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Data replication strategy with satisfaction of availability, performance and tenant budget requirements

  • Said LimamEmail author
  • Riad Mokadem
  • Ghalem Belalem
Article
  • 76 Downloads

Abstract

We propose a dynamic replication strategy that satisfies simultaneously availability and performance tenant requirements while taking into account the tenant budget and the provider profit. The proposed strategy is based on a cost model that aims to calculate the minimum number of replicas required to maintain a high data availability. A replica creation is triggered only when this number of replicas is not reached or when the response time objective is not satisfied. Then, the replication must be profitable for the provider when creating a new replica. Furthermore, data replication and query scheduling are coupled in order to place these replicas in a load balancing way while dealing with the tenant budget. The experiment results prove that the proposed strategy can significantly improve availability and performance while the tenant budget is taken into account.

Keywords

Cloud systems Data replication Replication cost Economic aspects CloudSim 

References

  1. 1.
    Kouki, Y., Ledoux, T.: CSLA : a Language for improving cloud SLA management. In: Proceedings of the International Conference on Cloud Computing and Services Science, CLOSER 2012, pp. 586–591. Porto, Portugal (2012)Google Scholar
  2. 2.
    Milani, A., Navimipour, N.J.: Comprehensive review of the data replication techniques in the cloud environments: major trends and future directions. J. Netw. Comput. Appl. 64, 229–238 (2016)CrossRefGoogle Scholar
  3. 3.
    Tabet, K., Mokadem, R., Laouar, M.R., Eom, S.B.: Data replication in cloud systems: a survey. IJISSC 8(3), 17–33 (2017)Google Scholar
  4. 4.
    Tos, U., Mokadem, R., Hameurlain, A., Ayav, T., Bora, S.: A performance and profit oriented data replication strategy f]or cloud systems. In: Proceedings of the International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), vol. 2016, pp. 780–787. Toulouse (2016)Google Scholar
  5. 5.
    Xiong, R., Luo, J., Song, A., Liu, B., Dong, F.: QoS preference-aware replica selection strategy using MapReduce-based PGA in data grids. In: 2011 International Conference on Parallel Processing, Taipei City, pp. 394–403 (2011)Google Scholar
  6. 6.
    Kloudas, K., et al.: PIXIDA: optimizing data parallel jobs in wide-area data analytics. PVLDB 9(2), 72–83 (2015)Google Scholar
  7. 7.
    Silvestre, G., Monnet, S., krishnaswamy, R., Sens, P.: AREN: a popularity aware replication scheme for cloud storage. In: IEEE International Conference on Parallel and Distributed Systems (ICPADS), pp. 189–196, IEEE, Singapore (2012)Google Scholar
  8. 8.
    Da-Wei, S., Gui-Ran, C., Shang, G., Li-Zhong, Jin, Xing-Wei, Wang: Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. J. Comput. Sci. Technol. 27(2), 256–72 (2012)CrossRefzbMATHGoogle Scholar
  9. 9.
    Wei, Q., Veeravalli, B., Gong, B., Zeng, L., Feng, D.: CDRM: a cost effective dynamic replication management scheme for cloud storage cluster. In: Proceedings of the IEEE Cluster Computing, pp. IEEE, 188–196. (2010)Google Scholar
  10. 10.
    Park, S.M., Kim, J.H., Ko, Y.B., Yoon, W.S.: Dynamic data grid replication strategy based on internet hierarchy. In: Li, M., Su, X.-H. (eds.) Grid and Cooperative Computing, pp. 838–846. Springer, Berlin (2004)CrossRefGoogle Scholar
  11. 11.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2011)Google Scholar
  12. 12.
    Belalem, G., Limam, S.: Towards improving the functioning of cloudsim simulator. Int. Conf. Dig. Inf. Process. Commun. 189, 258–267 (2011)Google Scholar
  13. 13.
    Lamehamedi, H., Szymanski, B., Shentu, Z., Deelman, E.: Data replication strategies in grid environments. In: Proceedings of the Fifth International Conference on Algorithms and Architectures for Parallel Processing, pp. 378–383 (2002)Google Scholar
  14. 14.
    Bell, W.H., Cameron, D.G., Carvajal-Schiaffino, R., Millar, A.P., Stockinger, K., Zini, F.: Evaluation of an economy-based file replication strategy for a data grid. In: Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2003, pp. 661–668 (2003)Google Scholar
  15. 15.
    Uras, Tos: Réplication de données dans les systè mes de gestion de données à grande échelle. PhD manuscript. (2017) (In French)Google Scholar
  16. 16.
    Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 2nd edn. Prentice-Hall Inc., Upper Saddle River, NJ (1999)Google Scholar
  17. 17.
    Lang, W., Shankar, S., Patel, J.M., Kalhan, A.: Towards multitenant performance SLOs. IEEE Trans. Knowl. Data Eng. 26(6), 1447–1463 (2014)CrossRefGoogle Scholar
  18. 18.
    Kouki, Y., Ledoux, T., Sharrock, R.: Cross-layer SLA selection for cloud services. In: Proceedings of the 1st International Symposium on Network Cloud Computing and Applications. IEEE, pp. 143–147 (2011)Google Scholar
  19. 19.
    Tos, U., Mokadem, R., Hameurlain, A., Ayav, T., Bora, S.: Ensuring performance and provider profit through data replication in cloud systems. Clust. Comput. 21, 1479–1492 (2017)CrossRefGoogle Scholar
  20. 20.
    Xiaohu, Bai, Hai, Jin, Xiaofei, Liao, Xuanhua, Shi, Zhiyuan Shao: RTRM: a response time-based replica management strategy for cloud storage system. In: Proceedings of the grid and pervasive computing. GPC, Springer, pp. 124–33 (2013)Google Scholar
  21. 21.
    Liu, J., Shen, H.: A popularity-aware cost-effective replication scheme for high data durability in cloud storage. In: Proceedings of the IEEE International Conference on Big Data (Big Data), Washington, DC, pp. 384–389, (2016)Google Scholar
  22. 22.
    Mansouri, Y., Nadjaran Toosi, A., Buyya, R.: Cost optimization for dynamic replication and migration of data in cloud data centers. IEEE Trans. Cloud Comput. 99, 1–14 (2017)CrossRefGoogle Scholar
  23. 23.
    Zhang, H., Lin, B., Liu, Z., Guo, W.: Data replication placement strategy based on bidding mode for cloud storage cluster. In: Proceedings of the 11th International Conference on Web Information System and Application, pp. 207–212 (2014)Google Scholar
  24. 24.
    Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Clust. Comput. 18(1), 385–402 (2015)CrossRefGoogle Scholar
  25. 25.
    Sakr, S., Liu, A.: SLA-based and consumer-centric dynamic provisioning for cloud databases. In: Proceedings of the IEEE 5th International Conference on Cloud Computing, IEEE, pp. 360–367 (2012)Google Scholar
  26. 26.
    Kaur, Gill Navneet, Sarbjeet, Singh: Dynamic cost-aware re-replication and rebalancing strategy in cloud system. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) pp. 39–47, (2014)Google Scholar
  27. 27.
    Sousa, F.R.C., Machado, J.C.: “Towards elastic multi-tenant database replication with quality of service. In: Proceedings of the IEEE/ACM 5th International Conference on Utility and Cloud Computing, UCC 2012, pp. 168–175 (2012)Google Scholar
  28. 28.
    Hussein, M.-K., Mousa, M.-H.: A light-weight data replication for cloud data centers environment. Int. J. Eng. Innov. Technol. 1(6), 169–175 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science Department, Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOranAlgeria
  2. 2.Institut de Recherche en Informatique de Toulouse (IRIT)Paul Sabatier UniversityToulouseFrance

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