CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Cloud Environment – Task Schedule and Adaptive Cost

by Mohammed A. S. Mosleh, Syed Hamid Hasan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 3
Year of Publication: 2017
Authors: Mohammed A. S. Mosleh, Syed Hamid Hasan
10.5120/ijca2017915787

Mohammed A. S. Mosleh, Syed Hamid Hasan . Cloud Environment – Task Schedule and Adaptive Cost. International Journal of Computer Applications. 178, 3 ( Nov 2017), 29-34. DOI=10.5120/ijca2017915787

@article{ 10.5120/ijca2017915787,
author = { Mohammed A. S. Mosleh, Syed Hamid Hasan },
title = { Cloud Environment – Task Schedule and Adaptive Cost },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 178 },
number = { 3 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number3/28656-2017915787/ },
doi = { 10.5120/ijca2017915787 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:49:25.244588+05:30
%A Mohammed A. S. Mosleh
%A Syed Hamid Hasan
%T Cloud Environment – Task Schedule and Adaptive Cost
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 3
%P 29-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Execution of tasks in cloud environment warrants use of data stored on remote data servers. Even though the memory requirement of the user computer are reduced yet it is critical to maintain deadlines. This paper proposes Task scheduling based on Adaptive Cost for providing access to data for VMs (Virtual Machines) in such a way that the cost is not impacted. CBAS(Cost Based Adaptive Scheduling) utilizes the time taken for data access to decide on the data access path that is most cost effective. Time taken is computed through variance & mean time of network service and I/O request arrival rate. Task Priority is then assigned for removing the data access time for tasks. In the end, analysis of data access path and allocation is done on the basis of task priority. Task with low priority are allocated to low cost path while tasks that have high priority are assigned more expensive path to meet deadlines. Hence, CBAS achieves task scheduling efficiently. The results of the experiments related to CPU Utilization, bandwidth, communication costs, computational cost, and execution time prove CBAS perform better than current contemporary methodologies.

References
  1. “The NIST definition of cloud computing,” National Institute of Standards and Technology, vol. 53, no. 6, p. 50, 2009. P. Mell and T. Grance
  2. “Cloud computing: stateof-the-art and research challenges,” Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7–18, 2010. Q. Zhang, L. Cheng, and R. Boutaba
  3. “A model for cost-benefit analysis of cloud computing,” Journal of International Technology and Information Management, vol. 22, no. 3, article 6, 2013. K. Nanath and R. Pillai
  4. “A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment,” IEEE Transactions on Cloud Computing, 2015. J.Sahniand D.Vidyarthi
  5. “A hyper-heuristic scheduling algorithm for cloud,” IEEE Transactions on Cloud Computing, vol. 2, no. 2, pp. 236–250, 2014. C. W. Tsai, W. C. Huang, M. H. Chiang, M. C. Chiang, and C. S. Yang
  6. “ANGEL: agentbased scheduling for real-time tasks in virtualized clouds,” IEEE Transactions on Computers, vol. 64, no. 12, pp. 3389–3403, 2015. X. Zhu, C. Chen, L. T. Yang, and Y. Xiang
  7. “Evolutionary multiobjective workflow scheduling in cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344–1357, 2016. Z. Zhu, G. Zhang, M. Li, and X. Liu
  8. “PRISM: fine-grained resource-aware scheduling for MapReduce,” IEEE Transactions on Cloud Computing, vol. 3, no. 2, pp. 182–194, 2015. Q. Zhang, M. F. Zhani, Y. Yang, R. Boutaba, and B. Wong
  9. “Realtime tasks oriented energy-aware scheduling in virtualized clouds,” IEEE Transactions on Cloud Computing, vol. 2, no. 2, pp. 168–180, 2014. X. Zhu, L. T. Yang, H. Chen, J. Wang, S. Yin, and X. Liu
  10. “Scheduling jobs with unknown durationinclouds,”IEEE/ACMTransactionsonNetworking,vol. 22, no. 6, pp. 1938–1951, 2014. S. T. Maguluri and R. Srikant
  11. “Costefficient task scheduling for executing large programs in the cloud,” Parallel Computing, vol. 39, no. 4-5, pp. 177–188, 2013. S. Su, J. Li, Q. Huang, X. Huang, K. Shuang, and J. Wang
  12. “Integrating QoS awareness with virtualization in cloud computing systems for delaysensitive applications,” Future Generation Computer Systems, vol. 37, pp. 478–487, 2014. J.-W. Lin, C.-H. Chen, and C.-Y. Lin
  13. “A highly practical approach toward achieving minimum data sets storage cost in the cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1234–1244, 2013. D. Yuan, Y. Yang, X. Liu et al.
  14. “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software—Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing

Powered by PhDFocusTM