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NoSQL-based storage systems: influence of consistency on performance, availability and energy consumption

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Abstract

Big data applications have motivated the adoption of NoSQL database management systems (DBMS), which usually provide better performance and availability than relational DBMSs. Nowadays, these applications are commonly hosted in cloud storage services. In general, NoSQL DBMSs adopt eventual consistency, in which not all redundant nodes have the newest data, but, eventually, such data will be present in all nodes. Different levels of consistency can be utilized, but they may affect user experience and service level agreements. Therefore, techniques for evaluating the impact of eventual consistency are important for system design. This work proposes a method based on generalized stochastic Petri nets for evaluating cloud storage systems based on NoSQL DBMS using quorum technique. The models take into account distinct consistency levels and redundant nodes for estimating system availability, latency and the probability of accessing the newest data. An energy consumption model is also proposed for assessing the influence of consistency levels. Experimental results demonstrate the practical feasibility of our approach.

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Notes

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

  2. https://riak.com/products/riak-kv/.

References

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

  2. Younas M (2019) “Research challenges of big data,”

  3. Corbellini et al (2017) Persisting big-data: the nosql landscape. Inf Syst 63:1–23

    Article  Google Scholar 

  4. Meier A, Kaufmann M (2019) “Nosql databases,” In: SQL & NoSQL databases, Springer, pp 201–218

  5. Tomar et al. (2019) “Migration of healthcare relational database to nosql cloud database for healthcare analytics and management,” In: Healthcare data analytics and management, Elsevier, pp 59–87

  6. Gomes V et al. (2017) “Verifying strong eventual consistency in distributed systems,” In: Proceedings of the ACM on programming languages, vol 1, no. OOPSLA, 109: 1–109:28, ISSN: 2475-1421

  7. Bailis, et al. (2014) “Quantifying eventual consistency with pbs,” VLDB J, vol 23, no. 2, pp 279–302

  8. Tian et al (2015) Latency critical big data computing in finance. J Finance Data Sci 1(1):33–41

    Article  Google Scholar 

  9. Singla et al. (2018) “Probabilistic sequential consistency in social networks,” In: 2018 IEEE 25th International Conference on High Performance Computing (HiPC), IEEE, pp 102–111

  10. Bailis et al. (2012) “Probabilistically bounded staleness for practical partial quorums,” In: Proceedings of VLDB Endowing, vol 5, no. 8, pp 776–787, ISSN: 2150-8097

  11. “Usage impact on data center electricity needs: a system dynamic forecasting model,” Appl Energy, vol 291, pp 116–798, (2021), ISSN: 0306- 2619

  12. Andrae AS (2019) Comparison of several simplistic high-level approaches for estimating the global energy and electricity use of ICT networks and data centers. Int J 5:51

    Google Scholar 

  13. Liu et al (2020) Energy consumption and emission mitigation prediction based on data center traffic and Pue for global data centers. Glob Energy Interconnect 3(3):272–282

    Article  Google Scholar 

  14. Maciel P et al. (2011) “Dependability modeling,” In: IGI Publishing, ch. 3, pp. 53–97

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

    Google Scholar 

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

    Google Scholar 

  17. Guay Paz JR (2018) “Introduction to azure cosmos db,” In: Microsoft Azure cosmos DB revealed: a multi-model database designed for the cloud, Berkeley, CA: A press, pp 1–23

  18. Perkins L, Redmond E, Wilson J (2018) Seven databases in seven weeks: a guide to modern databases and the NoSQL movement. Pragmatic Bookshelf

    Google Scholar 

  19. Haughian et al (2016) Benchmarking replication in Cassandra and Mongodb Nosql datastores. In: Hartmann S, Ma H (eds) Database Expert Syst Appl. Springer International Publishing, Cham, pp 152–166

    Chapter  Google Scholar 

  20. Huang et al (2017) An experimental study on tuning the consistency of Nosql systems. Concurr Comput Pract Exp 29(12):e4129

    Article  Google Scholar 

  21. Harrison G (2015) “Consistency models,” In: Next generation databases: NoSQL, NewSQL, and big data, Apress, pp 127–144

  22. 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 Singapore, Singapore, pp 519–526

    Chapter  Google Scholar 

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

    Google Scholar 

  24. Kalid 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

  25. Gifford DK (1979) “Weighted voting for replicated data,” In: Proceedings of the seventh ACM symposium on Operating systems principles, ACM, pp 150–162

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

    Article  Google Scholar 

  27. Burdakov et al (2016) “Estimation models for nosql database consistency characteristics,” In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp 35–42

  28. Klein et al. (2015) “Performance evaluation of nosql databases: a case study,” In: Proceedings of the 1st workshop on performance analysis of big data systems, ser. PABS ’15, Austin, Texas, USA: ACM, pp 5–10, ISBN: 978-1-4503-3338-2

