Skip to main content
Springer Nature Link
Log in

Cloud storage availability and performance assessment: a study based on NoSQL DBMS

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cloud storage systems are increasingly adopting NoSQL database management systems (DBMS), since they generally provide superior availability and performance than traditional DBMSs. To the detriment of better consistency guarantees, several NoSQL DBMSs allow eventual consistency, in which an operation is confirmed without checking all nodes. Different consistency levels for an operation (e.g. read) can be adopted, and such levels may distinctly affect system behaviour. Thus, the assessment of a system design taking into account distinct consistency levels is important for developing cloud storage systems. This work proposes an approach based on reliability block diagrams and generalized stochastic Petri nets to evaluate availability and performance of cloud storage systems, considering redundant nodes and eventual consistency based on NoSQL DBMS. Experimental results demonstrate system configuration may influence unavailability from 1 s to 21 h in a year, and performance can be impacted by up to 17.9%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://cassandra.apache.org/.

References

  1. Astrova I, Koschel A, Eickemeyer C et al (2017) Dbaas comparison: Amazon vs. microsoft. In: 2017 International Conference on Information Society, pp 15–21

  2. Astrova I, Koschel A, Eickemeyer C et al (2018) Comparison of dbaas architectures. In: 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), IEEE, pp 1–5

  3. Bailis P, Venkataraman S, Franklin MJ et al (2014) Quantifying eventual consistency with PBS. VLDB J 23(2):279–302

    Article  Google Scholar 

  4. Balbo G (2001) Introduction to stochastic petri nets. In: Brinksma E, Hermanns H, Katoen J-P (eds). Springer, Berlin, pp 84–155

    MATH  Google Scholar 

  5. Baron CA et al (2016) NoSQL key-value dbs riak and redis. Database Syst J 4:3–10

    Google Scholar 

  6. Bobrowski S (2011) Optimal multitenant designs for cloud apps. In: 2011 IEEE 4th International Conference on Cloud Computing, IEEE, pp 654–659

  7. Brewer E (2017) Spanner, truetime and the cap theorem. Google Research

  8. Buzacott JA (1967) Finding the mtbf of repairable systems by reduction of the reliability block diagram. Microelectron Reliab 6(2):105–112

    Article  Google Scholar 

  9. Cooper B (2019) Yahoo! cloud serving benchmark. https://github.com/brianfr ankcooper/YCSB. Accessed 10 Oct 2020

  10. Corbellini A, Mateos C, Zunino A et al (2017) Persisting big-data: the NoSQL landscape. Inf Syst 63:1–23

    Article  Google Scholar 

  11. Davoudian A, Chen L, Liu M (2018) A survey on NoSQL stores. ACM Comput Surv (CSUR) 51(2):40

    Google Scholar 

  12. de Sousa ETG, Lins FAA (2018) Modeling strategies to improve the dependability of cloud infrastructures. In: Dependability Engineering, p 7

  13. Diogo M, Cabral B, Bernardino J (2019) Consistency models of NoSQL databases. Future Internet 11(2):43

    Article  Google Scholar 

  14. Gifford DK (1979) Weighted voting for replicated data. In: Proceedings of the Seventh ACM Symposium on Operating Systems Principles, ACM, pp 150–162

  15. Gilbert S, Lynch N (2012) Perspectives on the cap theorem. Computer 45(2):30–36

    Article  Google Scholar 

  16. Gotter P, Kaur K (2020) Enhancing high availability for NoSQL database systems using failover technique. In: Inventive Communication and Computational Technologies, Springer, pp 23–32

  17. Guay Paz JR (2018) Introduction to azure cosmos db. In: Microsoft Azure Cosmos DB Revealed: A Multi-Model Database Designed for the Cloud, Apress, pp 1–23

  18. Harrison G (2015) Consistency models. In: Next Generation Databases: NoSQL, NewSQL, and Big Data, Apress, pp 127–144

  19. Haughian G, Osman R, Knottenbelt WJ (2016) Benchmarking replication in cassandra and mongodb NoSQL datastores. In: Hartmann S, Ma H (eds) Database and expert systems applications. Springer, Berlin, pp 152–166. ISBN: 978-3-319-44406-2

    Chapter  Google Scholar 

  20. Huang X, Wang J, Qiao J et al (2017) Performance and replica consistency simulation for quorum-based NoSQL system cassandra. In: International Conference on Application and Theory of Petri Nets and Concurrency, Springer, pp 78–98

  21. Huang X, Wang J, Yu PS et al (2017) An experimental study on tuning the consistency of NoSQL systems. Concurr Comput Pract Exp 29(12):e4129

    Article  Google Scholar 

  22. Kalid S, Syed A, Mohammad A et al (2017) Big-data NoSQL databases: a comparison and analysis of “big-table”, “dynamodb”, and “cassandra”. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), IEEE, pp 89–93

  23. Keesee W (1965) A method of determining a confidence interval for availability. Naval Missile Center Point. Technical report

  24. Khelaifa A, Benharzallah S, Kahloul L et al (2019) A comparative analysis of adaptive consistency approaches in cloud storage. J Parallel Distrib Comput 129:36–49

