International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Comparison of Models In Cloud Computing and Their Issues

Author : V Nagaraj ¹ S Mangaiarkarasi ² B Devikiruba ³

Date of Publication :7th September 2016

Abstract: Now-a-days one of the rapidly exploiting technology in our growing trend is cloud computing. Most of the organizations are storing their data, explore their products, extend their business by means of cloud. Some of the cloud providers are IBM, Microsoft Azure, Amazon and IBM etc. they provide availability, flexibility, integration and scalability. The developers of cloud applications can have flexibility to develop and access from anywhere and anytime. The virtual space is provided by techniques of cloud and can be deployed their applications to run their operations. In this paper, we highlight scheduling methods exploited in allocating and monitoring challenges in cloud based environment and their solutions to overcome the specific issues.

Reference :

1. S. Selvarani, G. Sudha Sadhasivam â€–Improved costBased Algorithm for Task Scheduling in Cloud computingâ€• Computational Intelligence and Computing Research (ICCIC), pp 1-5, 28- 29 Dec 2010 (IEEE).
2. K. Veeramachaneni and L. A. Osadciw. Optimal scheduling in sensor networks using swarm intelligence. 2004.
3. P.-Y. Yin, S.-S. Yu, and Y.-T. Wang. A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems. Computer Standards and Interfaces, 28 (4): 441–450, 2006.
4. H. Yoshida, K. Kawata, Y. Fukuyama, and Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage stability. In the International Conference on Intelligent System Application to Power System, pages 117–121, 1999.
5. B. Yu, X. Yuan, and J. Wang. Short-term hydrothermal scheduling using particle swarm optimization method. Energy Conversion and Management, 48 (7): 1902–1908, 2007.
6. J. Yu, R. Buyya, and K. Ramamohanarao. Workflow Scheduling Algorithms for Grid Computing, volume 146, pages 173–214. Springer, Heidelberg, 2008
7. S. Aranganatham, K.M. Mehta, "An ACO Algorithm for Scheduling data intensive application with various QOS requirements" International journal of Computer Applications (0973-8887) Vol 27, no 10, pp 1-5, August (2011).
8. Medhat A. Tawfeek ,Ashraf El-Sisi, Arabi E. keshk , Fawzy A. Torkey, "Cloud task scheduling based on Ant colony optimization" IEEE(2013).
9. Young Choon Lee1, Chen Wang2, Albert Y. Zomaya1, Bing, Bing Zhou â€•Profit-driven Service Request Scheduling in Cloudsâ€– 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
10. Zhao, L., Ren, Y., Sakurai, K.: â€•A Resource Minimizing Scheduling Algorithm with Ensuring the Deadline and Reliability in Heterogeneous Systemsâ€–. In: International Conference on Advance Information Networking and Applications, AINA.( IEEE 2011)
11. Sindhu, S., Mukherjee S.: â€•Efficient Task Scheduling Algorithms for Cloud Computing Environmentâ€–. In: International Conference on High Performance Architecture and Grid Computing (HPAGC-2011), Vol 169, pp 79-83 (2011)
12. Kaur, P.D., Chana, I. â€•Unfolding the distributed computing paradigmâ€–, In: International Conference on Advances in Computer Engineering, pp. 339-342 (2010)
13. Mei, L., Chan, W.K., Tse, and T.H., â€•A Tale of Clouds: Paradigm Comparisons and Some Thoughts on Research Issuesâ€–, In: APSCC 2008, pp. 464-469 (2008)
14. Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: â€•Cost Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads. In: 3rd IEEE International Conference on Cloud Computing, Miami (July 2010).