Cloud Computing consists of many resources, the problem of mapping tasks on unlimited computing resources in cloud computing is NP-hard optimization problem. In this paper, we provide a survey of popular nature inspired algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for solving NP-hard problems in cloud computing.
| Published in | International Journal of Intelligent Information Systems (Volume 5, Issue 5) |
| DOI | 10.11648/j.ijiis.20160505.11 |
| Page(s) | 60-64 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Ant Colony Optimization (ACO), Cloud Computing, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Metaheuristics
| [1] | Leena, A., Ajeena, S., and Rajasree, S. (2013). “Inter-cloud scheduling technique using power of two choices” in Proc. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-4. |
| [2] | Lakshmi, R., and Srinivasu, N. (2016). "A dynamic approach to task scheduling in cloud computing using genetic algorithm". Journal of Theoretical and Applied Information Technology, Vol. 85, No. 2, pp. 124-135. |
| [3] | Geetinder, K., and Sarabjit, K. (2016). "Improved Hyper-Heuristic Scheduling with Load-Balancing and RASA for Cloud Computing Systems ". International Journal of Grid and Distributed Computing, Vol. 9, No. 1, pp. 13-24. |
| [4] | Zhenzhen, X., Xiujuan, X., and Xiaowei, Z. (2015). "Task Scheduling Based on Multi-objective Genetic Algorithm in Cloud Computing". Journal of Information & Computational Science, Vol. 12, No. 4, pp. 1429-1438. |
| [5] | Parveen, K., and, Mandeep, K. (2015). "Load balancing in cloud using aco and genetic algorithm". International Journal of Scientific Research Engineering & Technology (IJSRET), Vol. 4, No. 7, pp. 724-730. |
| [6] | Murugesan, S., and, Bojanova, I. (2016). "Cloud Computing: An Overview". Encyclopedia of Cloud Computing, IEEE, pp. 3-14. |
| [7] | Kun, L., Gaochao, X., Guangyu, Z., Yushuang, D., and Dan, W., (2011). ” Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization”, IEEE Sixth Annual Chinagrid Conference, pp.3-9. |
| [8] | Xin, L., and Zilong, G. (2011). “A load adaptive cloud resource scheduling model based on ant colony algorithm”, IEEE International Conference on Cloud Computing and Intelligence Systems, IEEE, pp.296-300. |
| [9] | Pacinia, E, Mateosb, C, Garino, C. (2015). "Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization”, Advances in Engineering Software, Vol. 84, No. 1, pp. 31-47. |
| [10] | Pandey, S., Wu, L., Guru, S., and Buyya, R. (2010). “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments”, 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 400–407. |
| [11] | Zhanghui, L., and Xiaoli, W. (2012). “A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment”, Advances in Swarm Intelligence, Lecture Notes in Computer Science series, Springer, Vol. 7331; pp. 142-147. |
| [12] | Beegom, S., and Rajasree, S. (2014). “A particle swarm optimization based pareto optimal task scheduling in cloud computing”, Advances in Swarm Intelligence, Lecture Notes in Computer Science series, Springer,Vol.8795; pp.79-86. |
| [13] | Ramezani, F., Jie, L., and Hussain, K. (2014) “Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization”, International Journal of Parallel Programming, Vol. 42, No. 5, pp. 739–754. |
| [14] | Jang, S., Kim, T, Kim, K., and Lee, S. (2012). “The study of genetic algorithm-based task scheduling for cloud computing”. International Journal of Control and Automation, Vol. 5, No. 4, pp. 157–162. |
| [15] | Jing, L., Xing-Guo, L., Xing-Ming, Z., Fan, Z., and Bai-Nan, L. (2013). ”Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm”, International Journal of Computer Science Issues, Vol.10, No. 1, pp. 134-139. |
| [16] | Durairaj, M., and Kannan, P. (2015). ”Improvised Genetic Approach for an Effective Resource Allocation in Cloud Infrastructure”, International Journal of Computer Science and Information Technologies, Vol.6, No.4, pp. 4037-4046. |
| [17] | Leena, A., Ajeena, S., and Rajasree, S. (2016). ”Genetic Algorithm Based Bi-Objective Task Scheduling in Hybrid Cloud Platform”, International Journal of Computer Theory and Engineering, Vol. 8, No. 1, pp. 7-13. |
| [18] | Gamal Abd El-Nasser, S., Abeer, M., and El-Horbaty, S. (2014). “A Comparative Study of Metaheuristic Algorithms for Solving Quadratic Assignment Problem,” International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 5, No. 1, pp. 1-6. |
| [19] | Mala, K., and Sarbjeet, S. (2015). ”A review of metaheuristic scheduling techniques in cloud computing”, Egyptian Informatics Journal, Vol. 16, No. 1, pp. 275-295. |
APA Style
Gamal Abd El-Nasser A. Said. (2016). Nature Inspired Algorithms in Cloud Computing: A Survey. International Journal of Intelligent Information Systems, 5(5), 60-64. https://doi.org/10.11648/j.ijiis.20160505.11
ACS Style
Gamal Abd El-Nasser A. Said. Nature Inspired Algorithms in Cloud Computing: A Survey. Int. J. Intell. Inf. Syst. 2016, 5(5), 60-64. doi: 10.11648/j.ijiis.20160505.11
AMA Style
Gamal Abd El-Nasser A. Said. Nature Inspired Algorithms in Cloud Computing: A Survey. Int J Intell Inf Syst. 2016;5(5):60-64. doi: 10.11648/j.ijiis.20160505.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijiis.20160505.11,
author = {Gamal Abd El-Nasser A. Said},
title = {Nature Inspired Algorithms in Cloud Computing: A Survey},
journal = {International Journal of Intelligent Information Systems},
volume = {5},
number = {5},
pages = {60-64},
doi = {10.11648/j.ijiis.20160505.11},
url = {https://doi.org/10.11648/j.ijiis.20160505.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160505.11},
abstract = {Cloud Computing consists of many resources, the problem of mapping tasks on unlimited computing resources in cloud computing is NP-hard optimization problem. In this paper, we provide a survey of popular nature inspired algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for solving NP-hard problems in cloud computing.},
year = {2016}
}
TY - JOUR T1 - Nature Inspired Algorithms in Cloud Computing: A Survey AU - Gamal Abd El-Nasser A. Said Y1 - 2016/10/11 PY - 2016 N1 - https://doi.org/10.11648/j.ijiis.20160505.11 DO - 10.11648/j.ijiis.20160505.11 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 60 EP - 64 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20160505.11 AB - Cloud Computing consists of many resources, the problem of mapping tasks on unlimited computing resources in cloud computing is NP-hard optimization problem. In this paper, we provide a survey of popular nature inspired algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for solving NP-hard problems in cloud computing. VL - 5 IS - 5 ER -
Information
Email: