Machine Learning methods for Cloud Computing

P. Kiran Rao*, R. Sandeep Kumar**
*-** Assistant Professor, Department of Computer Science and Engineering, G. Pullaiah College of Engineering & Technology, Kurnool, AP, India.
Periodicity:August - October'2016
DOI : https://doi.org/10.26634/jcc.3.4.13593

Abstract

Due to the huge use of data made available in cloud computing, cloud computing challenged with issues of security on demand application resource management and self-monitoring without latency. This paper explores supervised and unsupervised learning methods on resource provisioning, monitoring, and management topics in cloud computing and examines a number of methods which propose to make use of machine learning to either allow for more self-monitored on demand application resource management or used to know the work level of resources in infinity cloud computing. The authors have also compared regular techniques in resource management in cloud computing like FIFO, VMs cluster management, etc., with machine learning methods.

Keywords

Machine Learning, Cloud Computing, Supervised Learning, Unsupervised Learning, Resource Provisioning and Management.

How to Cite this Article?

Rao. P. K., and Kumar. R. S. (2016). Machine Learning methods for Cloud Computing. i-manager’s Journal on Cloud Computing, 3(4), 7-11. https://doi.org/10.26634/jcc.3.4.13593

References

[1]. Aggarwal, P., & Sharma, S. K. (2015, February). An empirical comparison of classifiers to analyze intrusion detection. In Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on (pp. 446-450). IEEE.
[2]. Bala, K., & Vashist, S. (2014). Machine Learning based decision making by brokers in cloud computing. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(7), 269-273.
[3]. Dinda, P. A., & O'hallaron, D. R. (2000). Host load prediction using linear models. Cluster Computing, 3(4), 265-280.
[4]. Hu, R., Jiang, J., Liu, G., & Wang, L. (2014). Efficient resources provisioning based on load forecasting in cloud. The Scientific World Journal, 2014.
[5]. Li, K., Gibson, C., Ho, D., Zhou, Q., Kim, J., Buhisi, O., ... & Gerber, M. (2013, April). Assessment of machine learning algorithms in cloud computing frameworks. In Systems and Information Engineering Design Symposium (SIEDS), 2013 IEEE (pp. 98-103). IEEE.
[6]. Matsunaga, A., & Fortes, J. A. (2010, May). On the use of machine learning to predict the time and resources consumed by applications. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 495-504). IEEE Computer Society.
[7]. Rodriguez, M. A., & Buyya, R. (2015, September). A responsive knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds. In Parallel Processing (ICPP), 2015 44th International Conference on (pp. 839-848). IEEE.
[8]. Rodriguez, M. A., & Buyya, R. (2017). A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8).
[9]. Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Volume 1: Foundations.
[10]. Stamatakis, A. (2006). RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics, 22(21), 2688- 2690.
[11]. Vázquez, C., Huedo, E., Montero, R. S., & Llorente, I. M. (2011). On the use of clouds for grid resource provisioning. Future Generation Computer Systems, 27(5), 600-605.
[12]. Vecchiola, C., Calheiros, R. N., Karunamoorthy, D., & Buyya, R. (2012). Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka. Future Generation Computer Systems, 28(1), 58- 65.
[13]. Wang, S., & Summers, R. M. (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933-951.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.