Reliable and Energy Efficient Deployment Optimization of Internet of Things Applications in Cloud and Fog Infrastructure by Using Cuckoo Search Algorithm
Subject Areas : electrical and computer engineeringYaser Ramzanpoor 1 , میرسعید حسینی شیروانی 2 *
1 -
2 -
Keywords: Reliable deployment, energy-efficient, fog and cloud computing, IoT applications, distributed applications,
Abstract :
Deployment applications of internet of things (IoT) in fog infrastructure as cloud complementary leads effectively computing resource saving in cloud infrastructure. Recent research efforts are investigating on how to better exploit fog capabilities for execution and supporting IoT applications. Also, the distribution of an application’s components on the possible minimum number of fog nodes for the sake of reduction in power consumption leads degradation of the service reliability level. In this paper, a hybrid meta-heuristic algorithm based on cuckoo search algorithm is presented for static deployment the components of IoT applications on fog infrastructure in the aim of trade-off between efficient power usage, reduction in the effect of one point of failure and boosting the application reliability against failure. The results of simulations show that the proposed approach in this paper reduces the power consumption of fog network and meets the quality of service requirement of IoT application with the high reliability level.
[1] OpenFog Consortium Architecture Working Group, An OpenFog Architecture Overview, Available: https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf, 2017.
[2] S. Azimi, C. Pahl, and M. Hosseini Shirvani, "Particle swarm optimization for performance management in multi-cluster IoT edge architectures," in Proc. 10th Int. Conf. on .Cloud Computing and Services Science, pp. 328-337, Prague, Czech Republic, 7-9 May 2020.
[3] M. Hosseini Shirvani, "A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems," Engineering Applications of Artificial Intelligence, vol. 90, Article ID: 103501, Apr. 2019.
[4] H. Hong, P. Tsai, and C. Hsu, "Dynamic module deployment in a fog computing platform," in Proc 18th Asia-Pacific Network Operations and Management Symp., 6 pp., Kanazawa, Japan, 5-7 Oct. 2016.
[5] M. Taneja and A. Davy, "Resource-aware placement of IoT application modules in fog-cloud computing paradigm," in Proc IFIP/IEEE Symp. on Integrated Network and Service Management, pp. 1222-1228, Lisbon, Portugal, 8-12 May 2017.
[6] S. Venticinque and A. Amato, "A methodology for deployment of IoT application in fog," J. of Ambient Intelligence and Humanized Computing, vol. 10, no. 2, pp. 1955-1976, May 2019.
[7] A. Brogi and A. Forti, "QoS-aware deployment of IoT applications through the fog," IEEE Internet of Things J., vol. 4, no. 5, pp. 1185-1192, Oct. 2017.
[8] R. Mahmud, K. Ramamohanarao, and R. Buyya, "Latency-aware application module management for fog computing environments," ACM Trans. on Internet Technology, vol. 19, no. 1, Article No.: 9, pp. 1-21, Feb. 2018.
[9] M. Vogler, J. M. Schleicher, C. Inzinger, and S. Dustdar, "DIANE-dynamic IoT application deployment," in Proc IEEE Int. Conf. on Mobile Services, pp. 298-305, New York, NY, USA, 27 Jun.-2 Jul. 2015.
[10] E. Saurez, K. Hong, D. Lillethun, U. Ramachandran, and B. Ottenwalder, "Incremental deployment and migration of geo-distributed situation awareness applications in the fog," in Proc 10th ACM Int. Conf. on Distributed and Event-based Systems, pp. 258-269, Irvine, CA, USA, 20-24 Jun. 2016.
[11] U. Arora and N. Singh, "IoT application modules placement in heterogeneous fog-cloud," International J. of Information Technology, vol. 13, no. 9, pp. 1975-1982, May 2021.
[12] S. Omer, S. Azizi, M. Shojafar, and R. Tafazolli, "A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers," J. of Systems Architecture, vol. 15, Article ID: 101996, May 2021.
[13] I. F. Akyildiz, X. Wang, and W. Wang, "Wireless mesh networks: a survey," Computer Networks, vol. 47, no. 4, pp. 445-487, 15 Mar. 2005.
[14] J. P. Arcangeli, R. Boujbel, and S. Leriche, "Automatic deployment of distributed software systems: definitions and state of the art," The J. of Systems and Software, vol. 3pp. 198-218, May 2015.
[15] F. Bonomi, R. Milito, P. Natarajan, and J. Zhu, "Fog computing: a platform for Internet of Things and analytics," In: N. Bessis and C. Dobre (eds), Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham, pp. 169-186, 2014.
[16] S. Farzai, M. Hosseini Shirvani, and M. Rabbani, "Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters," Sustainable Computing: Informatics and Systems, vol. 28, Article ID: 100374, Dec. 2020.
[17] X. S. Yang and S. Deb, "Cuckoo search via Le´vy flights," in Proc. of World Congress on Nature & Biologically Inspired Computing, pp. 210-214, Coimbatore, India, 9-11 Dec. 2009.
[18] S. M. Sait, A. Bala, and A. H. El-Maleh, "Cuckoo search based resource optimization of datacenters," Applied Intelligence, vol. 44, no. 3, pp. 489-506, Apr. 2016.
[19] M. Tavana, S. Shahdi-Pashaki, E. Teymourian, F. J. Santos-Arteaga, and M. Komaki, "A discrete cuckoo optimization algorithm for consolidation in cloud computing," Computers & Industrial Engineering, vol. 115, pp. 495-511, Jan. 2017.
[20] M. Hosseini Shirvani and S. Farzai, "Optimal deployment of IoT application components on hybrid fog2cloud infrastructure for reduction of power consumption toward green computing by cuckoo search algorithm," in Proc.of the 1st National Conf. of New Development in Green Studies, Computations, Applications, and Challenges, 10 pp. Noor, Iran, 10-11 Sept.. 2020.
[21] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in Engineering Software, vol. 69?, pp. 46-61, Mar. 2014.
[22] C. G. Martínez, F. J. Rodriguez, and M. Lozano, "Genetic Algorithms," in Handbook of Heuristics, Springer, pp. 431-464, 2018.
[23] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of the IEEE Int. Conf. on Neural Networks, pp. 1942-1948, Perth, Australia, 27 Nov.-1 Dec. 1995.