Open Access   Article Go Back

Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre

Amit Chaturvedi1 , Aaqib Rashid2

  1. Dept. of MCA, Govt. Engineering College, Ajmer, India.
  2. Mewar University, Chittorgarh, Rajasthan, India.

Correspondence should be addressed to: amit0581@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-2 , Page no. 222-225, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i2.222225

Online published on Feb 28, 2018

Copyright © Amit Chaturvedi, Aaqib Rashid . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Amit Chaturvedi, Aaqib Rashid, “Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.222-225, 2018.

MLA Style Citation: Amit Chaturvedi, Aaqib Rashid "Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre." International Journal of Computer Sciences and Engineering 6.2 (2018): 222-225.

APA Style Citation: Amit Chaturvedi, Aaqib Rashid, (2018). Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre. International Journal of Computer Sciences and Engineering, 6(2), 222-225.

BibTex Style Citation:
@article{Chaturvedi_2018,
author = {Amit Chaturvedi, Aaqib Rashid},
title = {Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2018},
volume = {6},
Issue = {2},
month = {2},
year = {2018},
issn = {2347-2693},
pages = {222-225},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1727},
doi = {https://doi.org/10.26438/ijcse/v6i2.222225}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i2.222225}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1727
TI - Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre
T2 - International Journal of Computer Sciences and Engineering
AU - Amit Chaturvedi, Aaqib Rashid
PY - 2018
DA - 2018/02/28
PB - IJCSE, Indore, INDIA
SP - 222-225
IS - 2
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
649 432 downloads 256 downloads
  
  
           

Abstract

Cloud environment allows us to share the resources like CPU, Memory, etc to multiple tenants. These tenants put their tasks to the cloud server through the cloudlets. These cloudlets are treated as Process Elements (PEs). There are basically three entities Cloud Information Service [CIS], Data Centre, and Broker. All communications takes place between these three entities for executing the jobs or tasks. In this paper, we have created a cloud simulation environments, two sample sets are designed i.e. Table 1 and Table 2 to analyze the impacts of Submitted Tasks, Number of Virtual Machines variations on the Average Execution Time per task and illustrated through Figure 2 and Figure 3. It is observed that if the number of tasks and other environment constraints remains constant, increase in VMs decreases the Average Execution time per task, but limited number of VM can be increased according to the server architecture. If the number tasks are increased by keeping VMs and other simulation environments constant, the Average Execution time per task increases linearly.

Key-Words / Index Term

Cloud Computing, Virtual Machines, Data Centre, Process Elements, Broker, Cloud Informatio Centre.

References

[1] A.Singh, D. Juneja, M. Malhotra, “A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing”, Journal of King Saud University – Computer and Information Sciences (2015), pp. 1-10, 1319-1578.
[2] S.A. Hussain, M. Fatima, A.Saeed, I. Raza, R.K. Shahzad, “Multilevel classification of security concerns in
[3] cloud computing”, Applied Computing and Informatics (2016), pp.2-9, http://dx.doi.org/10.1016/j.aci.2016.03.001
[4] Saraswathi AT, Kalaashri.Y.RA, Dr.S.Padmavathi, “Dynamic Resource Allocation Scheme in Cloud Computing”, Procedia Computer Science 47 ( 2015 ) 30 – 36, doi: 10.1016/j.procs.2015.03.180
[5] M.Verma, GR Gangadharan, NC Narendra, R Vadlamani, V.Inamdar, L. Ramachandran, “Dynamic resource demand prediction and allocation in multi-tenant service clouds”, Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.3767
[6] Z. Shen, S. Subbiah, X Gu, J. Wilkes, “CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems”, ACM 978-1-4503-0976-9/11/10, October 27–28, 2011,
[7] W. Lin, J.Z. Wang, C. Liang, D. Qi, “A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing”, Procedia Engineering 23(2011), pp. 695-703
[8] P. Pradhan, R.K.Behera, BNB Ray, “Modified Round Robin Algorithm for Resource Allocation in Cloud Computing”, International Conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science 85 ( 2016 ), pp. 878 – 890
[9] Abhishek Chandra, Weibo Gong, PrashantSheno.Dynamic Resource Allocation for Shared DataCentres Using Online Measurements 2003
[10] J. Chase, D. Anderson, P. N. Thakar, and A. M. Vahdat.Managing energy and server resources in hosting centers. InProc. SOSP, 2001.
[11] X. Fan, W.-D.Weber, and L. A. Barroso. Power provisioningfor a warehouse-sized computer. In Proc. ISCA, 2007.
[12] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Capacitymanagement and demand prediction for next generation datacenters. In Proc. ICWS, 2007.
[13] E. Kalyvianaki, T. Charalambous, and S. Hand. Self-adaptiveand self-configured CPU resource provisioning forvirtualized servers using Kalman filters. In Proc. ICAC,2009.
[14] H. Lim, S. Babu, and J. Chase. Automated control for elasticstorage. In Proc. ICAC, 2010.
[15] Xiaoyun Zhu, Zhikui Wang, SharadSinghal Utility-driven workloadmanagement using nested control design. In Proc. AmericanControl Conference, 2006.
[16] B. Urgaonkar, M. S. G. Pacifici, P. J. Shenoy, and A. N.Tantawi. An analytical model for multi-tier internet services and its applications. In Proc. SIGMETRICS, 2005.
[17] Z. Gong, X. Gu, and J. Wilkes. PRESS: Predictive Elastic Resource Scaling for Cloud Systems. In Proc. CNSM, 2010.