Integration of Multi Server for Profit Efficiency in Cloud Computing

Authors(4) :- V. Praveen, V. Gobu, M. Kavitha, T. Suvaikin Punitha

Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings to the operator. Achieving similar benefits with real-time services can be a challenge. It seeks to lower a provider’s costs of real-time IPTV services through a virtualized IPTV architecture and through intelligent time-shifting of service delivery. The merits of the differences in the deadlines associated with Live TV versus Video-on-Demand (VoD) to effectively multiplex these services. A generalized framework is provided for computing the amount of resources needed to support several services, without missing the deadline for any service. An optimization formulation that uses a generic cost function is build. The multiple forms for the cost function (e.g., maximum, convex and concave functions) to reflect the different pricing options are implemented. The solution to this formula gives the number of servers needed at different time instants to support these services. A simple logic for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network is implemented. End results explain the load is minimized by ? 24%. There are interesting open problems in designing mechanisms that allow time-shifting of load in such environments.

Authors and Affiliations

V. Praveen
Assistant Professor, N.S.N College of Engineering and Technology, Karur, Coimbatore, Tamilnadu, India
V. Gobu
Assistant Professor, N.S.N College of Engineering and Technology, Karur, Coimbatore, Tamilnadu, India
M. Kavitha
Research Analyst, IsClor Soft Solutions, Coimbatore, Tamilnadu, India
T. Suvaikin Punitha
Assistant Professor, N.S.N College of Engineering and Technology, Karur, Coimbatore, Tamilnadu, India

Cloud Assets, IPTV Assistance

  1. D. Banodkar, K. K. Ramakrishnan, S. Kalyanaraman, A. Gerber, and O. Spatscheck, “Multicast instant channel change in IPTV system,” in Proceedings of IEEE COMSWARE, January 2008.
  2. “Microsoft TV: IPTV edition,” from the website
  3. H. A. Lagar-Cavilla, J. A. Whitney, A. Scannell, R. B. P. Patchin, S. M. Rumble, E. de Lara, M. Brudno, and M. Satyanarayanan, “SnowFlock: Virtual Machine Cloning as a First Class Cloud Primitive,” ACM Transactions on Computer Systems (TOCS), 2011.
  4. V. Aggarwal, V. Gopalakrishnan, R. Jana, K. K. Ramakrishnan, and V. Vaishampayan, “Exploiting Virtualization for Delivering Cloud-based IPTV Services,” in Proc. of IEEE INFOCOM (mini-conference), Shanghai, April 2011.
  5. J. A. Stankovic, M. Spuri, K. Ramamritham, and G. C. Buttazzo, Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms. Norwell, MA, USA: Kluwer Academic Publishers, 1998.
  6. N. V. Thoai and H. Tuy, “Convergent algorithms for minimizing a concave function,” in Mathematics of operations Research, vol. 5, 1980.
  7. R. Urgaonkar, U. Kozat, K. Igarashi, and M. J. Neely, “Dynamic resource allocation and power management in virtualized data centers,” in Proceedings of IEEE IFIP NOMS, March 2010.
  8. C. L. Liu and J. W. Layland, “Scheduling Algorithms for Multiprogramming in a Hard Real Time Environment,” Journal of the ACM, vol. 20, no. 1, pp. 46–61, 1973.
  9. A. Dan, D. Sitaram, and P. Shahabuddin, “Scheduling Policies for an On-Demand Video Server with Batching,” in Proc. of ACM Multimedia, San Francisco, CA, October 1994, pp. 15–23.
  10. A. J. Stankovic, M. Spuri, K. Ramamritham, and G. Buttazzo, “Deadline Scheduling for Real-Time Systems EDF and Related Algorithms,” 1998, the Springer International Series in Engineering and Computer Science.
  11. L. I. Sennott, Stochastic Dynamic Programming and the Control of Queueing Systems. Wiley-Interscience, 1998.
  12. D. P. Bertsekas, “Dynamic Programming and Optimal Control,” in Athena Scientific, Blemont, Massachusetts, 2007.
  13. G. Ramamurthy and B. Sengupta, “Delay analysis of a packet voice multiplexer by the ?Di/D/1 Queue,” in Proceedings of IEEE Transactions on Communications, July 1991.
  14. H. Tuy, “Concave programming under linear constraints,” Soviet Math 5, pp. 1437–1440, 1964.
  15. S. Sergeev, “Algorithms to solve some problems of concave programming with linear constraints,” Automation and Remote Control, vol. 68, pp. 399–412, 2007, 10.1134/S0005117907030034. [Online]. Available:

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 118-124
Manuscript Number : CSEIT172635
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

V. Praveen, V. Gobu, M. Kavitha, T. Suvaikin Punitha, "Integration of Multi Server for Profit Efficiency in Cloud Computing", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.118-124 , November-December-2017.
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