Cloud-Native Databases: An Application Perspective

  • Josef Spillner
  • Giovanni Toffetti
  • Manuel Ramírez López
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 824)

Abstract

As cloud computing technologies evolve to better support hosted software applications, software development businesses are faced with a multitude of options to migrate to the cloud. A key concern is the management of data. Research on cloud-native applications has guided the construction of highly elastically scalable and resilient stateless applications, while there is no corresponding concept for cloud-native databases yet. In particular, it is not clear what the trade-offs between using self-managed database services as part of the application and provider-managed database services are. We contribute an overview about the available options, a testbed to compare the options in a systematic way, and an analysis of selected benchmark results produced during the cloud migration of a commercial document management application.

Notes

Acknowledgements

This research has been funded by the Swiss Commission for Technology and Innovation (CTI) in project ARKIS/18992.1. It has also been supported by an AWS in Education Research Grant, an IBM Academic Initiative for Cloud offer, a Microsoft Azure Research Award and a Google Cloud credit, all of which helped us to conduct our experiments on public commercial cloud environments.

References

  1. 1.
    Bagui, S., Nguyen, L.T.: Database sharding: to provide fault tolerance and scalability of big data on the cloud. Int. J. Cloud Appl. Comput. (IJCAC) 5(2), 36–52 (2015)Google Scholar
  2. 2.
    Brunner, S., Blöchlinger, M., Toffetti, G., Spillner, J., Bohnert, T.M.: Experimental evaluation of the cloud-native application design. In: 4th International Workshop on Clouds and (eScience) Applications Management (CloudAM), Limassol, Cyprus, December 2015Google Scholar
  3. 3.
    Costa, C.H., Maia, P.H.M., Mendonça, N.C., Rocha, L.S.: Supporting partial database migration to the cloud using non-intrusive software adaptations: an experience report. In: Celesti, A., Leitner, P. (eds.) ESOCC Workshops 2015. CCIS, vol. 567, pp. 238–248. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-33313-7_18CrossRefGoogle Scholar
  4. 4.
    Costa, C.M., Leite, C.R.M., Sousa, A.L.: Efficient SQL adaptive query processing in cloud databases systems. In: IEEE EAIS, pp. 114–121, Natal, Brazil, May 2016Google Scholar
  5. 5.
    Floratou, A., Patel, J.M., Lang, W., Halverson, A.: When free is not really free: what does it cost to run a database workload in the cloud? In: Nambiar, R., Poess, M. (eds.) TPCTC 2011. LNCS, vol. 7144, pp. 163–179. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-32627-1_12CrossRefGoogle Scholar
  6. 6.
    Frey, S., Hasselbring, W., Schnoor, B.: Automatic conformance checking for migrating software systems to cloud infrastructures and platforms. J. Softw. Evol. Proc. 25(10), 1089–1115 (2013)CrossRefGoogle Scholar
  7. 7.
    Goldschmidt, T., Jansen, A., Koziolek, H., Doppelhamer, J., Breivold, H.P.: Scalability and robustness of time-series databases for cloud-native monitoring of industrial processes. In: 7th IEEE International Conference on Cloud Computing (CLOUD). pp. 602–609, Anchorage, Alaska, USA, July 2014Google Scholar
  8. 8.
    Götz, S., Ilsche, T., Cardoso, J., Spillner, J., Kissinger, T., Aßmann, U., Lehner, W., Nagel, W.E., Schill, A.: Energy-efficient databases using sweet spot frequencies. In: 1st International Workshop on Green Cloud Computing (GCC), pp. 871–876, London, UK, December 2014Google Scholar
  9. 9.
    Mian, R., Martin, P., Zulkernine, F.H., Vázquez-Poletti, J.L.: Cost-effective resource configurations for multi-tenant database systems in public clouds. Int. J. Cloud Appl. Computing (IJCAC) 5(2), 1–22 (2015)Google Scholar
  10. 10.
    Nguyen, H., Shen, Z., Gu, X., Subbiah, S., Wilkes, J.: AGILE: elastic distributed resource scaling for infrastructure-as-a-service. In: 10th International Conference on Autonomic Computing (ICAC), San Jose, California, USA, pp. 69–82, June 2013Google Scholar
  11. 11.
    Sakr, S.: Cloud-hosted databases: technologies, challenges and opportunities. Cluster Comput. 17(2), 487–502 (2014)CrossRefGoogle Scholar
  12. 12.
    Seriatos, G., Kousiouris, G., Menychtas, A., Kyriazis, D., Varvarigou, T.: Comparison of database and workload types performance in cloud environments. In: Karydis, I., Sioutas, S., Triantafillou, P., Tsoumakos, D. (eds.) ALGOCLOUD 2015. LNCS, vol. 9511, pp. 138–150. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-29919-8_11CrossRefGoogle Scholar
  13. 13.
    Szczyrbowski, M., Myszor, D.: Comparison of the behaviour of local databases and databases located in the cloud. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015-2016. CCIS, vol. 613, pp. 253–261. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-34099-9_19CrossRefGoogle Scholar
  14. 14.
    Wiese, L.: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. DeGruyter/Oldenbourg, Berlin (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Service Prototyping Lab, School of EngineeringZurich University of Applied SciencesWinterthurSwitzerland

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