Advertisement

OGSO-DR: oppositional group search optimizer based efficient disaster recovery in a cloud environment

  • A. Arul MaryEmail author
  • K. Chitra
Original Research
  • 21 Downloads

Abstract

In cloud computing, enormous information storage is one of the great challenging tasks in term of reliable storage of sensitive data and quality of storage service. Among different cloud security issues, the data disaster recovery is the most critical issue. The motive of recovery technique is to help the user to collect data from any backup server when server lost his data and unable to provide data to the user. To achieve this purpose, many types of research develop different techniques. Therefore, in this paper, we propose a data disaster recovery process using Oppositional Group search optimizer (OGSO) algorithm which is mainly avoid the disaster in the cloud. The proposed data recovery process consists of four modules such as (1) file uploading module, (2) replica generation module, (3) data backup module and (4) disaster recovery module. At first, we split the data into a number of files and upload the file to the corresponding virtual machine using OGSO algorithm. After that, we generate the replica based on each file bandwidth. The replica is mainly used for data backup strategy. Finally, the user query based files are backup and retrieve based on replicas. The experimental results show that the proposed OGSO based data disaster recovery process is better than other approaches.

Keywords

Cloud computing Data disaster Oppositional group search optimizer Data backup Replica Data recovery 

References

  1. Ahmed S, Maria S (2014) Cloud computing: paradigms and technologies. Springer, Berlin, Heidelberg, pp 39–67.  https://doi.org/10.1007/978-3-642-35016-0_2 Google Scholar
  2. Arul Mary A, Chitra A (2017) Study on disaster recovery in cloud environment, computing and communication technologies (WCCCT),  https://doi.org/10.1109/WCCCT.2016.48
  3. Caraman MC, Moraru SA, Dan S, Grama C (2012) Continuous disaster tolerance in the IaaS clouds. In: 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), IEEE. pp 1226–1232.  https://doi.org/10.1109/OPTIM.2012.6231987
  4. Greenberg A, Hamilton J, Maltz DA, Patel P (2008) The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput Commun Rev 39(10):68–73.  https://doi.org/10.1145/1496091.1496103 CrossRefGoogle Scholar
  5. Grolinger K, Capretz MA, Mezghani E, Exposito E (2013, June) Knowledge as a service framework for disaster data management. In: Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) 2013 IEEE 22nd International Workshop on.  https://doi.org/10.1109/WETICE.2013.48
  6. Gu Y, Wang D, Liu C (2014) DR-Cloud: multi-cloud based disaster recovery service. Tsinghua Sci Technol 19(1):13–23.  https://doi.org/10.1109/TST.2014.6733204 CrossRefGoogle Scholar
  7. Gu F, Shaban K, Ghani N, Khan S, Naeini MR, Hayat MM, Assi C (2015) Survivable cloud network mapping for disaster recovery support. IEEE Trans Comput 64(8):2353–2366.  https://doi.org/10.1109/TC.2014.2360542 MathSciNetCrossRefGoogle Scholar
  8. Gupta B, Agrawal DP, Yamaguchi S (2016) Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global.  https://doi.org/10.4018/978-1-5225-0105-3 Google Scholar
  9. He S, Wu QH, Saunders JR (2006) A novel group search optimizer inspired by animal behavioral ecology. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC ‘06), pp. 1272–1278, Vancouver, Canada, July 2006.  https://doi.org/10.1109/CEC.2006.1688455
  10. Ibtihal M, Hassan N (2017) Homomorphic encryption as a service for outsourced images in mobile cloud computing environment. Int J Cloud Appl Comput 7(2):27–40.  https://doi.org/10.4018/IJCAC.2017040103 Google Scholar
