Meeting of Time Limit Based Resource Distribution for Process in Cloud

Authors

  • R.Ramesh Kannan  Dhanalakshmi College of Engineering, Kancheepuram District, Tamilnadu, India
  • S.Abinaya  Dhanalakshmi College of Engineering, Kancheepuram District, Tamilnadu, India
  • D.Dheepikaraghavi  Dhanalakshmi College of Engineering, Kancheepuram District, Tamilnadu, India

Keywords:

Cloud Computing, Systematic Workflow, Resource Provisioning, Scheduling

Abstract

Cloud computing is the latest technology and it gives excellent possibilities to solve a systematic difficulties. It provides many queries that is used to finish the work economically. Even though it offers many benefits in workflow applications it also has some threats in cloud circumstances. In the existing invention the work get neglected due to the user's Quality of Service (QoS) and also it combines elasticity and heterogeneity as basic principles in computing assets. This paper presents resource provisioning and scheduling strategy for systematic workflows on Infrastructure as a Service (IaaS) Cloud. We use two algorithms namely meta-heuristic optimization technique and Particle Swarm Optimization (PSO), which plans to reduce the workflow execution cost in deadline constraints. Our heuristic is evaluated using systematic workflows and CloudSim. The conclusion of our project is, it advances better than the current state-of-the-art algorithms.

References

 [1]. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., & Vahi, K. (2012). Characterizing and profiling scientific workflows. Future Generation Comput. Syst. 29(3), 682- 692.


[2]. Mell, P., and T. Grance. (2011). The NIST definition of cloud computing—recommendations of the National Institute of Standards and Technology. Special Publication 800-145, NIST, Gaithersburg. 

[3]. Buyya, R., Broberg, J., and Goscinski, A. M. (Eds.). (2010). Cloud computing: Principles and paradigms (Vol. 87).                                 

[4]. Wiley.Kennedy, J., and Eberhart, R. (1995). Particle swarm optimization. In Proc. 6th IEEE Int. Conf. Neural Networks, 1942-1948. 

[5]. Fukuyama, Y., and Nakanishi, Y. (1999). A particle swarm optimization for reactive power and voltage control consider- ing voltage stability. In Proc. 11th IEEE Int. Conf. Intelligent Systems Application to Power Systems (ISAP), 117-121.

[6]. Ourique, C. O., Biscaia Jr, E. C., and Pinto, J. C. (2002). The use of particle swarm optimization for dynamical analysis in chem- ical processes. Comput. & Chemical Eng., 26(12), 1783-1793. 

[7]. Sousa, T., Silva, A., and Neves, A. (2004). Particle swarm based data mining algorithms for classification tasks. Parallel Computing, 30(5), 767-783.

[8]. Garey, M. R., & Johnson, D. S. (1979). Computer and intractability: A Guide to the NP-Completeness. Ney York, NY. WH Freeman and Company. 238.

[9]. Rahman, M., Venugopal, S., and Buyya, R. (2007). A dynamic critical path algorithm for scheduling scientific workflow applications on global grids. In Proc. 3rd IEEE Int. Conf. e-Sci. and Grid Computing, 35-42.

[10]. Chen, W. N., and Zhang, J. (2009). An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans. Syst., Man, Cybern., Part C: Applicat. Reviews, 39(1), 29-43. 

[11]. Yu, J., and Buyya, R. (2006). A budget constrained scheduling of workflow applications on utility grids using genetic algorithms. In Proc. 1st Workshop on Workflows in Support of Large-Scale Sci. (WORKS), 1-10. 

[12]. Mao, M., and Humphrey, M. (2011). Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In Proc. Int. Conf. High Performance Computing, Networking, Storage and Analysis (SC), 1-12.

[13]. Malawski, M., Juve, G., Deelman, E., and Nabrzyski, J. (2012). Cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. In Proc. Int. Conf. High Performance Computing, Networking, Storage and Anal. (SC), 22.

[14]. Abrishami, S., Naghibzadeh, M., and Epema, D. (2012). Dead- line-constrained workflow scheduling algorithms for IaaS Clouds. Future Generation Comput. Syst., 23(8), 1400-1414.

[15]. Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Proc. IEEE Int. Conf. Advanced Inform. Networking and Applicat. (AINA), 400- 407. 

[16]. Wu, Z., Ni, Z., Gu, L., & Liu, X. (2010). A revised discrete parti-cle swarm optimization for cloud workflow scheduling. In Proc. IEEE Int. Conf. Computational Intell. and Security (CIS), 184-188. 

[17]. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., and Epema, D. (2010). A performance analysis of EC2 cloud computing services for scientific computing. In Cloud Compu- ting. 115-131. Springer Berlin Heidelberg.


International Journal of Scientific Research in Science, Engineering and Technology

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Published

2015-02-25

Issue

Volume 1, Issue 1, January-February-2015

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R.Ramesh Kannan, S.Abinaya, D.Dheepikaraghavi, " Meeting of Time Limit Based Resource Distribution for Process in Cloud, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 1, pp.364-368, January-February-2015.