A locality-based replication manager for data cloud

  • Reza Sookhtsaraei
  • Javad Artin
  • Ali Ghorbani
  • Ahmad Faraahi
  • Hadi Adineh
Article

Abstract

Efficient data management is a key issue for environments distributed on a large scale such as the data cloud. This can be taken into account by replicating the data. The replication of data reduces the time of service and the delay in availability, increases the availability, and optimizes the distribution of load in the system. It is worth mentioning, however, that with the replication of data, the use of resources and energy increases due to the storing of copies of the data. We suggest a replication manager that decreases the cost of using resources, energy, and the delay in the system, and also increases the availability of the system. To reach this aim, the suggested replication manager, called the locality replication manager (LRM), works by using two important algorithms that use the physical adjacency feature of blocks. In addition, a set of simulations are reported to show that LRM can be a suitable option for distributed systems as it uses less energy and resources, optimizes the distribution of load, and has more availability and less delay.

Keywords

Data cloud Replication Graph Locality replication manager (LRM) 

CLC number

TP393 

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References

  1. Aazami, A., Ghandeharizadeh, S., Helmi, T., 2004. Near optimal number of replicas for continuous media in ad-hoc networks of wireless devices. Proc. 1st Workshop on Multimedia Information Systems.Google Scholar
  2. Amazon, 2008. Amazon Simple Storage Service (Amazon S3). Available from http://aws.amazon.com/s3.Google Scholar
  3. Armbrust, M., Fox, A., Griffith, R., et al., 2009. Above the Clouds: a Berkeley View of Cloud Computing. Tech-nical Report, No. UCB/EECS-2009-28, Department of EECS, California University, Berkeley.Google Scholar
  4. Bonvin, N., Papaioannou, T.G., Aberer, K., 2009. Dynamic cost-efficient replication in data clouds. Proc. 1st Work-shop on Automated Control for Datacenters and Clouds, p.49–56. http://dx.doi.org/10.1145/1555271.1555283CrossRefGoogle Scholar
  5. Buyya, R., Yeo, C.S., Venugopal, S., et al., 2009. Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the fifth utility. Fut. Gener. Comput. Syst., 25(6):599–616. http://dx.doi.org/10.1016/j.future.2008.12.001CrossRefGoogle Scholar
  6. Chang, R.S., Chang, H.P., 2008. A dynamic data replication strategy using access weights in data grids. J. Supercom-put., 45(3):277–295. http://dx.doi.org/10.1007/s11227-008-0172-6MathSciNetCrossRefGoogle Scholar
  7. Choi, S.C., Youn, H.Y., 2012. Dynamic hybrid replication effectively combining tree and grid topology. J. Super-comput., 59(3):1289–1311. http://dx.doi.org/10.1007/s11227-010-0536-6CrossRefGoogle Scholar
  8. Creeger, M., 2009. Cloud computing: an overview. ACM Queue, 7(5):2–4.Google Scholar
  9. Dabrowski, C., 2009. Reliability in grid computing systems. Concurr. Comput. Pract. Exp., 21(8):927–959. http://dx.doi.org/10.1002/cpe.v21:8CrossRefGoogle Scholar
  10. Dikaiakos, M.D., Katsaros, D., Mehra, P., et al., 2009. Cloud computing: distributed Internet computing for IT and scientific research. IEEE Internet Comput., 13(5):10–13. http://dx.doi.org/10.1109/MIC.2009.103CrossRefGoogle Scholar
  11. Doğan, A., 2009. A study on performance of dynamic file replication algorithms for real-time file access in data grids. Fut. Gener. Comput. Syst., 25(8):829–839. http://dx.doi.org/10.1016/j.future.2009.02.002CrossRefGoogle Scholar
  12. Ghemawat, S., Gobioff, H., Leung, S., 2003. The Google file system. Proc. 19th ACM Symp. on Operating Systems Principles, p.29–43. http://dx.doi.org/10.1145/1165389.945450Google Scholar
  13. Hassan, O.A.H., Ramaswamy, L., Miller, J., et al., 2009. Replication in overlay networks: a multi-objective opti-mization approach. Int. Conf. on Collaborative Compu-ting: Networking, Applications and Worksharing, p.512–528. http://dx.doi.org/10.1007/978-3-642-03354-4_39CrossRefGoogle Scholar