    Article  Google Scholar 

  25. Liu A, Yu T (2018) Overview of cloud storage and architecture. Int J Sci Technol Res

  26. Maciel P, Trivedi K, Kim, D (2010) Dependability modeling. In: Performance and Dependability in Service Computing: Concepts, Techniques and Research Directions, IGI Global, Hershey, vol 13

  27. Maciel PR, Trivedi KS, Matias R et al (2012) Dependability modeling. In: Performance and Dependability in Service Computing: Concepts, Techniques and Research Directions, IGI Global, pp 53–97

  28. Martins P, Abbasi M, Sá F (2019) A study over NoSQL performance. In: World Conference on Information Systems and Technologies, Springer, pp 603–611

  29. Marussy K, Klenik A, Molnár V et al (2016) Efficient decomposition algorithm for stationary analysis of complex stochastic petri net models. In: International Conference on Applications and Theory of Petri Nets and Concurrency, Springer, pp 281–300

  30. Mohamed MA, Altrafi OGG, Ismail MO (2014) Relational vs. NoSQL databases: a survey. Int J Comput Inf Technol 3(03):598–601

    Google Scholar 

  31. Mohiuddin I, Almogren A, Al Qurishi M et al (2019) Secure distributed adaptive bin packing algorithm for cloud storage. Futur Gener Comput Syst 90:307–316

    Article  Google Scholar 

  32. Montgomery DC (2017) Design and analysis of experiments, 9th edn. Wiley, Hoboken

    Google Scholar 

  33. Montgomery DC, Runger GC (2013) Applied statistics and probability for engineers, 6th edn. Wiley, Hoboken

    MATH  Google Scholar 

  34. Muñoz-Escoi FD, de Juan-Marin R, Garcia-Escrivá J-R et al (2019) Cap theorem: revision of its related consistency models. Comput J 62(6):943–960

    Article  MathSciNet  Google Scholar 

  35. Osman R, Piazzolla P (2014) Modelling replication in NoSQL datastores. In: Norman G, Sanders W (eds) Quantitative evaluation of systems. Springer, Berlin, pp 194–209

    Chapter  Google Scholar 

  36. Pankowski T (2015) Consistency and availability of data in replicated NoSQL databases. In: 2015 International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), pp 102–109

  37. Perkins L, Redmond E, Wilson J (2018) Seven databases in seven weeks: a guide to modern databases and the NoSQL movement. Pragmatic Book-shelf, Raleigh

    Google Scholar 

  38. Pinheiro et al (2021) The mercury environment: a modeling tool for performance and dependability evaluation. In: 10th International Workshop on the Reliability of Intelligent Environments (WoRIE)

  39. Sherratt E, Prinz A (2019) Eventual consistency formalized. In: International Conference on System Analysis and Modeling, Springer, pp 249–265

  40. Singla P, Singh SS, Gopinath K et al (2018) Probabilistic sequential consistency in social networks. In: 2018 IEEE 25th International Conference on High Performance Computing (HiPC), IEEE, pp 102–111

  41. Verma AK, Ajit S, Karanki DR et al (2010) Reliability and safety engineering, vol 43. Springer, Berlin

    Book  Google Scholar 

  42. Wahid A, Kashyap K (2019) Cassandra—a distributed database system: an overview. In: Abraham A, Dutta P, Mandal JK et al (eds) Emerging technologies in data mining and information security. Springer, Berlin, pp 519–526

    Chapter  Google Scholar 

  43. Wu J, Ping L, Ge X et al (2010) Cloud storage as the infrastructure of cloud computing. In: 2010 International Conference on Intelligent Computing and Cognitive Informatics, IEEE, pp 380–383

  44. Yao X, Wang C (2020) Probabilistic consistency guarantee in partial quorum-based data store. IEEE Trans Parallel Distrib Syst 31(8):1815–1827

    Article  Google Scholar 

  45. Yao X, Wang C-L (2020) Probabilistic consistency guarantee in partial quorum-based data store. IEEE Trans Parallel Distrib Syst 31(8):1815–1827

    Article  Google Scholar 

  46. Younas M (2019) Research challenges of big data

  47. Zimmermann A (2017) Modelling and performance evaluation with timenet 4.4. In: International Conference on Quantitative Evaluation of Systems, Springer, pp 300–303

Download references

Acknowledgements

This work has been supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq under Grants 405224/2018-4 and 302373/2018-7.

Author information

Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

    Carlos Gomes & Eduardo Tavares

  2. Instituto Federal de Pernambuco, Campus Recife, Recife, Brazil

    Meuse Nogueira de O. Junior

  3. Instituto de Computação, Universidade Federal de Alagoas, Maceió, Brazil

    Bruno Nogueira

Authors
  1. Carlos Gomes
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Eduardo Tavares
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Meuse Nogueira de O. Junior
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Bruno Nogueira
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Carlos Gomes.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gomes, C., Tavares, E., Junior, M.N.d.O. et al. Cloud storage availability and performance assessment: a study based on NoSQL DBMS. J Supercomput 78, 2819–2839 (2022). https://doi.org/10.1007/s11227-021-03976-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03976-1

Keywords