Advertisement

Auto-Scaling in Data Stream Processing Applications: A Model-Based Reinforcement Learning Approach

  • Valeria Cardellini
  • Francesco Lo Presti
  • Matteo Nardelli
  • Gabriele Russo Russo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 825)

Abstract

By exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process huge volumes of data in a near real-time fashion. Adapting the application parallelism at run-time is critical in order to guarantee a proper level of QoS in face of varying workloads. In this paper, we consider Reinforcement Learning based techniques in order to self-configure the number of parallel instances for a single DSP operator. Specifically, we propose two model-based approaches and compare them to the baseline Q-learning algorithm. Our numerical investigations show that the proposed solutions provide better performance and faster convergence than the baseline.

Keywords

Data Stream Processing Elasticity Reinforcement Learning 

References

  1. 1.
    Cardellini, V., Lo Presti, F., Nardelli, M., Russo Russo, G.: Optimal operator deployment and replication for elastic distributed data stream processing. Concurr. Comput. 30(9), e4334 (2018).  https://doi.org/10.1002/cpe.4334CrossRefGoogle Scholar
  2. 2.
    De Matteis, T., Mencagli, G.: Elastic scaling for distributed latency-sensitive data stream operators. In: Proceedings of PDP 2017, pp. 61–68 (2017)Google Scholar
  3. 3.
    Fernandez, R.C., Migliavacca, M., Kalyvianaki, E., Pietzuch, P.: Integrating scale out and fault tolerance in stream processing using operator state management. In: Proceedings of ACM SIGMOD 2013, pp. 725–736 (2013)Google Scholar
  4. 4.
    Gedik, B., Schneider, S., Hirzel, M., Wu, K.L.: Elastic scaling for data stream processing. IEEE Trans. Parallel Distrib. Syst. 25(6), 1447–1463 (2014)CrossRefGoogle Scholar
  5. 5.
    Heinze, T., Pappalardo, V., Jerzak, Z., Fetzer, C.: Auto-scaling techniques for elastic data stream processing. In: Proceedings of IEEE ICDEW 2014, pp. 296–302 (2014).  https://doi.org/10.1109/ICDEW.2014.6818344
  6. 6.
    Heinze, T., Aniello, L., Querzoni, L., Jerzak, Z.: Cloud-based data stream processing. In: Proceedings of ACM DEBS 2014, pp. 238–245 (2014)Google Scholar
  7. 7.
    Hirzel, M., Soulé, R., Schneider, S., Gedik, B., Grimm, R.: A catalog of stream processing optimizations. ACM Comput. Surv. 46(4), 46:1–46:34 (2014)CrossRefGoogle Scholar
  8. 8.
    Lohrmann, B., Janacik, P., Kao, O.: Elastic stream processing with latency guarantees. In: Proceedings of IEEE ICDCS 2015, pp. 399–410 (2015)Google Scholar
  9. 9.
    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).  https://doi.org/10.1007/s10723-014-9314-7CrossRefGoogle Scholar
  10. 10.
    Mastronarde, N., van der Schaar, M.: Fast reinforcement learning for energy-efficient wireless communication. IEEE Trans. Signal Process. 59(12), 6262–6266 (2011).  https://doi.org/10.1109/TSP.2011.2165211MathSciNetCrossRefGoogle Scholar
  11. 11.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (2014)zbMATHGoogle Scholar
  12. 12.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  13. 13.
    Tesauro, G., Jong, N.K., Das, R., Bennani, M.N.: On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Comput. 10(3), 287–299 (2007).  https://doi.org/10.1007/s10586-007-0035-6CrossRefGoogle Scholar
  14. 14.
    Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992).  https://doi.org/10.1007/BF00992698CrossRefzbMATHGoogle Scholar
  15. 15.
    Yoon, K.P., Hwang, C.L.: Multiple Attribute Decision Making: An Introduction, vol. 104. Sage Publications, Thousand Oaks (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Valeria Cardellini
    • 1
  • Francesco Lo Presti
    • 1
  • Matteo Nardelli
    • 1
  • Gabriele Russo Russo
    • 1
  1. 1.Department of Civil Engineering and Computer Science EngineeringUniversity of Rome Tor VergataRomeItaly

Personalised recommendations