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Decentralization for Securing Data with Cloud Firewall Framework to Increase QoS


  • Department of Computer Science, Vels University, Chennai - 600117, Tamil Nadu, India


Objectives: To implement the cloud decentralized firewall concept. Methods/Statistical Analysis: A balanced approach is always needed while satisfying the user’s need and at the same time the Quality of Service (QoS) is to increase. The solution could be in allocating the resource dynamically to handle the multiple users and to optimize the cost of the resource provision. Mostly, queuing theory is used for quantitative system analyze. Markov Chain and Z-transform is used to obtain the mean packet in the response time to closed form of expression. Findings: The M/M/1 model is our proposed M/Geo/1 model for better firewall real system. The experimental results also prove that we can set up the firewall in cloud with comfortable cost to cloud users.


Cloud Computing, Firewall, Resource Allocation, System Modeling, Virtualization

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  • Armbrust M, Griffith R, Katz R, Lee G. A view of cloud computing, in X.kio, Firewall policy change-impact analysis.ACM Transactions Security on Net Technology (TOIT).2012; 11(6):1–15.
  • Kumar A, Sathasivam C, Periyasamy P. Virtual machine placement in cloud computing. Indian Journal of Science and Technology. 2016 Aug; 9(29):1–5.
  • Diallo L, Hashim AHA, Olanrewaju RF, Islam S, Zarir AA.Two objectives big data task scheduling using swarm intelligence in cloud computing. Indian Journal of Science and Technology. 2016 Jul; 9(28):1–10.
  • Liu AX. Firewall policy change-impact analysis. ACM Transactions on Internet Technology (TOIT). 2012; 11(4):1–15.
  • Zhu J, Jiang Z, Xiao Z. Twinkle: A fast resource provisioning mechanism for internet services. INFOCOM, Proceedings IEEE; China. 2011. p. 802–10.
  • Tahir Z, Jamil M, Liaqat SA, Mubarak L, Tahir W, Gilani SO.State space system modeling of a quad copter UAV. Indian Journal of Science and Technology. 2016 Jul; 9(27):1–5.
  • Fatima SN, Begam SJ, Muneera NS. Reverse auction to trade unused cloud computing resources. Indian Journal of Science and Technology. 2016 Aug; 9(30):1–7.
  • Jain R, Kop F. The art of computer system performance analysis techniques for experimental design, measurement, simulation and modeling, New York: John Willey; 1991. p. 720.
  • Hokstad P, Yum Y. Approximations for the m/g/m queue.Operations Research. 1978; 26(3):510–23.
  • Moore D, Brown G, Mop M, Savage S. Inferring internet denial-of-service attack activity. ACM Transactions on Computer Systems (TOCS). 2006; 24(2):115–39.
  • Hwang RH, Chen YR, Lee CN. Cost optimization of elasticity cloud resource subscription policy. IEEE Transactions on Services Computing. 2014; 7(4):561–74.
  • Li K, Cao J, Zimaya A. Optimal multi-server configuration in profit maximization in cloud computing. IEEE Transactions on Parallel Distributed Systems. 2012; 24(6):1087–96.
  • Sharma U, Shaikh A. A cost-aware full elasticity provisioning system for the cloud. IEEE 31st International Conference on Distributed Computing Systems (ICDCS); USA. 2011. p. 559–70.
  • Kleinrock L. Zip Theory, Queuing systems. Wiley Inter Science; 1975.


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