Abstract
Cloud computing technology is making advancement recently. Automated service provisioning, load balancing, virtual machine task migration, algorithm complexity, resource allocation, and scheduling are used to make improvements in the quality of service in the cloud environment. Load balancing is an NP-hard problem. The main objective of the proposed work is to achieve low makespan and minimum task execution time. An experimental result proved that the proposed algorithm performs good load balancing than Firefly algorithm, Honey Bee Behavior-inspired Load Balancing (HBB-LB), and Particle Swarm Optimization (PSO) algorithm.
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References
Joshi, G., Verma, S.K.: Load balancing approach in cloud computing using improvised genetic algorithm: a soft computing approach. Int. J. Comput. Appl. 122(9), 24–28 (2015)
Mahmoud, M.M.E.A., Shen, X.: A cloud-based scheme for protecting source-location privacy against hotspot-locating attack in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(10), 1805–1818 (2012)
Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high performance computing data centers: a cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)
TSai, P.W., Pan, J.S., Liao, B.Y.: Enhanced artificial bee colony optimization. Int. J. Innov. Comput. Inf. Control 5(12), 5081–5092 (2009)
Kashyap, D., Viradiya, J.: A survey of various load balancing algorithms in cloud computing. Int. J. Sci. Technol. 3(11), 115–119 (2014)
Zhu, H., Liu, T., Zhu, D., Li, H.: Robust and simple N-Party entangled authentication cloud storage protocol based on secret sharing scheme. J. Inf. Hiding Multimedia Signal Process. 4(2), 110–118 (2013)
Chang, B., Tsai, H.-F., Chen, C.-M.: Evaluation of virtual machine performance and virtualized consolidation ratio in cloud computing system. J. Inf. Hiding Multimedia Signal Process. 4(3), 192–200 (2013)
Florence, A.P., Shanthi, V.: A load balancing model using firefly algorithm in cloud computing. J. Comput. Sci. 10(7), 1156 (2014)
Polepally, V., Shahu Chatrapati, K.: Dragonfly optimization and constraint measure based load balancing in cloud computing. Cluster Comput. 20(2), 1–13 (2017)
Mei, J., Li, K., Li, K.: Energy-aware task scheduling in heterogeneous computing environments. Cluster Comput. 17(2), 537–550 (2014)
Kaur, P., Kaur, P.D.: Efficient and enhanced load balancing algorithms in cloud computing. Int. J. Grid Distrib. Comput. 8(2), 9–14 (2015)
Chen, S.-L., Chen, Y.-Y., Kuo, S.-H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 56(2), 154–160 (2016)
Priyadarsini, R.J., Arockiam, L.: Performance evaluation of min-min and max-min algorithms for job scheduling in federated cloud. Int. J. Comput. Appl. 99(18), 47–54 (2014)
Pacini, E., Mateos, C., GarcÃa Garino, C.: Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron. J. 17(1), 3–13 (2014)
Chen, S.-L., Chen, Y.-Y., Kuo, S.-H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58(1), 154–160 (2016)
Aruna, M., Bhanu, D., Karthik, S.: An improved load balanced metaheuristic scheduling in cloud. Cluster Comput. 5(7), 1107–1111 (2015)
Dhinesh Babu, L.D., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)
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Sakthivelmurugan, V., Vimala, R., Aravind Britto, K.R. (2019). Star Hotel Hospitality Load Balancing Technique in Cloud Computing Environment. In: Peter, J., Alavi, A., Javadi, B. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 750. Springer, Singapore. https://doi.org/10.1007/978-981-13-1882-5_10
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DOI: https://doi.org/10.1007/978-981-13-1882-5_10
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