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Gunasekaran, S. A.
- Lucid Firefly Based Routing Protocol (LFRP) for Accessing Big Data in Cloud
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Affiliations
1 Department of Computer Applications, Coimbatore Institute of Technology, Coimbatore, IN
2 Department of Computer Science, Government Arts & Science College, Avinashi, Tirupur, IN
1 Department of Computer Applications, Coimbatore Institute of Technology, Coimbatore, IN
2 Department of Computer Science, Government Arts & Science College, Avinashi, Tirupur, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 3 (2021), Pagination: 228-237Abstract
Minimizing energy consumption is a significant issue in cloud computing. Nodes present in cloud computing are heterogeneous in nature. Traditional routing protocols fit best for homogenous networks and while using in heterogeneous network it will never give its better performance. Accessing big data in cloud is a challenging task because more stable route is necessary for the access of big data. Routes failures are unexpected and if a route gets failed in cloud computing while accessing big data, then it will affect the network performance drastically. In this paper, Lucid Firefly based Routing Protocol (LFRP) is proposed to identify the optimized route to access the big data and to minimize the energy consumption. LFRP utilizes the natural characteristics of firefly to identify the best route and to share the identified best route with others. LFRP finds the route based on the size of data where the fitness function plays a major role in identifying the best route. The simulation results make an indication that the proposed routing protocol LFRP has consumed less amount of energy i.e., 3.95J in accessing the big data than other routing protocols which makes an indication that the routing protocol has found the better route to destination which faces low delay (65ms) and packet delivery ratio as 94.20%.Keywords
Big Data, Cloud Computing, Energy, Firefly, Lifetime, Network, Routing.Full Text
References
- X. Ma, Y. Wang, and X. Pei, “A Scalable and Reliable Matching Service for Content-Based Publish/Subscribe Systems,” IEEE Trans. Cloud Comput., vol. 3, no. 1, pp. 1–13, 2015, DOI: 10.1109/TCC.2014.2338327.
- A. M. Manasrah, T. Smadi, and A. ALmomani, “A Variable Service Broker Routing Policy for data center selection in cloud analyst,” J. King Saud Univ. - Comput. Inf. Sci., vol. 29, no. 3, pp. 365–377, 2017, DOI: 10.1016/j.jksuci.2015.12.006.
- Y. Wang, A. Zhang, P. Zhang, and H. Wang, “Cloud-Assisted EHR Sharing With Security and Privacy Preservation via Consortium Blockchain,” IEEE Access, vol. 7, pp. 136704–136719, 2019, DOI: 10.1109/ACCESS.2019.2943153.
- K. Saleem, A. Derhab, J. Al-Muhtadi, and M. A. Orgun, “Analyzing ant colony optimization based routing protocol against the hole problem for enhancing user’s connectivity experience,” Comput. Human Behav., vol. 51, pp. 1340–1350, Oct. 2015, DOI:10.1016/j.chb.2014.11.030.
- A. S. Albahri et al., “IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art,” J. Netw. Comput. Appl., vol. 173, p. 102873, 2021, DOI: 10.1016/j.jnca.2020.102873.
- S. A. Bello et al., “Cloud computing in construction industry: Use cases, benefits and challenges,” Autom. Constr., vol. 122, p. 103441, 2021, DOI: 10.1016/j.autcon.2020.103441.
- S. Meng, X. He, and X. Tian, “Research on Fintech development issues based on embedded cloud computing and big data analysis,” Microprocess. Microsyst., vol. 83, p. 103977, 2021, DOI: 10.1016/j.micpro.2021.103977.
- X. Li et al., “Curriculum Reform in Big Data Education at Applied Technical Colleges and Universities in China,” IEEE Access, vol. 7, pp. 125511–125521, 2019, DOI: 10.1109/ACCESS.2019.2939196.
- P. Li, S. Guo, S. Yu, and W. Zhuang, “Cross-Cloud MapReduce for Big Data,” IEEE Trans. Cloud Comput., vol. 8, no. 2, pp. 375–386, 2020, DOI: 10.1109/TCC.2015.2474385.
- L. Hu, Q. Ni, and F. Yuan, “Big data oriented novel background subtraction algorithm for urban surveillance systems,” Big Data Min. Anal., vol. 1, no. 2, pp. 137–145, 2018, DOI: 10.26599/BDMA.2018.9020013.
- C. Li and H. Dai, “Throughput Scaling of Primary and Secondary Ad Hoc Networks With Same-Order Dimensions,” IEEE Trans. Veh. Technol., vol. 63, no. 8, pp. 3957–3966, 2014, DOI: 10.1109/TVT.2014.2310424.