  29. Attiya et al (2016) Limitations of highly-available eventually-consistent data stores. IEEE Trans Parallel Distrib Syst 28(1):141–155

    Article  Google Scholar 

  30. Liu et al (2015) Quantitative analysis of consistency in nosql key-value stores. In: Campos J, Haverkort BR (eds) Quantitative evaluation of systems. Springer International Publishing, Cham, pp 228–243

    Chapter  Google Scholar 

  31. Chihoub et al. (2015) “Exploring energy-consistency trade-offs in Cassandra cloud storage system,” In: 2015 27th International symposium on computer architecture and high performance computing (SBAC-PAD), pp 146–153

  32. Osman R, Piazzolla P (2014) “Modelling replication in nosql datastores,” in Quantitative Evaluation of Systems: 11th International Conference, QEST (2014) Florence, Italy, September 8–10. Proceedings, G. Norman and W. Sanders. Eds. Cham: Springer International Publishing 2014:194–209

  33. Gotter P, Kaur K (2020) “Enhancing high availability for nosql database systems using failover techniques,” In: Inventive communication and computational technologies, Springer, pp 23–32

  34. Mahajan D, Blakeney C, Zong Z (2019) Improving the energy efficiency of relational and nosql databases via query optimizations. Sustain Comput Inform Syst 22:120–133

    Google Scholar 

  35. Naseri Seyedi Noudoust N, Adabi S, Rezaee A (2022) A quorum-based data consistency approach for non-relational database. Clust Comput 25:1–26

    Article  Google Scholar 

  36. Khelaifa A, Benharzallah S, Kahloul L (2022) A new adaptive causal consistency approach in edge computing environment. Int J Comput Digit Syst 12(1):945–960

    Article  Google Scholar 

  37. Abadi D (2012) Consistency tradeoffs in modern distributed database system design: cap is only part of the story. Computer 45(2):37–42

    Article  Google Scholar 

  38. “Details omitted due to double-blind reviewing.”

  39. Datastax, Datastax documentation, https://docs.datastax.com/en/cassandra-oss/2.1/cassandra/tools/toolsCFstats.html, Acessed: 2022-05-21, (2022)

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

  41. Cooper B (2022) Yahoo! cloud serving benchmark, https://github.com/brianfrankcooper/YCSB, Acessed: 2022-12-30

  42. Maciel P (2023) Performance, reliability, and availability evaluation of computational systems. CRC Press LLCs

    Book  Google Scholar 

  43. Tang E, Fan Y (2016) “Performance comparison between five nosql databases,” In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pp 105–109. https://doi.org/10.1109/CCBD.2016.030.

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

  45. Montgomery DC (2017) Design and analysis of experiments, 9th edn. John wiley & sons

    Google Scholar 

  46. Melo C et al. (2017) “Capacity-oriented availability model for resources estimation on private cloud infrastructure,” In: 2017 IEEE 22nd Pacific rim international symposium on dependable computing (PRDC)

  47. Chou Y-H, Raghavan A, Lahiri T (2018) “Oracle timesten scaleout: a new scale-out in-memory database architecture for extreme oltp,” In: Proceedings of the international workshop on real-time business intelligence and analytics, pp 1–4

  48. Astrova et al (2018) “Comparison of dbaas architectures,” In: 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), IEEE, pp 1–5

  49. AWS, Pricing for provisioned capacity, https://aws.amazon.com/dynamodb/pricing/provisioned/?nc1=h ls, Acessed: 2022-12-30, 2022

  50. Gayathiri N, Jaspher DD, Natarajan A (2018) Big health data processing with document-based Nosql database. J Comput Theor Nanosci 15(5):1649–1655

    Article  Google Scholar 

  51. Silva et al (2018) Sensitivity analysis of an availability model for disaster tolerant cloud computing system. Int J Netw Manag 28(6):e2040

    Article  Google Scholar 

  52. IEA, Data centres and data transmission networks, http://www.iea.org/reports/data-centres-and-data-transmission-networks, Accessed: 2021-05- 02, (2020)

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Acknowledgements

This work has been supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq under grant 302997/2021-0.

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Authors and Affiliations

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

    Carlos Gomes, Paulo Maciel & 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

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  1. Carlos Gomes
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  3. Bruno Nogueira
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Contributions

CG, ET, and MN contributed to model conception. Paulo Maciel and Bruno Nogueira contributed to data analysis. All authors reviewed the manuscript.

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Correspondence to Carlos Gomes.

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Gomes, C., de O. Junior, M.N., Nogueira, B. et al. NoSQL-based storage systems: influence of consistency on performance, availability and energy consumption. J Supercomput 79, 21424–21448 (2023). https://doi.org/10.1007/s11227-023-05488-6

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