CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment

by J. Kok Konjaang, J.Y. Maipan-uku, Kumangkem Kennedy Kubuga
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 8
Year of Publication: 2016
Authors: J. Kok Konjaang, J.Y. Maipan-uku, Kumangkem Kennedy Kubuga
10.5120/ijca2016909884

J. Kok Konjaang, J.Y. Maipan-uku, Kumangkem Kennedy Kubuga . An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment. International Journal of Computer Applications. 142, 8 ( May 2016), 25-30. DOI=10.5120/ijca2016909884

@article{ 10.5120/ijca2016909884,
author = { J. Kok Konjaang, J.Y. Maipan-uku, Kumangkem Kennedy Kubuga },
title = { An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 8 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number8/24917-2016909884/ },
doi = { 10.5120/ijca2016909884 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:44:27.818082+05:30
%A J. Kok Konjaang
%A J.Y. Maipan-uku
%A Kumangkem Kennedy Kubuga
%T An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 8
%P 25-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/users. The current Max-Min algorithm selects tasks with maximum execution time on a faster available machine or resource that is capable of giving minimum completion time. The concern of this algorithm is to give priority to tasks with maximum execution time first before assigning those with the minimum execution time for the purpose of minimizing makespan. The drawback of this algorithm is that, the execution of tasks with maximum execution time first may increase the makespan, and leads to a delay in executing tasks with minimum execution time if the number of tasks with maximum execution time exceeds that of tasks with minimum execution time, hence the need to improve it to mitigate the delay in executing tasks with minimum execution time. CloudSim is used to compare the effectiveness of the improved Max-Min algorithm with the traditional one. The experimented results show that the improved algorithm is efficient and can produce better makespan than Max-Min and DataAware.

References
  1. Xiong, C., Feng, L., & Chen, L. (2013). A new task scheduling algorithm based on an improved genetic algorithm in a cloud computing environment. Advances in Information Sciences and Service Sciences. Vol. 5(3), pp. 32.
  2. Thomas, A., Krishnalal, G., & Raj, V. J. (2015). Credit based scheduling algorithm in cloud computing environment. Procedia Computer Science. Vol. 46, pp. 913-20.
  3. Elghoneimy, E., Bouhali, O., & Alnuweiri, H. (2012). Resource allocation and scheduling in cloud computing. International Conference on Networking and Communications (ICNC). Pp. 309-314.
  4. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems. Vol. 25(6), pp. 599-616.
  5. Buyya, R., & Sukumar, K. (2011). Platforms for building and deploying applications for cloud computing. ArXiv Preprint arXiv:1104.4379.
  6. Elzeki, O., Rashad, M., & Elsoud, M. (2012). Overview of scheduling tasks in distributed computing systems. International Journal of Soft Computing and Engineering. Vol. 2(3), pp. 470-475.
  7. Selvarani, S., & Sadhasivam, G. S. (2010). Improved cost-based algorithm for task scheduling in cloud.
  8. Brar, S. S., & Rao, S. (2015). Optimizing workflow scheduling using max-min algorithm in a cloud environment. International Journal of Computer Applications. Vol. 124(4).
  9. Santhosh, B., & Manjaiah, D. (2014). An improved task scheduling algorithm based on max-min for cloud computing.
  10. Agarwal, D., & Jain, S. (2014). Efficient optimal algorithm of task scheduling in cloud computing environment. ArXiv Preprint arXiv:1404.2076.
  11. Deyo, J. (2008) Software as a Service (SaaS), 2008.
  12. http://www.cisco.com/c/en/us/solutions/data-center- virtualization/private-cloud/index.html (sited on 28/09/2015 at 5:59a
  13. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.
  14. Hogan, M., Liu, F., Sokol, A., & Tong, J. (2011). Nist cloud computing standards roadmap. NIST Special Publication, 35.
  15. Goyal, S. (2014). Public vs private vs hybrid vs community-cloud computing: A critical review. International Journal of Computer Network and Information Security. Vol. 6(3), pp. 20.
  16. Saraswathi, A., Kalaashri, Y., & Padmavathi, S. (2015). Dynamic resource allocation scheme in cloud computing. Procedia Computer Science. Vol. 47, pp. 30-36.
  17. Vinothina, V., Sridaran, R., & Ganapathi, P. (2012). A survey on resource allocation strategies in cloud computing. International Journal of Advanced Computer Science and Applications. Vol. 3(6), pp. 97-104.
  18. Sudeepa, R., & Guruprasad, H. (2014). Resource allocation in cloud computing. International Journal of Modern Communication Technologies & Research. 2321-0850.
  19. Wei, G., Vasilakos, A. V., Zheng, Y., & Xiong, N. (2010). A game-theoretic method of fair resource allocation for cloud computing services. The Journal of Supercomputing. Vol. 54(2), pp. 252-269.
  20. Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., & Stoica, I. (2011). Dominant resource fairness: Fair allocation of multiple resource types. Nsdi, 11 24-24.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing task allocation Makespan and Max-Min algorithm.

Powered by PhDFocusTM