CFP last date
22 April 2024
Reseach Article

Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment

by Bhuvnesh Pathania, Abhilash Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 3
Year of Publication: 2017
Authors: Bhuvnesh Pathania, Abhilash Sharma
10.5120/ijca2017913603

Bhuvnesh Pathania, Abhilash Sharma . Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment. International Journal of Computer Applications. 164, 3 ( Apr 2017), 37-41. DOI=10.5120/ijca2017913603

@article{ 10.5120/ijca2017913603,
author = { Bhuvnesh Pathania, Abhilash Sharma },
title = { Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 3 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number3/27466-2017913603/ },
doi = { 10.5120/ijca2017913603 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:18.115824+05:30
%A Bhuvnesh Pathania
%A Abhilash Sharma
%T Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 3
%P 37-41
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper represents the Parallel Computing is now extremely popular because of its wide variety of applications through internet. The particular service based approaches which are aware from the server selection through the parallel can easily progress toward the cost and performance of parallel computing. A new hybrid DLBS Artificial bee colony optimization algorithm for parallel computing environment has been done. The overall objective of this paper is enlighten the performance analysis on dynamic load balancing strategy (DLBS) with ABC algorithm which enhances the results further by using mean flow time parameter.

References
  1. Wang, Lizhe, et al. "Energy-aware parallel task scheduling in a cluster." Future Generation Computer Systems 29.7 (2013): 1661-1670.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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).
  19. Priya, S. Baghavathi, M. Prakash, and K. K. Dhawan. "Fault tolerance-genetic algorithm for grid task scheduling using check point." Grid and Cooperative Computing, 2007. GCC 2007. Sixth International Conference on. IEEE, 2007.
  20. Li, S., Li, G., Wang, X., & Liu, Q. (2005). Minimizing makespan on a single batching machine with release times and non-identical job sizes. Operations Research Letters, 33(2), 157-164.
  21. Lorpunmanee, S., Sap, M. N., Abdullah, A. H., & Chompoo-inwai, C. (2007). An ant colony optimization for dynamic job scheduling in grid environment. International Journal of Computer and Information Science and Engineering, 1(4), 207-214.
  22. Bokhari, M. U., Mahfooz Alam, and Faraz Hasan. "Performance analysis of dynamic load balancing algorithm for multiprocessor interconnection network." Perspectives in Science 8 (2016): 564-566.
Index Terms

Computer Science
Information Sciences

Keywords

Parallel computing Scheduling System DLBS Ant Colony optimization Artificial Bee Colony