CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

SB-PSO based Secure Moving Average Time-based Fuzzy Resource Provisioning Approach (SBPSO-MATFRPA) with RSA

by Sweta Dey, Piyush Kumar Garg
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 31
Year of Publication: 2018
Authors: Sweta Dey, Piyush Kumar Garg
10.5120/ijca2018916824

Sweta Dey, Piyush Kumar Garg . SB-PSO based Secure Moving Average Time-based Fuzzy Resource Provisioning Approach (SBPSO-MATFRPA) with RSA. International Journal of Computer Applications. 180, 31 ( Apr 2018), 36-41. DOI=10.5120/ijca2018916824

@article{ 10.5120/ijca2018916824,
author = { Sweta Dey, Piyush Kumar Garg },
title = { SB-PSO based Secure Moving Average Time-based Fuzzy Resource Provisioning Approach (SBPSO-MATFRPA) with RSA },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 31 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number31/29246-2018916824/ },
doi = { 10.5120/ijca2018916824 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:24.742850+05:30
%A Sweta Dey
%A Piyush Kumar Garg
%T SB-PSO based Secure Moving Average Time-based Fuzzy Resource Provisioning Approach (SBPSO-MATFRPA) with RSA
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 31
%P 36-41
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

SB-PSO and RSA based environment consists a lots of end user’s requests for resources simultaneously or sequentially in a dynamic environment and it is a big challenge for this environment. In this paper we propose a method: combination of both SB-PSO and Fuzzy Logic with RSA that allocates requested resources by the end user dynamically so that the available resources are fully utilized in an efficient manner. Here the Monitoring components are continuously monitored the requested resources and allocates them accordingly. Here the incoming requests are grouped together and satisfied in such a way that the maximum numbers of available resources are provisioned appropriately and our proposed approach is efficiently measured by finding the performance of resource allocation.

