CFP last date
22 April 2024
Reseach Article

Optimized Load Balancing based Task Scheduling in Cloud Environment

Published on December 2014 by Elrasheed Ismail Sultan, Noraziah A, Faisal Alamri, Nawsher Khan, Tutut Herawan
Majan College International Conference
Foundation of Computer Science USA
MIC - Number 1
December 2014
Authors: Elrasheed Ismail Sultan, Noraziah A, Faisal Alamri, Nawsher Khan, Tutut Herawan
f8272ba4-0ea8-4a63-a6a1-3027a5fc7207

Elrasheed Ismail Sultan, Noraziah A, Faisal Alamri, Nawsher Khan, Tutut Herawan . Optimized Load Balancing based Task Scheduling in Cloud Environment. Majan College International Conference. MIC, 1 (December 2014), 35-38.

@article{
author = { Elrasheed Ismail Sultan, Noraziah A, Faisal Alamri, Nawsher Khan, Tutut Herawan },
title = { Optimized Load Balancing based Task Scheduling in Cloud Environment },
journal = { Majan College International Conference },
issue_date = { December 2014 },
volume = { MIC },
number = { 1 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 35-38 },
numpages = 4,
url = { /proceedings/mic/number1/19035-1414/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Majan College International Conference
%A Elrasheed Ismail Sultan
%A Noraziah A
%A Faisal Alamri
%A Nawsher Khan
%A Tutut Herawan
%T Optimized Load Balancing based Task Scheduling in Cloud Environment
%J Majan College International Conference
%@ 0975-8887
%V MIC
%N 1
%P 35-38
%D 2014
%I International Journal of Computer Applications
Abstract

The fundamental issue of Task scheduling is one important factor to load balance between the virtual machines in a Cloud Computing network. However, the optimal broadcast methods which have been proposed so far focus only on cluster or grid environment. In this paper, task scheduling strategy based on load balancing Quantum Particles Swarm algorithm (BLQPSO) was proposed. The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. Furthermore, the performance of proposed algorithm had been compared with existing PSO and shows their effectiveness in balancing the load.

References
  1. B. Raghavan, et al. , "Cloud control with distributed rate limiting," Proc. SIGCOMM'07, pp. 337 - 348, Kyoto, Japan, 2007.
  2. D. Ardagna and B. Pernici, "Adaptive service composition in flexible processes," IEEE Transactions on Software Engineering, pp. 369-384, 2007.
  3. K. Bhattacharya, et al. , "ICSE Cloud 09: First international workshop on software engineering challenges for Cloud Computing," Proc. 31st International Conference on Software Engineering - Companion Volume,. (ICSE-Companion 2009), pp. 482-483. 2009
  4. W. Van der Aalst and K. Van Hee, Workflow management: models, methods, and systems: The MIT press, 2004.
  5. I. Foster, Zhao Yong, I. Raicu, and S. Lu, "Cloud Computing and Grid Computing 360-Degree Compared", Proc. Grid Computing Environments workshop, 2008. GCE '08, pp. 1-10, 2008.
  6. D. Yuan, et al. , "A data placement strategy in scientific cloud workflows," Future Generation Computer Systems, pp. 1200-1214 2010.
  7. S. Pandey, et al. , "A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments," in Advanced Information Networking and Applications (AINA), 24th IEEE International Conference on, pp. 400-407,2010.
  8. D. Bratton and J. Kennedy, "Defining a Standard for Particle Swarm Optimization," in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, 2007, pp. 120-127.
  9. M. Clerc, "Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem," New optimization techniques in engineering(Springer), 2004.
  10. G. Pampara, et al. , "Combining particle swarm optimisation with angle modulation to solve binary problems," Proc. The IEEE Congress on Evolutionary Computation , pp. 89-96, vol. 1,2005.
  11. D. Sha and C. Hsu, "A hybrid particle swarm optimization for job shop scheduling problem," Computers & Industrial Engineering, pp. 791-808, vol. 51,2006.
  12. J. Grobler, et al. , "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, pp. 1-32, 2008.
  13. M. Neethling and A. P. Engelbrecht, "Determining RNA Secondary Structure using Set-based Particle Swarm Optimization," IEEE Congress on Evolutionary Computation, BC, Canada,pp. 1670-1677, 2006.
  14. C. Wei-Neng, et al. , "A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems," IEEE Transactions on Evolutionary Computation, , vol. 14, pp. 278-300, 2010.
  15. Z. Wu, et al. , "A Market-Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems," Journal of Supercomputing, Special issue on Advances in Network&ParallelComptg, to be appeared,2010.
  16. J. Yu and R. Buyya, "A Taxonomy of Workflow Management Systems for Grid Computing," Journal of Grid Computing, no. 3, pp. 171-200, 2005.
  17. X. Liu, J. Chen, Z. Wu, Z. Ni, D. Yuan, Y. Yang, Handling Recoverable Temporal Violations in Scientific Workflow Systems: A Workflow Rescheduling Based Strategy. Proc. of 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid2010), pages 534-537, Melbourne, Australia, May 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Scheduling Load Balancing Storage System Virtual Machines.