TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.

1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
PROBABILISTIC OPTIMIZATION OF RESOURCE DISTRIBUTION AND
ENCRYPTION FOR DATA STORAGE IN THE CLOUD
ABSTRACT
In this paper, we develop a decentralized probabilistic method for
performance optimization of cloud services. We focus on Infrastructure-as-a-
Service where the user is provided with the ability of configuring virtual
resources on demand in order to satisfy specific computational requirements.
This novel approach is strongly supported by a theoretical framework based on
tail probabilities and sample complexity analysis. It allows not only the
inclusion of performance metrics for the cloud but the incorporation of
security metrics based on cryptographic algorithms for data storage. To the
best of the authors’ knowledge this is the first unified approach to provision
performance and security on demand subject to the Service Level Agreement
between the client and the cloud service provider. The quality of the service is
guaranteed given certain values of accuracy and confidence. We present some
experimental results using the Amazon Web Services, Amazon Elastic Compute
Cloud service to validate our probabilistic optimization method.
CONCLUSION
We have presented a decentralized mathematical approach to optimally
distribute virtual resources in the cloud amongst a set of users. This novel
technique uses the notion of tail probabilities and sample complexity to design
a randomized algorithm for optimal resource allocation. Moreover, we

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
introduced a heuristic algorithm for the parallelization of the optimization
process given the sometimes prohibitive number of iterations obtained from
the sample complexity analysis. Security has been introduced as part of the
virtual resources to be optimized. This approach proposes a security metric
consisting of an ordered classification of cryptographic algorithms based on
their key-length, their capacity of hiding identification patterns, their immunity
to cryptanalysis, the parallelization of the algorithm and their capacity of
overcoming errors. This approach has been implemented and tested in the
AWS EC2 cloud. The experimental results show that this approach is able to
optimize resources based on measured performance and according to the SLA.
REFERENCES
[1] R. Nathuji, A. Kansal, and A. Ghaffarkhah, “Q-clouds: Managing
performance interference effects for qos-aware clouds,” in ACM European Soc.
Syst. Conf. EuroSys’10, Paris, 2010, pp. 237–250.
[2] D. Hilley, “Cloud computing: A taxonomy of platform and infrastructure-
level offerings,” Georgia Institute of Technology, Atlanta, GA, Tech. Rep., 2009.
[3] B. R. Kandukuri, V. R. Paturi, and A. Rakshit, “Cloud security issues,” in IEEE
Int. Conf. Services Computing SCC’09, Bangalore, India, 2009, pp. 517–520.
[4] S. Pearson and A. Benameur, “Privacy, security and trust issues arising from
cloud computing,” in 2nd IEEE Int. Conf. Cloud Computing Technology Sci.
CloudCom’10, Indianapolis, IN, 2010, pp. 693–702.

3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[5] Y. Diao, J. L. Hellerstein, S. Parekh, H. Shaikh, and M. Surendra, “Controlling
quality of service in multi-tier web applications,” in 26th IEEE Int. Conf.
Distributed Computing Syst. ICDCS’06, Lisbon, 2006, p. 25.
[6] J. V. Vliet and F. Paganelli, Programming Amazon EC2. Sebastopol, CA:
O’reilly Media Inc., 2011.
[7] H. Lim, S. Babu, J. Chase, and S. Parekh, “Automated control in cloud
computing: Challenges and opportunities,” in 1st Workshop on Automat.
Control Datacenters Clouds ACDC’09, Barcelona, 2009, pp. 13–18.
[8] B. D. Chung, H. Jeon, and K.-K. Seo, “A framework of cloud service quality
evaluation system - focusing on security quality evaluation,” Int. J. Software
Eng. Applicat., vol. 8, no. 4, pp. 41–46, May 2014.
[9] D. Zissis and D. Lekkas, “Addressing cloud computing security issues,”
Future Generation Computer Systems, vol. 28, no. 3, pp. 583–592, March 2012.
[10] R. Tempo, G. Calafiore, and F. Dabbene, Randomized Algorithms for
Analysis and Control of Uncertain Systems, with Applications, 2nd ed. London:
Springer-Verlag, 2013.