall, Inc., Upper Saddle River (1984)
Menascé, D.A., Dowdy, L.W., Almeida, V.A.F.: Performance by Design: Computer Capacity Planning by Example. Prentice Hall PTR, Upper Saddle River (2004)
Spinner, S., Casale, G., Brosig, F., Kounev, S.: Evaluating approaches to resource demand estimation. Perform. Eval. 92, 51–71 (2015)
Willnecker, F., Dlugi, M., Brunnert, A., Spinner, S., Kounev, S., Gottesheim, W., Krcmar, H.: Comparing the accuracy of resource demand measurement and estimation techniques. In: Beltrán, M., Knottenbelt, W., Bradley, J. (eds.) EPEW 2015. LNCS, vol. 9272, pp. 115–129. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23267-6_8
Rolia, J., Vetland, V.: Parameter estimation for performance models of distributed application systems. In: CASCON 1995, p. 54. IBM Press (1995)
Brosig, F., Kounev, S., Krogmann, K.: Automated extraction of palladio component models from running enterprise java applications. In: VALUETOOLS 2009, pp. 1–10 (2009)
Wang, W., et al.: Application-level CPU consumption estimation: towards performance isolation of multi-tenancy web applications. In: IEEE CLOUD 2012, pp. 439–446, June 2012
Zheng, T., Woodside, C., Litoiu, M.: Performance model estimation and tracking using optimal filters. IEEE TSE 34(3), 391–406 (2008)
Spinner, S., Casale, G., Zhu, X., Kounev, S.: Librede: a library for resource demand estimation. In: ACM/SPEC ICPE 2014, pp. 227–228. ACM, New York (2014)
Grohmann, J., Herbst, N., Spinner, S., Kounev, S.: Self-tuning resource demand estimation. In: Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017), July 2017
Bolch, G., et al.: Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications. John Wiley & Sons, New York (2006)
Bunch, J.R., Hopcroft, J.E.: Triangular factorization and inversion by fast matrix multiplication. Math. Comput. 28(125), 231–236 (1974)
Herbst, N., Kounev, S., Weber, A., Groenda, H.: BUNGEE: an elasticity benchmark for self-adaptive IaaS cloud environments. In: SEAMS 2015, pp. 46–56. IEEE Press (2015)
Herbst, N., et al.: Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics. CoRR abs/1604.03470 (2016)
Iosup, A., Yigitbasi, N., Epema, D.: On the performance variability of production cloud services. In: CCGrid 2011, pp. 104–113 (2011)
Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manag. 23(3), 567–619 (2015)
Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)
Han, R., Guo, L., et al.: Lightweight resource scaling for cloud applications. In: IEEE/ACM CCGrid 2012, pp. 644–651. IEEE (2012)
Maurer, M., Brandic, I., Sakellariou, R.: Enacting SLAs in clouds using rules. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 455–466. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23400-2_42
Urgaonkar, B., et al.: Agile dynamic provisioning of multi-tier internet applications. ACM TAAS 3(1), 1 (2008)
Zhang, Q., Cherkasova, L., Smirni, E.: A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: IEEE ICAC 2007, p. 27. IEEE (2007)
Kalyvianaki, E., Charalambous, T., Hand, S.: Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters. In: ACM ICAC 2009, pp. 117–126. ACM (2009)
Ali-Eldin, A., Tordsson, J., Elmroth, E.: An adaptive hybrid elasticity controller for cloud infrastructures. In: IEEE NOMS 2012, pp. 204–212. IEEE (2012)
Tesauro, G., Jong, N.K., Das, R., Bennani, M.N.: A hybrid reinforcement learning approach to autonomic resource allocation. In: IEEE ICAC 2006, pp. 65–73. IEEE (2006)
Rao, J., et al.: VCONF: a reinforcement learning approach to virtual machines auto-configuration. In: ACM ICAC 2009, pp. 137–146. ACM (2009)
Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Futur. Gener. Comput. Syst. 27(6), 871–879 (2011)
Chen, G., et al.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: NSDI, vol. 8, pp. 337–350 (2008)
Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: Cloudscale: elastic resource scaling for multi-tenant cloud systems. In: ACM Symposium on Cloud Computing. ACM (2011)
Nguyen, H., et al.: Agile: elastic distributed resource scaling for infrastructure-as-a-service. In: ICAC, vol. 13, pp. 69–82 (2013)
Spinner, S., et al.: Runtime vertical scaling of virtualized applications via online model estimation. In: IEEE SASO 2014, pp. 157–166. IEEE (2014)
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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