  11. Kalantar MH, Rosenberg F, Doran J, Eilam T, Elder MD, Oliveira F, Roth T (2014) Weaver: language and runtime for software defined environments. IBM J Res Dev 58(2):10–11.  https://doi.org/10.1147/JRD.2014.2304865 Google Scholar
  12. Lenk A (2015) Cloud Standby deployment: a model-driven deployment method for disaster recovery in the cloud. In: 2015 IEEE 8th International Conference on Cloud Computing. IEEE. pp 933–940.  https://doi.org/10.1109/CLOUD.2015.127
  13. Li J, Huang X, Li J, Chen X, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25(8):2201–2210.  https://doi.org/10.1109/TPDS.2013.271 CrossRefGoogle Scholar
  14. Li J, Li J, Chen X, Jia C, Lou W (2015) Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans Comput 64(2):425–437.  https://doi.org/10.1109/TC.2013.208 MathSciNetCrossRefzbMATHGoogle Scholar
  15. Li P, Li J, Huang Z, Gao CZ, Chen WB, Chen K (2017a) Privacy-preserving outsourced classification in cloud computing. J Clust Comput.  https://doi.org/10.1007/s10586-017-0849-9 Google Scholar
  16. Li P, Li J, Huang Z, Li T, Gao CZ, Yiu SM, Chen K (2017b) Multi-key privacy-preserving deep learning in cloud computing. J Future Gener Comput Syst 74:76–85.  https://doi.org/10.1016/j.future.2017.02.006 CrossRefGoogle Scholar
  17. Li J, Zhang Y, Chen X, Xiang Y (2018) Secure attribute-based data sharing for resource-limited users in cloud computing. J Comput Secur 72:1–12.  https://doi.org/10.1016/j.cose.2017.08.007 CrossRefGoogle Scholar
  18. Liu C, Gu Y, Sun L, Yan B, Wang D (2009) RADMAD: High-reliability provision for large-scale deduplication archival storage systems. In: Proceedings of the 23rd International Conference on Supercomputing. pp 370–379.  https://doi.org/10.1145/1542275.1542327
  19. Pokharel M, Lee S, Park JS (2010) Disaster recovery for system architecture using cloud computing. In: Applications and the Internet (SAINT), 2010 10th IEEE/IPSJ International Symposium on IEEE. pp 304–307.  https://doi.org/10.1109/SAINT.2010.23
  20. Rokade S, Pable K, Suryavanshi P, Jhaveri S (2016) Data back up and recovery using Seed Block algorithm. Int J Adv Res Comput Commun Eng.  https://doi.org/10.17148/IJARCCE.2016.5359 Google Scholar
  21. Sindoori R, Preetha Pallavi V, Abinaya P (2013) An overview of disaster recovery in virtualization technology. J Artif Intell 6:60–67.  https://doi.org/10.3923/jai.2013.60.67 CrossRefGoogle Scholar
  22. Sree AD, Janagama D, Prakash MB (2016) A hybrid cloud approach for secure authorized de-duplication. IEEE Trans Parallel Distrib Syst 26(5):1206–1216.  https://doi.org/10.1109/TPDS.2014.2318320 Google Scholar
  23. Stergiou C, Psanni KE, Kim BG, Gupta B (2018) Secure integration of IoT and cloud computing. J Future Gener Comput Syst 78:964–975.  https://doi.org/10.1016/j.future.2016.11.031 CrossRefGoogle Scholar
  24. Suguna S, Suhasini A (2014) Overview of data backup and disaster recovery in cloud. Inf Commun Embed Syst (ICICES).  https://doi.org/10.1109/ICICES.2014.7033804 Google Scholar
  25. Suguna S, Suhasini A (2015) Enriched multi-objective optimization model based cloud disaster recovery. Karbala Int J Modern Sci 1(2):122–128.  https://doi.org/10.1016/j.kijoms.2015.09.001 CrossRefGoogle Scholar
  26. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation. Vienna, Austria, November. Vol 1, pp 695–701.  https://doi.org/10.1109/CIMCA.2005.1631345

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Bharathidhasan UniversityTamil NaduIndia
  2. 2.Faculty of Computer ScienceGovt Arts and Science CollegeTamil NaduIndia

Personalised recommendations