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Davinderjit Kaur
Er.Amit chabbra


This paper has focused on a new Meta heuristic technique i.e. GA+ FIREFLY Hybrid algorithm for parallel job scheduling problem. It has been observed that in existing literature has introduced genetic algorithm which solve parallel job scheduling problem but the genetic algorithm suffers from local optima problem. Moreover it converges slowly so more time it takes to provide the final results. In order to eliminate this problem further improvement has been required to get the sub optimal solution as well as Firefly algorithm works on global optima. It is flexible, robust. Moreover, it uses few parameters as compared to GA and it can be easily hybridized with GA. This research has proposed the hybridisation of GA and Firefly which has done the work on various parameters like make span, flow time, mean waiting time, normalization function etc. The experimental results will also be drawn in order to find the best decomposition among the available one.


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Wang, Lizhe, et al. "Energy-aware parallel task scheduling in a cluster." Future Generation Computer Systems 29.7 (2013): 1661-1670.

Arsuaga-Ríos, María, and Miguel A. Vega-Rodríguez. "Energy optimization for task scheduling in distributed systems by an Artificial Bee Colony approach." Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on. IEEE, 2014.

Javanmardi, Saeed, et al. "Hybrid job scheduling algorithm for cloud computing environment." Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Springer International Publishing, 2014.

Zhao, Jianfeng, and Hongze Qiu. "Genetic algorithm and ant colony algorithm based Energy-Efficient Task Scheduling." Information Science and Technology (ICIST), 2013 International Conference on. IEEE, 2013.

Javanmardi, S., Shojafar, M., Amendola, D., Cordeschi, N., Liu, H., & Abraham, A. (2014). Hybrid job scheduling algorithm for cloud computing environment. In Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 (pp. 43-52). Springer International Publishing.

Liu, J., Luo, X. G., Zhang, X. M., Zhang, F., & Li, B. N. (2013). Job scheduling model for cloud computing based on multi-objective genetic algorithm. IJCSI International Journal of Computer Science Issues, 10(1), 134-139.

Liu, S. L., Liu, Y. X., Zhang, F., Tang, G. F., & Jing, N. (2007). Dynamic web services selection algorithm with QoS global optimal in web services composition. Ruan Jian Xue Bao(Journal of Software), 18(3), 646-656.

Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., & Tenhunen, H. (2015). Using ant colony system to consolidate vms for green cloud computing. IEEE Transactions on Services Computing, 8(2), 187-198.

Masdari, M., ValiKardan, S., Shahi, Z., & Azar, S. I. (2016). Towards workflow scheduling in cloud computing: A comprehensive analysis. Journal of Network and Computer Applications, 66, 64-82.

Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A. Y., Talbi, E. G., & Bouvry, P. (2014). A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustainable Computing: Informatics and Systems, 4(4), 252-261.

Civicioglu, Pinar, and Erkan Besdok. "A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms." Artificial intelligence review 39.4 (2013): 315-346.

Sajedi, Hedieh, and Maryam Rabiee. "A metaheuristic algorithm for job scheduling in grid computing." International Journal of Modern Education and Computer Science 6.5 (2014): 52.

Ferrandi, F., Lanzi, P. L., Pilato, C., Sciuto, D., & Tumeo, A. (2010). Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 29(6), 911-924.

Alkhanak, Ehab Nabiel, Sai Peck Lee, and Saif Ur Rehman Khan. "Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities." Future Generation Computer Systems 50 (2015): 3-21.

Li, Jun-Qing, Quan-Ke Pan, and Kai-Zhou Gao. "Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems." The International Journal of Advanced Manufacturing Technology 55.9-12 (2011): 1159-1169.

Jia, H. Z., Nee, A. Y., Fuh, J. Y., & Zhang, Y. F. (2003). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing, 14(3-4), 351-362.

Pinel, F., Dorronsoro, B., Pecero, J. E., Bouvry, P., & Khan, S. U. (2013). A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster Computing, 16(3), 421-433.

Bilgaiyan, Saurabh, Santwana Sagnika, and Madhabananda Das. "An analysis of task scheduling in cloud computing using evolutionary and swarm-based algorithms." International Journal of Computer Applications 89.2 (2014).