Abstract
A lot of scientific problems in various domains from modelling sky as mosaics to understanding Genome sequencing in biological applications are modelled as workflows with a large number of interconnected tasks. Even though many works are cited in the literature on workflow scheduling, most of the existing works are focused on reducing the makespan alone. Moreover, energy efficiency is considered only in a few works included in the literature. Constraints about the dynamic workload allocation are not introduced in the existing systems. Moreover, the optimization techniques used in the existing systems have improved the QoS with little scalability in the cloud environment since they consider only the infrastructure as the service model. In this work, a new algorithm has been proposed based on the proposal of a new Multi-Objective Optimization model called F-NSPSO using NSPSO Meta-heuristics. This method allows the user to choose a suitable configuration dynamically. When compared to NSPSO an energy reduction of at least 10% has been observed for F-NSPSO for Montage, Cybershake, and Epigenomics workflow applications. Compared to the NSPSO algorithm F-NSPSO algorithm shows at least 13%, 12%, and 21% improvement in average makespan for Montage, Cybershake, and Epigenomics workflow applications respectively.
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Delforge, P., & Whitney, J. (2014). Issue paper: Data centre efficiency assessment scaling up energy efficiency across the data centre industry: Evaluating key drivers and barriers. Natural Resources Defense Council (NRDC).
Sareh, F. P. (2016). Energy-efficient management of resources in enterprise and container based clouds. The University of Melbourne.
Dong, F., & Selim, G.A. (2006). Scheduling algorithms for grid computing: State of the art and open problems. School of Computing, Queen's University, Kingston, Ontario. Technical Report No. 2006-504.
Yu, J., Buyya, R., & Ramamohanarao, K. (2008). ‘Workflow scheduling algorithms for grid computing’, Metaheuristics for scheduling in distributed computing environments, studies. Computational Intelligence, 146, 173–214.
Abrishami, S., Naghibzadeh, M., & Epema, D. H. (2013). Deadline constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Generation Computer Systems, 29(1), 158–169.
Ferdaus, M. H., Murshed, M. M., Calheiros, R. N., & Buyya, R. (2014).Virtual machine consolidation in cloud data centers using aco metaheuristic. In Proceedings of European conference on parallel processing, Springer, pp. 306–317.
Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. Journal of the Supercomputing, 60(2), 268–280.
Mohanapriya, N., Kousalya, G., Balakrishnan, P., & Pethuru Raj, C. (2018). Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. Journal of Intelligent & Fuzzy Systems, 34(3), 1561–1572.
Li, J., Li, Y. K., Chen, X., Lee, P. P., & Lou, W. (2015). A hybrid cloud approach for secure authorized deduplication. IEEE Transactions on Parallel and Distributed Systems, 26(5), 1206–1216.
Pietri, I., & Sakellariou, R. (2014) Cost-efficient provisioning of cloud resources priced by CPU frequency. In Proceedings of the IEEE/ACM Seventh international conference on utility and cloud computing, pp. 483–484.
Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S. U., & Li, K. (2016). An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. Journal of Grid Computing, 14(1), 55–74.
Wang, L., Von Laszewski, G., Dayal, J., & Wang, F. (2010). Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with DVFS. In Proceedings of the 10th IEEE/ACM international conference on cluster, cloud and grid computing, pp. 368–377.
Durillo, J. J., Fard, H. M., & Prodan, R. (2012). Moheft: A multi-objective list-based method for workflow scheduling. In Proceedings of the 4th international conference in cloud computing technology and science (CloudCom), pp. 185–192.
Yassa, S., Chelouah, R., Kadima, H., & Granado, B. (2013). Multiobjective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal, 2013, 1–1138.
Zhu, Z., Zhang, G., Li, M., & Liu, X. (2016). Evolutionary multi-objective workflow scheduling in cloud. IEEE Transactions On Parallel And Distributed Systems, 27(5), 1344–1357.
Reyes, S. M., & Coello, C. C. (2006). ‘Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International journal of computational intelligence research, 2(3), 287–308.
Coello, C. A. C. (2011) An introduction to multi-objective particle swarm optimizers. In Soft computing in industrial applications, pp. 3–12.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Network, 1000, 1942–1948.
Li, X. (2003) A non-dominated sorting particle swarm optimizer for multi-objective optimization. In Proceedings of the genetic and evolutionary computation (GECCO), Springer pp. 198–198.
Fernandez, N., Alfonso, H. & Gallard, R.H. (2000). Crowding under diverse distance criteria for niche formation in multimodal optimization. Journal of Computer Science & Technology, 1(3).
Subashini, G., & Bhuvaneswari, M. C. (2011). ‘Non dominated particle swarm optimization for scheduling independent tasks on heterogeneous distributed environments. International Journal of Advance Soft Computing Application, 3(1), 1–17.
Wakil, K., Badfar, A., Dehghani, P., Shoja Sadati, S. M., & Jafari Navimipour, N. (2019). A fuzzy logic-based method for solving the scheduling problem in the cloud environments using a non-dominated sorted algorithm. Concurrency and Computation: Practice and Experience, 31(17), e5185.
Garg, R. & Singh, A. K. (2011). Multi-objective workflow grid scheduling based on discrete particle swarm optimization. In Proceedings of the international conference on swarm, evolutionary, and memetic computing, Springer, pp. 183–190.
Beloglazov, A., Buyya, R., Lee, Y., & Zomaya, C. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in computers, 82(2), 47–111.
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Soma, P., Latha, B. & Vijaykumar, V. An Improved Multi-Objective Workflow Scheduling Using F-NSPSO with Fuzzy Rules. Wireless Pers Commun 124, 3567–3589 (2022). https://doi.org/10.1007/s11277-022-09526-z
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DOI: https://doi.org/10.1007/s11277-022-09526-z