Intelligent Resource Allocation and Capacity Computation through RaI Representation in the Cloud using Deep Learning Techniques
DOI:
https://doi.org/10.61841/6ez54z75Keywords:
RaI, Intelligent Agent algorithm,, Machine learning, Cloud computingAbstract
There are some situations where cloud computing is used to enhance the ability to the business goals, when and where to offload the resources like hardware, software, networks to cloud. So that one can offload the resources for processing as image based computation includes segmentation, deep learning for object recognition. Intelligent Agent algorithm also uses to collect performance metric in continuous period of time. Dynamic cloud allocation mechanism is implemented in processing of images parallelly. By adopting suitable mechanism one can automatically add images to cloud in real-time to know the number of available cloud instances. Queue length can be known with this. The proposed intelligent cloud resource procedure through RaI (Resources as Images) in the cloud improves overall response time, optimal utilization of cloud in order to access, allot and to determine the capacity of the resources.
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1. Satyanarayanan, Mahadev, Paramvir Bahl, Ramón Caceres, and Nigel Davies. "The case for vm-based cloudlets in mobile computing." IEEE pervasive Computing 8, no. 4 (2009): 14-23.
2. Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18, no. 7 (2006): 1527-1554.
3. Hassan, Mohammad Mehedi, Biao Song, Ahmad Almogren, M. Shamim Hossain, Atif Alamri, Mohammed Alnuem, Muhammad Mostafa Monowar, and M. Anwar Hossain. "Efficient Virtual Machine Resource Management for Media Cloud Computing." KSII Transactions on Internet & Information Systems 8, no. 5 (2014).
4. M. Shamim Hossain and G. Muhammad, cloud – Assisted Indutrial IOT – enabled Framework for health monitoring, Elsevier computer networks, 2016
5. Y. Bengio, Learning deep architectures for AI, Foundat. Trends in Mach. Learn., vol. 2, no. 1,pp. 1- 127,2009
6. Jennings, N. And Wooldridge, M., editors (1998). Applications of Intelligent Agents, chapter 1, pages 3-
28. Agent Technology: Foundations, Applications and Markets. Springer
7. Anandasivam, A and Premm M (2009). Bid price control and dynamic pricing in clouds. In proceedings of the European Conference on Information Systems, Pages 1-14.
8. Armbrust, M Fox, A., Griffith, R Joseph, A Katz, R Konwinski, A Lee, Patterson, D Rabkin, A stoica, I., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4):50-58.
9. Sim, K. (2010). Towards complex negotiation for cloud economy. Advances in Grid and Pervasive computing, Pages 395-406
10. Weiss A (2007). Computing in the clouds. netWorker, 11(4):16-25.
11. A.S Prasad and S>Rao, “ A Mechanism Design Approach to Resource Procurement in cloud computing.” IEEE Trans. Computers, vol. 63, no. 1, 2014, pp. 17-30
12. Y. LeCun, Y. Bengio and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7533, 2015,pp.436-444
13. W.Wang, B. Li and B. Liang, “Dominant Resource Fairness in cloud computing systems with heterogeneous Servers,” Proc. 33rd International conference on computer communications, 2014
14. V. Mnih et al., “Asynchonous methods for deep reinforcement learning,” Proc, 33rd Internation conference on Machine Learning, 2016.
15. R.H. Hwang et al., “cost optimization of Elasticity cloud resource subscription ploicy,” IEEE Trans. Services Computing, vol. 7, no. 4, 2014, pp.561-574
16. Dr. B. Sankara babu, A. Sampath Dakshina Murthy, Sampenga Veerraju, B. Omkar Lakshmi Jagan , K. Saikumar “Implementation of Real and Accurate Watermarking System For Security Using Logistic Regression Machine Learning Techniques”, The Journal of Research on the Lepidoptera, Volume 51 (1): 783-792, March 2020.
17. A. Sampath Dakshina Murthy, P. Satyanarayana Murthy, V. Rajesh, Sk. Hasane Ahammad, B. Omkar Lakshmi Jagan, “Execution of Natural Random Forest Machine Learning Techniques on Multi Spectral Image Compression”, International Journal of Pharmaceutical Research Volume 11, Issue 4, Oct - Dec, 2019.
18. K.Raju, S.Kiran Pilli, G. Siva Suresh Kumar, K. Saikumar, B. Omkar Lakshmi Jagan, “Implementation of Natural Random Forest Machine Learning Methods on Multi Spectral Image Compression”, Journal of Critical Review, Volume 6, Issue 5, pg. 265-273, 2019.
19. Ravada Aamani, Adinarayana Vannala, A. Sampath Dakshina Murthy, K. Saikumar, B. Omkar Lakshmi Jagan, “Heart Disease Diagnosis Process using MRI Segmentation And Lasso Net Classification ML”, Journal of Critical Review, Volume 7, Issue 6, pg. 717-721, 2020.
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