A Novel Load Balance Algorithm for Cloud Computing
A good scheduling algorithm is a key for load balance system, in which system’s load meets users’ requirement. Here, a new load balance algorithm based on swarm intelligence is proposed which can enhance the production of the systems while schedule tasks to VMs properly. Here tasks completion time is compared with some other classical algorithms. The result shows that the proposed algorithm could meet users’ requirement and get resource utilization higher. The algorithm is better for network of a large area which is simulated by CloudSim.
KeywordsComposite sequence Power spectrum Direct sequence spread spectrum Interference avoidance
Unable to display preview. Download preview PDF.
- 1.Zhu, H., Liu, T., Zhu, D., Li, H.: Robust and simple N-Party entangled authentication cloud storage protocol based on secret sharing scheme. Journal of Information Hiding and Multimedia Signal Processing (JIHMSP) 4, 110–118 (2013)Google Scholar
- 2.Chang, B., Tsai, H.-F., Chen, C.-M.: Evaluation of virtual machine performance and virtualized consolidation ratio in cloud computing system. Journal of Information Hiding and Multimedia Signal Processing (JIHMSP) 4, 192–200 (2013)Google Scholar
- 4.Foster, I., Zhao, Y., Raicu, I., et al.: Cloud computing and grid computing 360-degree compared. Grid Computing Environments Workshop, GCE 2008, vol. 1, pp. 1–10 (2008)Google Scholar
- 6.Jadeja, Y., Modi, K.: Cloud computing-concepts, architecture and challenges. In: The International Conference on Computing & Electronics and Electrical Technologies, vol. 1, pp. 877–880. IEEE, Nagercoil (2012)Google Scholar
- 7.Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable cloud computing environments and the cloudSim toolkit: challenges and opportunities. In: International Conference on High Performance Computing & Simulation, HPCS 2009, vol. 1, pp. 1–11. IEEE (2009)Google Scholar
- 8.Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience 41, 23–50 (2011)Google Scholar
- 9.Das, S., Viswanathan, H., Rittenhouse, G.: Dynamic load balance through coordinated scheduling in packet data systems INFOCOM 2003. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, vol. 1, pp. 786–796. IEEE Societies, IEEE (2003)Google Scholar
- 12.Jijian, L., Longjun, H., Haijun, W.: Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO. Engineering Sciences 12, 009 (2011)Google Scholar
- 13.Kazem, A., Rahmani, A.M., Aghdam, H.H.: A modified simulated annealing algorithm for static task scheduling in grid computing. In: International Conference on Computer Science and Information Technology, ICCSIT 2008, vol. 1, pp. 623–627. IEEE (2008)Google Scholar
- 15.Pandey, S., Wu, L., Guru, S.M., et al.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), vol. 1, pp. 400–407. IEEE (2010)Google Scholar
- 16.Hua, X., Zheng, J., Hu, W.: Ant colony optimization algorithm for computing resource allocation based on cloud computing environment. Journal of East China Normal University (Natural Science) 1, 127–134 (2010)Google Scholar
- 18.TSai, P.W., Pan, J.S., Liao, B.Y., et al.: Enhanced artificial bee colony optimization. The International Journal of Innovative Computing, Information and Control 5, 5081–5092 (2009)Google Scholar