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Reseach Article

Resource Management in the Multi-Tenant Cloud Environment

by Piwal Priya M., Mandre B. R.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 2
Year of Publication: 2017
Authors: Piwal Priya M., Mandre B. R.
10.5120/ijca2017915064

Piwal Priya M., Mandre B. R. . Resource Management in the Multi-Tenant Cloud Environment. International Journal of Computer Applications. 172, 2 ( Aug 2017), 6-10. DOI=10.5120/ijca2017915064

@article{ 10.5120/ijca2017915064,
author = { Piwal Priya M., Mandre B. R. },
title = { Resource Management in the Multi-Tenant Cloud Environment },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 2 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number2/28221-2017915064/ },
doi = { 10.5120/ijca2017915064 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:14.516323+05:30
%A Piwal Priya M.
%A Mandre B. R.
%T Resource Management in the Multi-Tenant Cloud Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 2
%P 6-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The cloud computing is an Internet-based computing emerging as a new architecture which aims to give reliable, customizable and QoS guaranteed dynamic environment for end-users. As multi-tenancy is one of the key features of cloud computing where service providers and users have scalable and economic benefits on same cloud platforms. In cloud computing environment the execution process requires resource management due to the processing capability is high to the resource ratio. The aim of the system is to handle resource management by executing scientific workflows. The locating and assigning of free resources is handled through the Cloud-based Workflow Scheduling Algorithm (CWSA) policy. The simulation results shows that the scheduling algorithm improves the performance of scientific workflows and helps in minimization of workflow completion time, tardiness, execution cost and use of idle resources of cloud using simulator Workflowsim.

References
  1. Martin Maier, Bhaskar Prasad Rimal, "Workflow Scheduling in Multi- Tenant Cloud Computing Environments," IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, no. 99, pp. 1-1, JULY 2015.
  2. R. N. Calheiros, R. Buyya A N Toosi, "Interconnected cloud computing environments: challenges, taxonomy and survey," ACM Computing Surveys, vol. 47, no. 1, Apr 2014.
  3. John Grundy, Jacky Keung Jia Ru, "Software Engineering for Multi-tenancy Computing Challenges and Implications," InnoSWDev’14, 16 Nov 2014.
  4. Rizos Sakellariou and Henan Zhao, "Hybrid Heuristic for DAG Scheduling on Heterogeneous Systems," in Parallel and Distributed Processing Symposium IEEE International Conference., 2004.
  5. R. Prodan, J. J. D. Barrionuevo, and T. Fahringer, H. M. Fard, "A multi-objective approach for workflow scheduling in heterogeneous environments," in in Proc., IEEE/ACM CCGrid, May 2012, pp. 300-309.
  6. Wei Neng Chena and Jun Zhang, "An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem with Various QoS Requirements," System, Man and Cybernetics, Applications and Reviews IEEE Transactions, vol. 39, no. 1, pp. 29-43, 2009.
  7. The XML files that describe of the workflow applications are available via the Pegasus project. [Online]. https://confluence.pegasus.isi.edu/display/ pegasus/WorkflowGenerator.
  8. L.Wu, S. Guru, and R. Buyya S. Pandey, "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environment," IEEE Advanced Information Networking and Applications , pp. 400-407, Apr 2010.
  9. R. Prodan, and T. Fahringer H. M. Fard, "A truthful dynamiv workflow scheduling mechanism for commercial multicloud environments," IEEE Trans Parallel and Distrib. Syst., vol. 24, no. 6, pp. 1203-1212, June 2013.
  10. E. Deelman, K. Vahi, G. Mehta, B. Berriman, B. Berman and P. Maechling G. Juve, "Scientific workflow applications on amazon ec2," in IEEE E-Science Wksp, 2009, pp. 59-66.
  11. X. Sun, Q. Shao, and G. Qi W. Tsai, "Two-tier multi-tenancyscaling and load balancing," in Proc., IEEE ICEBE, vol. 9, no. 1, pp. 484-489, Nov 2010.
  12. B.Latha, AND G. Sumathi D.A.Prathibha, "Efficient Scheduling of Workflow In Cloud Enviornment using Billing Model Aware Task Clustering," Journal of Theoretical and Applied Information Technology, vol. 65, no. 3, pp. 595-605, july 2014.
  13. B.Latha, AND G. Sumathi D.A.Prathibha, "Efficient Scheduling of Workflow In Cloud Enviornment using Billing Model Aware Task Clustering," Journal of Theoretical and Applied Information Technology, vol. 65, no. 3, pp. 595-605, july 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Multi-Tenant Cloud Environment Scientific Workflows Resource management.