Cost optimization using hybrid evolutionary algorithm in cloud computing

Research Journal of Applied Sciences, Engineering and Technology

Volumn 10, Issue 7, 2015, Pages 758-769

  Kavitha, B ^a   Varalakshmi, P ^b  

^a ANNA UNIVERSITY   (India)

^b ANNA UNIVERSITY   (India)

Author keywords

Binary cuckoo search; Binary particle swarm optimization; Computational cost; Levy flights; Monetary cost

Indexed keywords

EID: 84938849206     PISSN: 20407459     EISSN: 20407467     Source Type: Journal    
DOI: 10.19026/rjaset.10.2428     Document Type: Article

References (30)

1. An, B., V. Lesser, D. Irwin and M. Zink, 2010. Automated negotiation with decommitment for dynamic resource allocation in cloud computing. Auton. Agent. Multi-Ag., 1(1): 981-988.
2. Babukartik, R.G. and P. Dhavachelvan, 2012. Hybrid algorithm using the advantage of ACO and cuckoo search for job scheduling. Int. J. Inform. Technol. Convergence Serv., 2(4): 25-34.
3. Beloglazov, A., J. Abawajy and R. Buyya, 2012. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comp. Sy., 28: 755-768.
4. Brajevic, I., M. Tuba and N. Bacanin, 2012. Multilevel image thresholding selection basedon the cuckoo search algorithm. Proceeding of the Advances in Sensors, Signals, Visualization, Imaging and Simulation (VIS, 2012), pp: 217-222.
5. Dhivya, M., M. Sundarambal and L.N. Anand, 2011. Energy efficient computation of data fusion in wireless sensor networks using cuckoo based particle approach. Int. J. Commun. Network Syst. Sci., 4: 249-255.
6. Eberhart, R.C. and J. Kennedy, 1995. A new optimizer using particle swarm theory. Proceeding of 6th International Symposium Micromachine Human Science. Nagoya, Japan, pp: 39-43.
7. Gogulan, R., A. Kavitha and U.K. Kumar, 2012. An multiple pheromone algorithm for cloud scheduling with various QOS requirements. Int. J. Comput. Sci. (IJCSI), 9(3).
8. Goudarzi, H. and M. Pedram, 2011. Maximizing profit in cloud computing system via resource allocation. Proceeding of 31st International Conference on Distributed Computing Systems Workshops (ICDCSW, 2011), pp: 1-6.
9. Ho, S.Y., H.S. Lin, W.H. Liauh and S.J. Ho, 2008. OPSO: Orthogonal particle swarm optimization and its application to task assignment problems. IEEE T. Syst. Man, Cy. A, 38(2): 288-298.
10. Jati, G.K., H.M. Manurung and S. Suyanto, 2012. Discrete cuckoo search for traveling salesman problem. Proceeding of 7th International Conference on Computing and Convergence Technology (ICCCT, 2012), pp: 993-997.
11. Kennedy, J. and R.C. Eberhart, 1995. Particle swarm optimization. Proceeding of IEEE International Conferences on Neural Networks, Perth, Australia, 4: 1942-1948.
12. Kennedy, J., R.C. Eberhart and Y.H. Shi, 2001. Swarm Intelligence. Morgan Kaufmann, San Mateo, CA.
13. Layeb, A. and S.R. Boussalia, 2012. A novel quantum inspired cuckoo search algorithm for bin packing problem. Int. J. Inform. Technol. Comput. Sci., 4(5): 58-67.
14. Noghrehabadi, A., M. Ghalambaz, M. Ghalambaz and A. Vosough, 2011. A hybrid power series-cuckoo search optimization algorithm to electrostatic deflection of micro fixed-fixed actuators. Int. J. Multidisciplinary Sci. Eng., 2(4): 22-26.
15. Onat, Y.Y., C. Matthews, F. Roozbeh and S. Neville, 2010. Dynamic resource allocation in computing clouds through distributed multiple criteria decision analysis. Proceeding of IEEE 3rd International Conference on Cloud Computing (CLOUD), pp: 91-98.
16. Pawar, C.S. and R.B. Wagh, 2012. A review of resource allocation policies in cloud computing. J. Sci. Technol., 2(3): 165-167.
17. Rani, K.N., M.F. Malek and N.S. Chin, 2012. Nature-inspired cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array. Radioengineering, 21(3): 865-874.
18. Stillwell, M., D. Schanzenbach, F. Vivien and H. Casanova, 2010. Resource allocation algorithms for virtualized service hosting platforms. J. Parallel Distr. Com., 70(9): 962-974.
19. Taşgetiren, M.F. and Y.C. Liang, 2003. A binary particle swarm optimization algorithm for lot sizing problem. J. Econ. Soc. Res., 5(2): 1-20.
20. Valian, E., S. Mohanna and S. Tavakoli, 2011a. Improved cuckoo search algorithm for feed forward neural network training. Int. J. Artif. Intell. Appl., 2(3): 36-43.
21. Valian, E., S. Mohanna and S. Tavakoli, 2011a. Improved cuckoo search algorithm for feed forward neural network training. Int. J. Artif. Intell. Appl., 2(3): 36-43.
22. Valian, E., S. Mohanna and S. Tavakoli, 2011b. Improved cuckoo search algorithm global optimization. Int. J. Commun. Inform. Technol., 1(1): 31-44.
23. Warneke, D. and O. Kao, 2011. Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. IEEE T. Parall. Distr., 3(3): 1-3.
24. Xiao, Z., W. Song and Q. Chen, 2013. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE T. Parall. Distr., 24(6): 1107-1117.
25. Yang, X.S. and S. Deb, 2009. Cuckoo search via levy flights. Proceeding of World Congress on Nature and Biologically Inspired Computing. India, pp: 210-214.
26. Yang, X.S. and S. Deb, 2010. Engineering Optimization by Cuckoo Search. Int. J. Math. Model. Numer. Optim., 1(4): 330-343.
27. Yin, B., Y. Wang, L. Meng and X. Qiu, 2012. A multi-dimensional resource allocation algorithm in cloud computing. J. Inform. Comput. Sci., 9(11): 3021-3028.
28. Zhan, Z.H., J., Zhang, Y. Li and H.S. Chung, 2009. Adaptive particle swarm optimization. IEEE T. Syst. Man Cy. B, 39(6): 1362-1381.
29. Zhang, Q., L. Cheng and R. Boutaba, 2010. Cloud computing: State-of-the-art and research challenges. J. Int. Serv. Appl., 1(6): 7-18.
30. Zhang, Q., Q. Zhu and R. Boutaba, 2011. Dynamic resource allocation for spot markets in cloud computing environments. Proceeding of 4th IEEE International Conference on Utility and Cloud Computing (UCC, 2011), pp: 178-185.