  14. Intanagonwiwat, C., Govindan, R., Estrin, D., 2000. Directed diffusion: a scalable and robust communication para-digm for sensor networks. Proc. 6th Annual Int. Conf. on Mobile Computing and Networking, p.56–67. http://dx.doi.org/10.1145/345910.345920Google Scholar
  15. Lamehamedi, H., Shentu, Z., Szymanski, B., et al., 2003. Simulation of dynamic data replication strategies in data grids. Proc. Int. Parallel and Distributed Processing Symp. http://dx.doi.org/10.1109/IPDPS.2003.1213206Google Scholar
  16. Lei, M., Vrbsky, S.V., Hong, X.Y., 2008. An on-line replica-tion strategy to increase availability in data grids. Fut. Gener. Comput. Syst., 24(2):85–98. http://dx.doi.org/10.1016/j.future.2007.04.009CrossRefGoogle Scholar
  17. Li, W.H., Yang, Y., Yuan, D., 2011. A novel cost-effective dynamic data replication strategy for reliability in cloud data centres. IEEE 9th Int. Conf. on Dependable, Auto-nomic and Secure Computing, p.496–502. http://dx.doi.org/10.1109/DASC.2011.95Google Scholar
  18. Li, W.H., Yang, Y., Chen, J.J., et al., 2012. A cost-effective mechanism for cloud data reliability management based on proactive replica checking. 12th IEEE/ACM Int. Symp. on Cluster, Cloud and Grid Computing, p.564–571. http://dx.doi.org/10.1109/CCGrid.2012.33Google Scholar
  19. Nukarapu, D.T., Tang, B., Wang, L.Q., et al., 2011. Data replication in data intensive scientific applications with performance guarantee. IEEE Trans. Parall. Distr. Syst., 22(8):1299–1306. http://dx.doi.org/10.1109/TPDS.2010.207CrossRefGoogle Scholar
  20. Qiu, L.L., Padmanabhan, V.N., Voelker, G.M., 2001. On the placement of Web server replicas. Proc. IEEE 20th An-nual Joint Conf. of the IEEE Computer and Communica-tions Societies. http://dx.doi.org/10.1109/INFCOM.2001.916655Google Scholar
  21. Rahman, R.M., Barker, K., Alhajj, R., 2006. Replica place-ment design with static optimality and dynamic main-tainability. Proc. 6th IEEE Int. Symp. on Cluster Com-puting and the Grid, p.434–437. http://dx.doi.org/10.1109/CCGRID.2006.85Google Scholar
  22. Ranganathan, K., Foster, I.T., 2001. Identifying dynamic replication strategies for a high-performance data grid. Int. Workshop on Grid Computing, p.75–86. http://dx.doi.org/10.1007/3-540-45644-9_8MATHGoogle Scholar
  23. Shvachko, K., Hairong, K., Radia, S., et al., 2010. The Ha-doop distributed file system. IEEE 26th Symp. on Mass Storage Systems and Technologies, p.1–10. http://dx.doi.org/10.1109/MSST.2010.5496972Google Scholar
  24. Tang, B., Das, S.R., Gupta, H., 2008. Benefit-based data caching in ad hoc networks. IEEE Trans. Mob. Comput., 7(3):289–304. http://dx.doi.org/10.1109/TMC.2007.70770CrossRefGoogle Scholar
  25. Tang, X., Xu, J., 2005. QoS-aware replica placement for con-tent distribution. IEEE Trans. Parall. Distr. Syst., 16(10):921–932. http://dx.doi.org/10.1109/TPDS.2005.126CrossRefGoogle Scholar
  26. Tu, M., Tadayon, T., Xia, Z., et al., 2007. A secure and scala-ble update protocol for P2P data grids. 10th IEEE High Assurance Systems Engineering Symp., p.423–424. http://dx.doi.org/10.1109/HASE.2007.40CrossRefGoogle Scholar
  27. Wei, Q., Veeravalli, B., Gong, B., et al., 2010. CDRM: a cost-effective dynamic replication management scheme for cloud storage cluster. IEEE Int. Conf. on Cluster Computing, p.188–196. http://dx.doi.org/10.1109/CLUSTER.2010.24Google Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Reza Sookhtsaraei
    • 1
  • Javad Artin
    • 1
  • Ali Ghorbani
    • 2
  • Ahmad Faraahi
    • 1
  • Hadi Adineh
    • 1
  1. 1.Department of Computer Engineering and Information TechnologyPayame Noor UniversityTehranIran
  2. 2.Department of Industrial Engineering, Faculty of EngineeringMaziar UniversityNoorIran

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