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Constrained Cuckoo Search Optimization Based Protocol for Routing in Cloud Network


Affiliations
1 PG and Research Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
2 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India
3 Department of Computer Technology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
 

Cloud Computing (CC) is the process of providing on-demand data to the user via the internet. In CC, users don't need to manage data storage and computational power actively. Finding the best route in a cloud network is entirely different from other general networks which it is due to high scalability. Protocols developed for other general networks will never suit or give better performance in cloud networks due to its scalability. This paper proposes a bio-inspired protocol for routing in a cloud network, namely Constrained Cuckoo Search Optimization-based Protocol (CCSOP). The routing strategy of CCSOP is inspired by the natural characteristics of the cuckoo bird towards finding a nest to lay its eggs. Levy Flight concept is applied with different constraints to enhance optimization performance towards finding the best route in a cloud network that minimizes energy consumption. CCSOP is evaluated in Greencloud using benchmark network performance metrics against the current routing protocols. The efficacy of CCSOP is evaluated using benchmark performance measures. CCSOP appears to outperform current cloud network routing protocols in terms of energy consumption.

Keywords

Cuckoo, Cloud, Energy, Flight, Levy, Optimization, Routing, Scalability.
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  • 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, “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.

  • J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.

  • M. Faheem, R. A. Butt, R. Ali, B. Raza, M. A. Ngadi, and V. C. Gungor, “CBI4.0: A Cross-layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0,” J. Ind. Inf. Integr., p. 100236, 2021, doi: https://doi.org/10.1016/j.jii.2021.100236.

  • C. Y. Huang and Y. J. Chang, “An adaptively multi-attribute index framework for big IoT data,” Comput. Geosci., p. 104841, 2021, doi: https://doi.org/10.1016/j.cageo.2021.104841.

  • J. Qu, “Research on mobile learning in a teaching information service system based on a big data driven environment,” Educ. Inf. Technol., pp. 1–19, Jun. 2021, doi: 10.1007/s10639-021-10614-z.

  • R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.

  • 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, “Improved Wolf prey inspired protocol for routing in cognitive radio Ad Hoc networks,” Int. J. Comput. Networks Appl., vol. 7, no. 5, pp. 126–136, 2020, doi: 10.22247/ijcna/2020/202977.

  • 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.

  • R. R. Hoy, “Quantitative skills in undergraduate neuroscience education in the age of big data,” Neurosci. Lett., p. 136074, 2021, doi: https://doi.org/10.1016/j.neulet.2021.136074.

  • P. L. Martínez, R. Dintén, J. M. Drake, and M. Zorrilla, “A big data-centric architecture metamodel for Industry 4.0,” Futur. Gener. Comput. Syst., 2021, doi: https://doi.org/10.1016/j.future.2021.06.020.

  • M. Rhahla, S. Allegue, and T. Abdellatif, “Guidelines for GDPR compliance in Big Data systems,” J. Inf. Secur. Appl., vol. 61, p. 102896, 2021, doi: https://doi.org/10.1016/j.jisa.2021.102896.

  • A. Sevtsuk, R. Basu, X. Li, and R. Kalvo, “A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco,” Travel Behav. Soc., vol. 25, pp. 41–51, 2021, doi: https://doi.org/10.1016/j.tbs.2021.05.010.

  • V. Keskar, J. Yadav, and A. Kumar, “Perspective of anomaly detection in big data for data quality improvement,” Mater. Today Proc., 2021, doi: https://doi.org/10.1016/j.matpr.2021.05.597.

  • T. G. Kim and S. Yu, “Big Data Analysis of the Risk of Intracranial Hemorrhage in Korean Populations Taking Low-Dose Aspirin,” J. Stroke Cerebrovasc. Dis., vol. 30, no. 8, p. 105917, 2021, doi: https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.105917.

  • D. Balazka, D. Houtman, and B. Lepri, “How can big data shape the field of non-religion studies? And why does it matter?,” Patterns, vol. 2, no. 6, p. 100263, 2021, doi: https://doi.org/10.1016/j.patter.2021.100263.

  • Y. Su and X. Wang, “Innovation of Agricultural Economic Management in the Process of Constructing Smart Agriculture by Big Data,” Sustain. Comput. Informatics Syst., p. 100579, 2021, doi: https://doi.org/10.1016/j.suscom.2021.100579.

  • C. Wen, J. Yang, L. Gan, and Y. Pan, “Big data driven Internet of Things for credit evaluation and early warning in finance,” Futur. Gener. Comput. Syst., vol. 124, pp. 295–307, 2021, doi: https://doi.org/10.1016/j.future.2021.06.003.

  • M. Nilashi et al., “Big social data and customer decision making in vegetarian restaurants: A combined machine learning method,” J. Retail. Consum. Serv., vol. 62, p. 102630, 2021, doi: https://doi.org/10.1016/j.jretconser.2021.102630.

  • N. B. Long, H. Tran-Dang, and D. Kim, “Energy-Aware Real-Time Routing for Large-Scale Industrial Internet of Things,” IEEE Internet Things J., vol. 5, no. 3, pp. 2190–2199, 2018, doi: 10.1109/JIOT.2018.2827050.

