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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arul Mary, A., Chitra, K. OGSO-DR: oppositional group search optimizer based efficient disaster recovery in a cloud environment. J Ambient Intell Human Comput 10, 1885–1895 (2019). https://doi.org/10.1007/s12652-018-0781-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0781-8