- C. Cramer, O. Stanze, K. Weniger, and M. Zitterbart, “Reactive clustering in MANETs,” Int. J. Pervasive Comput. Commun., vol. 2, no. 2, pp. 81–90, May 2007, DOI:10.1108/17427370780000143.
- A. Yassine, A. A. N. Shirehjini, and S. Shirmohammadi, “Bandwidth On-Demand for Multimedia Big Data Transfer Across Geo-Distributed Cloud Data Centers,” IEEE Trans. Cloud Comput., vol. 8, no. 4, pp. 1189–1198, 2020, DOI: 10.1109/TCC.2016.2617369.
- P. Sen, R. Prasad, and P. Saurabh, “A New Approach for Cloud Security Using Hybrid Querying System Over Cloud Scenario,” in Advances in Intelligent Systems and Computing, 2019, vol. 904, pp. 367–376, DOI: 10.1007/978-981-13-5934-7_33.
- J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, DOI:10.1108/WJE-08-2017-0260.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, DOI: 10.30534/ijeter/2020/82882020.
- L. Zhao, Z. Bi, M. Lin, A. Hawbani, J. Shi, and Y. Guan, “An intelligent fuzzy-based routing scheme for software-defined vehicular networks,” Comput. Networks, vol. 187, p. 107837, Mar. 2021, DOI: 10.1016/j.comnet.2021.107837.
- W. xi Liu, J. Cai, Q. C. Chen, and Y. Wang, “DRL-R: Deep reinforcement learning approach for intelligent routing in software-defined data-center networks,” J. Netw. Comput. Appl., vol. 177, p. 102865, Mar. 2021, DOI: 10.1016/j.jnca.2020.102865.
- M. S. Daas, S. Chikhi, and E.-B. Bourennane, “A dynamic multi-sink routing protocol for static and mobile self-organizing wireless networks: A routing protocol for Internet of Things,” Ad Hoc Networks, vol. 117, p. 102495, Jun. 2021, DOI: 10.1016/j.adhoc.2021.102495.
- X. Liu et al., “Adaptive data and verified message disjoint security routing for gathering big data in energy harvesting networks,” J. Parallel Distrib. Comput., vol. 135, pp. 140–155, Jan. 2020, DOI: 10.1016/j.jpdc.2019.08.012.
- X. Wang et al., “Building efficient probability transition matrix using machine learning from big data for personalized route prediction,” in Procedia Computer Science, Jan. 2015, vol. 53, no. 1, pp. 284–291, DOI: 10.1016/j.procs.2015.07.305.
- T. Baker, B. Al-Dawsari, H. Tawfik, D. Reid, and Y. Ngoko, “GreeDi: An energy efficient routing algorithm for big data on cloud,” Ad Hoc Networks, vol. 35, pp. 83–96, Dec. 2015, DOI: 10.1016/j.adhoc.2015.06.008.
- Y. Chen and J. Wu, “Joint coflow routing and scheduling in leaf-spine data centers,” J. Parallel Distrib. Comput., vol. 148, pp. 83–95, 2021, DOI: 10.1016/j.jpdc.2020.09.007.
- P. K. Dey and M. Yuksel, “An Economic Analysis of Cloud-Assisted Routing for Wider Area SDN,” IEEE Trans. Netw. Serv. Manag., vol. 17, no. 1, pp. 445–458, 2020, DOI: 10.1109/TNSM.2019.2947030.
- K. Wu, P. Lu, and Z. Zhu, “Distributed Online Scheduling and Routing of Multicast-Oriented Tasks for Profit-Driven Cloud Computing,” IEEE Commun. Lett., vol. 20, no. 4, pp. 684–687, 2016, DOI: 10.1109/LCOMM.2016.2526001.
- S. Xu, X. Wang, G. Yang, J. Ren, and S. Wang, “Routing optimization for cloud services in SDN-based Internet of Things with TCAM capacity constraint,” J. Commun. Networks, vol. 22, no. 2, pp. 145–158, 2020, DOI: 10.1109/JCN.2020.000006.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, DOI: 10.1007/s11277-021-08495-z.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, DOI: 10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “CSIP—Cuckoo Search Inspired Protocol for routing in Cognitive Radio Ad Hoc Networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, DOI: 10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” Indian Journal of Science and Technology., vol. 13, no. 30, pp. 3059–3069, 2020, DOI: 10.17485/IJST/v13i30.1152.