Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
General Research Article

A Prediction based Cloud Resource Provisioning using SVM

Author(s): Vijayasherly Velayutham* and Srimathi Chandrasekaran

Volume 13, Issue 3, 2020

Page: [531 - 535] Pages: 5

DOI: 10.2174/2666255813666200206124025

Price: $65

Abstract

Aim: To develop a prediction model grounded on Machine Learning using Support Vector Machine (SVM).

Background: Prediction of workload in a Cloud Environment is one of the primary task in provisioning resources. Forecasting the requirements of future workload lies in the competency of predicting technique which could maximize the usage of resources in a cloud computing environment.

Objective: To reduce the training time of SVM model.

Methods: K-Means clustering is applied on the training dataset to form ‘n’ clusters firstly. Then, for every tuple in the cluster, the tuple’s class label is compared with the tuple’s cluster label. If the two labels are identical then the tuple is rightly classified and such a tuple would not contribute much during the SVM training process that formulates the separating hyperplane with lowest generalization error. Otherwise the tuple is added to the reduced training dataset. This selective addition of tuples to train SVM is carried for all clusters. The support vectors are a few among the samples in reduced training dataset that determines the optimal separating hyperplane.

Results: On Google Cluster Trace dataset, the proposed model incurred a reduction in the training time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM. The model has also been tested on Los Alamos National Laboratory’s Mustang and Trinity cluster traces.

Conclusion: The Cloudsim’s CPU utilization (VM and Cloudlet utilization) was measured and it was found to increase upon running the same set of tasks through our proposed model.

Keywords: Cloud computing, machine learning, K-means clustering, support vector machine (SVM), CPU, tuple's cluster label.

Graphical Abstract

[1]
C. Reiss, A. Tumanov, G.R. Ganger, R.H. Katz, and M.A. Kozuch, "Heterogeneity and dynamicity of clouds at scale: Google trace analysis", SoCC '12 Proceedings of the 3rd ACM Symposium on Cloud Computing, pp. 1-13, . October 2012.
[http://dx.doi.org/10.1145/2391229.2391236]
[2]
H. Che, B. Ding, H. Wang, B. Hu, and H. Che, "IKNN-SVM: A hybrid incremental algorithm for image classification", 2nd International Conference on Artificial Intelligence and Industrial Engineering, 2016.
[3]
V. Vapnik, The Nature of Statistical Learning Theory., Springer Verlag, 1995.
[http://dx.doi.org/10.1007/978-1-4757-2440-0]
[4]
C. Burges, "A tutorial on support vector machines for pattern recognition", Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121-167, 1998.
[http://dx.doi.org/10.1023/A:1009715923555]
[5]
C. Cortes, and V. Vapnik, Support vector networks, 1995.
[http://dx.doi.org/10.1007/BF00994018]
[6]
T. Kudo, and Y. Matsumoto, "Chunking with support vector machines NAACL", Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies, pp. 1-8, June 2001.
[http://dx.doi.org/10.3115/1073336.1073361]
[7]
E. Osuna, R. Freund, and F. Girosi, "An improved training algorithm for support vector machines,” Neural Networks for Signal Processing VII" Proceedings of the 1997 IEEE Signal Processing Society Workshop, 1997, pp. 276-285.
[8]
J. Platt, Fast training of support vector machines using sequential minimal optimization.Advances in Kernel Methods: Support Vector Learning. C., MIT Press: Cambridge, MA, 1999, pp. 185-208.
[9]
R. Xiao, J. Wang, and F. Zhang, "An Approach to Incremental SVM Learning Algorithm" Proceedings 12th IEEE International Conference on Tools with Artificial Intelligence, ICTAI, 2000, pp. 268-273.
[10]
W. Zeng, and M. Jian, "A novel approach to incremental SVM learning algorithm", J. Xinjiang Univ. [Natural Science], vol. 41, no. 6, pp. 687-690, 2002.
[11]
D. Li, D. Shuxin, and W. Tiejun, "Fast incremental learning algorithm of linear support vector machine based on hull vectors", J. Zhejiang Univ. Eng. Sci., vol. 40, no. 2, pp. 202-206, 2006.
[12]
H. Xiao, F. Sun, and Y. Liang, "A fast incremental learning algorithm for SVM based on K nearest neighbors" Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, 2010, pp. 413-416.
[http://dx.doi.org/10.1109/AICI.2010.207]
[13]
Y. Qin, D. Li, and A. Zhang, "A new SVM multiclass incremental learning algorithm", Math. Probl. Eng., 2015.
[http://dx.doi.org/10.1155/2015/745815]
[14]
S. Di, D. Kondo, and W. Cirne, "Host load prediction in a Google compute cloud with a Bayesian model", SC ’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012.
[http://dx.doi.org/10.1109/SC.2012.68]
[15]
Q. Zhang, M.F. Zhani, R. Boutaba, and J.L. Hellerstein, "Dynamic heterogeneity-aware resource provisioning in the cloud", IEEE Transactions on Cloud Computing, vol. 2, no. 1, pp. 14-28, 2014.
[http://dx.doi.org/10.1109/TCC.2014.2306427]
[16]
S. Chen, M. Ghorbani, Y. Wang, P. Bogdan, and M. Pedram, "Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique", IEEE International Congress on Big Data, 2014.
[http://dx.doi.org/10.1109/BigData.Congress.2014.108]
[17]
Md. Rasheduzzaman, Md.A. Islam, T. Islam, T. Hossain, and R.M. Rahman, "Study of different forecasting models on Google cluster trace", IEEE International Conference on Computer and Information Technology (ICCIT), 2014.
[http://dx.doi.org/10.1109/ICCITechn.2014.6997346]
[18]
J.O. Iglesias, L. Murphy, M. De Cauwer, D. Mehta, and B. O’Sullivan, "A methodology for online consolidation of tasks through more accurate resource" Estimations IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014, pp. 89-98.
[http://dx.doi.org/10.1109/UCC.2014.17]
[19]
F. Caglar, and A. Gokhale, "iOverbook: Intelligent resource-overbooking to support soft real-time applications in the cloud" IEEE 7th International Conference on Cloud Computing, 2014, pp. 538-545.
[http://dx.doi.org/10.1109/CLOUD.2014.78]
[20]
R. Hu, J. Jiang, G. Liu, and L. Wang, "Efficient resources provisioning based on load forecasting in cloud", Sci. World J., vol. 2014, p. 321231, 2014.
[http://dx.doi.org/10.1155/2014/321231] [PMID: 24701160]
[21]
R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A. De Rose, and R. Buyya, "“CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Software", Pract. Exper. [SPE], vol. 41, no. 1, pp. 23-50, 2011.
[http://dx.doi.org/10.1002/spe.995]
[22]
M.H. Hilman, M.A. Rodriguez, and R. Buyya, "Task runtime prediction in scientific workflows using an online incremental learning approach" IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), 2018, pp. 93-102.
[23]
"Google Cluster Data", Available from:, https://ai.googleblog.com/2010/01/google-cluster-data.html
[24]
"Cloudsim", Available from:, http://www.cloudbus.org
[25]
"Dataset", Available from:, https://ftp.pdl.cmu.edu/pub/data sets/ ATLAS/

Rights & Permissions Print Cite
Article Metrics
10
© 2024 Bentham Science Publishers | Privacy Policy