  • Y. Xu, Z. Yue, and L. Lv, “Clustering Routing Algorithm and Simulation of Internet of Things Perception Layer Based on Energy Balance,” IEEE Access, vol. 7, pp. 145667–145676, 2019, doi: 10.1109/ACCESS.2019.2944669.

  • H. A. Omar, W. Zhuang, and L. Li, “Gateway Placement and Packet Routing for Multihop In-Vehicle Internet Access,” IEEE Trans. Emerg. Top. Comput., vol. 3, no. 3, pp. 335–351, 2015, doi: 10.1109/TETC.2015.2395077.

  • Z. Ding, L. Shen, H. Chen, F. Yan, and N. Ansari, “Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs,” IEEE Internet Things J., vol. 7, no. 9, pp. 9050–9065, 2020, doi: 10.1109/JIOT.2020.3002233.

  • J. V. V Sobral, J. J. P. C. Rodrigues, R. A. L. Rabêlo, K. Saleem, and S. A. Kozlov, “Improving the Performance of LOADng Routing Protocol in Mobile IoT Scenarios,” IEEE Access, vol. 7, pp. 107032– 107046, 2019, doi: 10.1109/ACCESS.2019.2932718.

  • T. Mick, R. Tourani, and S. Misra, “LASeR: Lightweight Authentication and Secured Routing for NDN IoT in Smart Cities,” IEEE Internet Things J., vol. 5, no. 2, pp. 755–764, 2018, doi: 10.1109/JIOT.2017.2725238.

  • Q. Zhang, M. Jiang, Z. Feng, W. Li, W. Zhang, and M. Pan, “IoT Enabled UAV: Network Architecture and Routing Algorithm,” IEEE Internet Things J., vol. 6, no. 2, pp. 3727–3742, 2019, doi:10.1109/JIOT.2018.2890428.

  • Z. Zhou, B. Yao, R. Xing, L. Shu, and S. Bu, “E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things,” IEEE Sens. J., vol. 16, no. 11, pp. 4072–4082, 2016, doi: 10.1109/JSEN.2015.2437904.

  • C. Wang, L. Zhang, Z. Li, and C. Jiang, “SDCoR: Software Defined Cognitive Routing for Internet of Vehicles,” IEEE Internet Things J., vol. 5, no. 5, pp. 3513–3520, 2018, doi: 10.1109/JIOT.2018.2812210.

  • K. Z. Ghafoor, L. Kong, D. B. Rawat, E. Hosseini, and A. S. Sadiq, “Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles,” IEEE Internet Things J., vol. 6, no. 2, pp. 2817–2828, 2019, doi: 10.1109/JIOT.2018.2875482.

  • W. Itani, C. Ghali, R. Bassil, A. Kayssi, and A. Chehab, “ServBGP: BGP-inspired autonomic service routing for multi-provider collaborative architectures in the cloud,” Futur. Gener. Comput. Syst., vol. 32, pp. 99–117, 2014, doi: https://doi.org/10.1016/j.future.2012.05.013.

  • 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: https://doi.org/10.1016/j.adhoc.2015.06.008.

  • S. Kaja, E. M. Shakshuki, and A. Yasar, “Long Short-Term Memory Approach for Routing Optimization in Cloud ACKnowledgement Scheme for Node Network,” Procedia Comput. Sci., vol. 184, pp. 461–468, 2021, doi: https://doi.org/10.1016/j.procs.2021.03.058.

  • X. Peng and Y. Chang, “Energy-efficient routing technique for reliable data transmission under the background of big data for disaster region,” Comput. Intell., vol. 36, no. 4, 2020, doi: 10.1111/coin.12294.

  • 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.


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  • Constrained Cuckoo Search Optimization Based Protocol for Routing in Cloud Network

Abstract Views: 304  |  PDF Views: 1

Authors

J. Ramkumar
PG and Research Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
R. Vadivel
Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India
B. Narasimhan
Department of Computer Technology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India

Abstract


Cloud Computing (CC) is the process of providing on-demand data to the user via the internet. In CC, users don't need to manage data storage and computational power actively. Finding the best route in a cloud network is entirely different from other general networks which it is due to high scalability. Protocols developed for other general networks will never suit or give better performance in cloud networks due to its scalability. This paper proposes a bio-inspired protocol for routing in a cloud network, namely Constrained Cuckoo Search Optimization-based Protocol (CCSOP). The routing strategy of CCSOP is inspired by the natural characteristics of the cuckoo bird towards finding a nest to lay its eggs. Levy Flight concept is applied with different constraints to enhance optimization performance towards finding the best route in a cloud network that minimizes energy consumption. CCSOP is evaluated in Greencloud using benchmark network performance metrics against the current routing protocols. The efficacy of CCSOP is evaluated using benchmark performance measures. CCSOP appears to outperform current cloud network routing protocols in terms of energy consumption.

Keywords


Cuckoo, Cloud, Energy, Flight, Levy, Optimization, Routing, Scalability.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F210727