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, Volume 104, Issue 2, pp 739–752 | Cite as

QoS Aware Replica Control Strategies for Distributed Real Time Database Management System

  • Nuparam ChauhanEmail author
  • Surya Prakash Tripathi
Article
  • 33 Downloads

Abstract

In distributed real time database management system (DRT-DBMS), QoS aware replication control strategy is necessary to ensure availability and to improve the system performance. Since user request and need of data is dynamic in nature, it requires an approach which addresses the problem of data distribution and also resolves them in DRT-DBMS. A large quantity of data file is produced so we need a dynamic replication approach through which we can reduce the data access time. In order to replicate it, at the nearest site, replica placement and replacement algorithms are required to be evaluated. This paper presents a heuristic approach Greedy-Cover Firefly algorithm in which replica is placed dynamically based on QoS requirement and replaced with the optimal adaptive replica replacement algorithm for each data object. The replication cost required for copying data object to each set of a node is calculated to select the optimal node. Here, the replication decision is taken on the basis of access history of data on multiple sites. Due to the limited storage capacity of each node, the value of each file is calculated, and the file with the smallest value is removed to replace other files. A detailed simulation shows that the proposed algorithms can greatly improve the system performance to reduce the system resources consumed with respect to time and QoS requirement.

Keywords

Real time systems Quality of service Optimal replication Replica replacement Access frequency 

Notes

References

  1. 1.
    Ram, N., & Udai, S. (2011). Over load detection and admission control policy in DRTDBS. Information technology and mobile communication (pp. 45–54). Berlin: Springer.Google Scholar
  2. 2.
    Shanker, U., Misra, M., & Sarje, A. K. (2008). Distributed real time database systems: Background and literature review. Distributed and Parallel Databases, ACM, 23(2), 127–149.Google Scholar
  3. 3.
    Lam, K.-Y., Lee, V. C. S., Hung, S. L., & Kao, B. C. M. (1997). Priority assignment in distributed real-time databases using optimistic concurrency control. IEE Proceedings-Computers and Digital Techniques, 144(5), 324–330.Google Scholar
  4. 4.
    Udai, S., & Ram, N. (2011). A new admission control policy and over load detection in DRTDBS. International Journal of Data Mining and Emerging Technologies, 1(1), 30–38.Google Scholar
  5. 5.
    Pinho, L. M., Vasques, F., & Wellings, A. (2004). Replication management in reliable real-time systems. Real-Time Systems, Springer, 26(3), 261–296.zbMATHGoogle Scholar
  6. 6.
    Lam, K. Y., Kuo, T. W., Tsang, W. H., & Law, G. C. (2000). Concurrency control in mobile distributed real-time database systems. Information Systems, Elsevier, 25(4), 261–286.Google Scholar
  7. 7.
    Özgür, U. (1994). Processing real-time transactions in a replicated database system. Distributed and Parallel Databases, Springer, 2(4), 405–436.Google Scholar
  8. 8.
    Andler, S. F., Hansson, J., Eriksson, J., Mellin, J., Berndtsson, M., & Eftring, B. (1996). DeeDS towards a distributed and active real-time database system. ACM SIGMOD Record, 25(1), 38–51.Google Scholar
  9. 9.
    Malik, S. U., Khan, S. U., Ewen, S. J., Tziritas, N., Kolodziej, J., Zomaya, A. Y., et al. (2016). Performance analysis of data intensive cloud systems based on data management and replication: A survey. Distributed and Parallel Databases, Springer, 34(2), 179–215.Google Scholar
  10. 10.
    Matthias, N., & Matthias, J. (2000). Performance modeling of distributed and replicated databases. IEEE Transactions on Knowledge and Data Engineering, 12(4), 645–672.Google Scholar
  11. 11.
    Swaroop, V., & Shanker, U. (2011). Data management in mobile distributed real time database systems: Reviews and issues. International Journal of Computer Science and Information Technologies, 2(4), 1517–1522.Google Scholar
  12. 12.
    Shetan, R. C. (2013). Performance evaluation of real time database systems in distributed environment. International Journal of Computer Technology and Applications, 4(5), L785–L792.Google Scholar
  13. 13.
    Krithi, R. (1993). Real-time databases. Distributed and Parallel Databases, 1(2), 199–226.Google Scholar
  14. 14.
    Tokuda, H., & Mercer, C. W. (1989). ARTS: A distributed real-time kernel. ACM SIGOPS Operating Systems Review, 23(3), 29–53.Google Scholar
  15. 15.
    Ramamritham, K., Son, S. H., & Dipippo, L. C. (2004). Real-time databases and data services. Real-Time Systems, ACM Transactions, 28(2–3), 179–215.zbMATHGoogle Scholar
  16. 16.
    Bahareh, A. M., & Nima, J. N. (2016). A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. Journal of Network and Computer Applications, 64, 229–238.Google Scholar
  17. 17.
    Korupolu, M. R., & Dahlin, M. (2002). Coordinated placement and replacement for large-scale distributed caches. IEEE Transactions on Knowledge and Data Engineering, 14(6), 1317–1329.Google Scholar
  18. 18.
    Umut, T. (2014). Distributed database design using evolutionary algorithms. Journal of Communications and Networks, 16(4), 430–435.Google Scholar
  19. 19.
    Zaman, S., & Grosu, D. (2011). A distributed algorithm for the replica placement problem. IEEE Transactions on Parallel and Distributed Systems, 22(9), 1455–1468.Google Scholar
  20. 20.
    Ashish, S., & Shyam, P. S. K. (2015). Issues in distributed real time replication database system. The Opinion, 4(8), 11–20.Google Scholar
  21. 21.
    Rashed, S., Safa’A, S., & Abdul-kader, H. (2016). Scalable data-oriented replication with flexible consistency in real-time data systems. Data Science Journal, 15(4), 1–15.Google Scholar
  22. 22.
    Stefan, P. (1994). Replica determinism in distributed real-time systems: A brief survey. Real-Time Systems, Springer, 6(3), 289–316.Google Scholar
  23. 23.
    Guoxin, L., Haiying, S., & Harrison, C. (2016). Selective data replication for online social networks with distributed datacenters. IEEE Transactions on Parallel and Distributed Systems, 27(8), 2377–2393.Google Scholar
  24. 24.
    Jon, O. H., Norvald, H. R., & Kjetil, N. (2010). DYFRAM: Dynamic fragmentation and replica management in distributed database systems. Distributed and Parallel Databases, Springer, 282(3), 157–185.Google Scholar
  25. 25.
    Lin, W., & Bharadwaj, V. (2006). An object replication algorithm for real-time distributed databases. Distributed and Parallel Databases, Springer, 19(2), 125–146.Google Scholar
  26. 26.
    Torky, S., Hazem, E.-B., & Hala, A. H. (2012). Design of efficient dynamic replica control algorithm for periodic/aperiodic transactions in distributed real-time databases. IJCSI International Journal of Computer Science Issues, 9(1), 92–100.Google Scholar
  27. 27.
    Xueyan, T., & Jianliang, X. (2005). QoS-aware replica placement for content distribution. IEEE Transactions on Parallel and Distributed Systems, 16(10), 921–932.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentFGIETRaebareliIndia
  2. 2.Computer Science and Engineering DepartmentIETLucknowIndia

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