Chive: A Simulation Tool for Epidemic Data Replication Protocols Benchmarking

A. Jiménez-Yáñez, J. Navarro, F. D. Muñoz-Escoí, I. Arrieta-Salinas, J. E. Armendáriz-Iñigo


Epidemic data replication protocols are an interesting approach to address the scalability limitations of classic distributed databases. However, devising a system layout that takes full advantage of epidemic replication is a challenging task due to the high number of associated configuration parameters (e.g., replication layers, number of replicas per layer, etc.). The purpose of this paper is to present a Java-based simulation tool that simulates the execution of epidemic data replication protocols on user-defined configurations under different workloads. Conducted experiments show that by using the proposed approach (1) the internal dynamics of epidemic data replication protocols under a specific scenario are better understood, (2) the distributed database system design process is considerably speeded up, and (3) different system configurations can be rapidly prototyped.


  1. Agrawal, D., El Abbadi, A., and Steinke, R. C. (1997). Epidemic algorithms in replicated databases (extended abstract). In 16th ACM Symp. on Principles of Database Syst. (PODS), pages 161-172, Tucson, Arizona, USA. ACM Press.
  2. Apache Software Foundation (2014). Apache Cassandra Gossiper documentation.
  3. Arrieta-Salinas, I., Armendáriz-In˜igo, J. E., and Navarro, J. (2012). Classic replication techniques on the cloud. In Seventh International Conference on Availability, Reliability and Security, Prague, ARES 2012, Czech Republic, August 20-24, 2012, pages 268-273.
  4. Bailis, P., Davidson, A., Fekete, A., Ghodsi, A., Hellerstein, J. M., and Stoica, I. (2013). Highly available transactions: Virtues and limitations. PVLDB, 7(3):181-192.
  5. Bakhshi, R. (2011). Gossiping Models: Formal Analysis of Epidemic Protocols. PhD thesis, Vrije Universiteit, Amsterdam.
  6. Baldoni, R., Guerraoui, R., Levy, R. R., Quéma, V., and Piergiovanni, S. T. (2006). Unconscious eventual consistency with gossips. In Proceedings of the 8th International Conference on Stabilization, Safety, and Security of Distributed Systems, SSS'06, pages 65-81, Berlin, Heidelberg. Springer-Verlag.
  7. Barabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439):509- 512.
  8. Bernstein, P. A., Hadzilacos, V., and Goodman, N. (1987). Concurrency Control and Recovery in Database Systems. Addison-Wesley.
  9. Chockler, G., Keidar, I., and Vitenberg, R. (2001). Group communication specifications: a comprehensive study. ACM Computing Surveys, 33(4):427-469.
  10. Das, S., Agarwal, S., Agrawal, D., and Abbadi, A. E. (2010). ElasTraS: An elastic, scalable, and self managing transactional database for the cloud. Technical report, CS, UCSB.
  11. Daudjee, K. and Salem, K. (2006). Lazy database replication with snapshot isolation. In 32nd Intnl. Conf.
  12. Davidson, S. B., Garcia-Molina, H., and Skeen, D. (1985). Consistency in partitioned networks. ACM Comput. Surv., 17(3):341-370.
  13. Eugster, P. T., Guerraoui, R., m. Kermarrec, A., and Massouli, L. (2004). From epidemics to distributed computing. IEEE Computer, 37:60-67.
  14. Fekete, A. D. and Ramamritham, K. (2010). Consistency models for replicated data. In Replication, pages 1- 17.
  15. Gilbert, S. and Lynch, N. A. (2002). Brewer's conjecture and the feasibility of consistent, available, partitiontolerant web services. SIGACT News, 33(2):51-59.
  16. Gilbert, S. and Lynch, N. A. (2012). Perspectives on the CAP theorem. IEEE Computer, 45(2):30-36.
  17. Holliday, J., Steinke, R. C., Agrawal, D., and El Abbadi, A. (2003). Epidemic algorithms for replicated databases. IEEE Trans. Knowl. Data Eng., 15(5):1218-1238.
  18. JChart2D (2014). JChart2D, precise visualization of data.
  19. Johnson, R., Pandis, I., and Ailamaki, A. (2014). Eliminating unscalable communication in transaction processing. The VLDB Journal, 23:1-23.
  20. JUNG (2014). JUNG - Java Universal Network/Graph Framework.
  21. Lamport, L. (1979). How to make a multiprocessor computer that correctly executes multiprocess programs. IEEE Trans. Computers, 28(9):690-691.
  22. Lin, Y., Kemme, B., Jiménez-Peris, R., Patin˜o-Martínez, M., and Armendáriz-In˜igo, J. E. (2009). Snapshot isolation and integrity constraints in replicated databases. ACM Trans. Database Syst., 34(2).
  23. Navarro, J., Armendáriz-In˜igo, J. E., and Climent, A. (2011). An adaptive and scalable replication protocol on power smart grids. Scalable Computing: Practice and Experience, 12(3).
  24. Sancho-Asensio, A., Navarro, J., Arrieta-Salinas, I., Armendáriz-In˜igo, J. E., Jiménez-Ruano, V., Zaballos, A., and Golobardes, E. (2014). Improving data partition schemes in smart grids via clustering data streams. Expert Systems with Applications, 41(13):5832 - 5842.
  25. Shoens, K. A. (1986). Data sharing vs. partitioning for capacity and availability. IEEE Database Eng. Bull., 9(1):10-16.
  26. Stonebraker, M. (1986). The case for shared nothing. IEEE Database Eng. Bull., 9(1):4-9.
  27. Stonebraker, M. (2010). SQL databases v. NoSQL databases. Communications of the ACM, 53(4):10-11.
  28. Terry, D. B. (2008). Replicated data management for mobile computing. Synthesis Lectures on Mobile and Pervasive Computing, 3(1):1-94.
  29. Terry, D. B., Demers, A. J., Petersen, K., Spreitzer, M., Theimer, M., and Welch, B. B. (1994). Session guarantees for weakly consistent replicated data. In 13th Intnl. Conf. Paral. Dist. Inform. Syst. (PDIS), pages 140-149, Austin, Texas, USA. IEEE-CS Press.
  30. Vogels, W. (2009). Eventually consistent. Commun. ACM, 52(1):40-44.
  31. Wiesmann, M. and Schiper, A. (2005). Comparison of database replication techniques based on total order broadcast. IEEE TKDE, 17(4):551-566.

Paper Citation

in Harvard Style

Jiménez-Yáñez A., Navarro J., Muñoz-Escoí F., Arrieta-Salinas I. and Armendáriz-Iñigo J. (2014). Chive: A Simulation Tool for Epidemic Data Replication Protocols Benchmarking . In Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014) ISBN 978-989-758-036-9, pages 428-436. DOI: 10.5220/0005107004280436

in Bibtex Style

author={A. Jiménez-Yáñez and J. Navarro and F. D. Muñoz-Escoí and I. Arrieta-Salinas and J. E. Armendáriz-Iñigo},
title={Chive: A Simulation Tool for Epidemic Data Replication Protocols Benchmarking},
booktitle={Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)
TI - Chive: A Simulation Tool for Epidemic Data Replication Protocols Benchmarking
SN - 978-989-758-036-9
AU - Jiménez-Yáñez A.
AU - Navarro J.
AU - Muñoz-Escoí F.
AU - Arrieta-Salinas I.
AU - Armendáriz-Iñigo J.
PY - 2014
SP - 428
EP - 436
DO - 10.5220/0005107004280436