References
  1. Jing Bi, Zhiliang Zhu, Ruixiong Tian, Qingbo Wang, “Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center”, Proceedings of 2010 IEEE 3rd International Conference on Cloud Computing, 978-0-7695-4130-3/10
  2. Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., & Stoica, I. (2009). Above the clouds: A Berkeley view of cloud computing. Berkeley, Rep. UCB/EECS, 28, 13.
  3. Jiangtao Zhang, Hejiao Huang, Xuan Wang, “ Resource Provisioning Algorithm in Cloud Computing: A Survey” , Proceedings of the Journal of Network and Computer Applications(2016), j.jnca.2015.12.018.
  4. Ghobaei-Arani, M., Jabbehdari, S., & Pourmina, M. A. (2018). An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Future Generation Computer Systems, 78, 191-210.
  5. Yu Sun, Jules White, Sean Eade, Douglas C. Schmidt, "ROAR: A QoS-oriented modeling framework for automated cloud resource allocation and optimization," Elsevier The Journal of Network and Computer Applications, Volume: 41, 2014, pp:424–440.
  6. Chunlin Li, La Yuan Li," Optimal resource provisioning for cloud computing environment," Springer The Journal of Super Computing, Volume:62, Issue:2 ,November 2012, pp:989-1022.
  7. Sunilkumar S. Manvi, GopalKrishna Shyam," Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey," Elsevier Journal of Network and Computer Applications , Volume:41, 2014, pp:424–440.
  8. Weijie Shi, Linquan Zhang, Chuan Wu, Zongpeng Li, and Francis C. M. Lau, " An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing, " IEEE/ACM Transactions on Networking, Volume:24 Issue:4,August 2016 , pp-2060-2073.
  9. Kennedy, J. (2010). Particle swarm optimization. In Encyclopedia of Machine Learning (pp. 760-766). Springer US.
  10. Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. Evolutionary Computation, IEEE Transactions on, 6(1), 58-73.
  11. Zhang, R. (2011). A particle swarm optimization algorithm based on local perturbations for the job shop scheduling problem. International Journal of Advancements in Computing Technology, 3(4), 256-264.
  12. Yang, C. H., Tsai, S. W., Chuang, L. Y., & Yang, C. H. (2011). A modified particle swarm optimization for global optimization. IJACT: International Journal of Advancements in Computing Technology, 3(7), 169-189.
  13. Yang, J., & Yang, J. (2011). Intelligence optimization algorithms: A survey. IJACT: International Journal of Advancements in Computing Technology, 3(4), 144-152.
  14. Samal, P., & Mishra, P. (2013). Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing. International Journal of computer science and Information Technologies, 4(3), 416-419.
  15. Sran, N., & Kaur, N. (2013). Comparative analysis of existing load balancing techniques in cloud computing. International Journal of Engineering Science Invention, 2(1), 60-63.
  16. Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer applications, 34(1), 1-11.
  17. Megharaj, G., & Mohan, K. G. A Survey on Load Balancing Techniques in Cloud Computing.
  18. Kansal, N. J., & Chana, I. (2012). Existing load balancing techniques in cloud computing: a systematic review. Journal of Information Systems and Communication, 3(1), 87.
  19. Chaczko, Z., Mahadevan, V., Aslanzadeh, S., & Mcdermid, C. (2011, September). Availability and load balancing in cloud computing. In International Conference on Computer and Software Modeling, Singapore (Vol. 14).
  20. Roy, A., & Dutta, D. (2013). Dynamic load balancing: improve efficiency in cloud computing. International Journal of Emerging Research in Management Technology, 78-82.
  21. Kazem, A. A. P., Rahmani, A. M., & Aghdam, H. H. (2008, August). A modified simulated annealing algorithm for static task scheduling in grid computing. In Computer Science and Information Technology, 2008. ICCSIT'08. International Conference on (pp. 623-627). IEEE.
  22. LD, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing, 13(5), 2292-2303.
  23. Kawser, M. T., Hasib, M. A. B., Hasin, A. R., Sadik, A. M., & Razu, I. K. (2012). Performance comparison between round robin and proportional fair scheduling methods for LTE. International Journal of Information and Electronics Engineering, 2(5), 678.
  24. Dr.S.Padmavathi, N.Soundarya, P.K.Soniha, S.Srimathi (2017). Dynamic Resource Provisioning and Monitoring for Cloud Computing. International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, 978-1-5090-4778 9/17.
  25. Kaneria, O., & Banyal, R. K. (2016, November). Analysis and improvement of load balancing in Cloud Computing. In ICT in Business Industry & Government (ICTBIG), International Conference on (pp. 1-5). IEEE.
  26. Lee, R., & Jeng, B. (2011, October). Load-balancing tactics in cloud. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011 International Conference on (pp. 447-454). IEEE.
  27. Lorpunmanee, S., Sap, M. N., Abdullah, A. H., & Chompoo-inwai, C. (2007). An ant colony optimization for dynamic job scheduling in grid environment. International Journal of Computer and Information Science and Engineering, 1(4), 207-214.
  28. Kaur, J., & Kinger, S. (2014). A survey on load balancing techniques in cloud computing. International Journal of Science and Research (IJSR), 3(6), 2662-2665.
  29. Zhou, M., Zhang, R., Zeng, D., & Qian, W. (2010, October). Services in the cloud computing era: A survey. In Universal Communication Symposium (IUCS), 2010 4th Internationa l(pp. 40-46). IEEE.
  30. Buyya. R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
  31. Bhanot, R., & Hans, R. (2015). A review and comparative analysis of various encryption algorithms. International Journal of Security and Its Applications, 9(4), 289-306.
  32. Longhua, L. (2012, July). A novel design of OTP-based authentication scheme using smart phones and 2-D barcodes for the visually impaired. In Proceedings of the 6th International Conference on Rehabilitation Engineering & Assistive Technology (p. 8). Singapore Therapeutic, Assistive & Rehabilitative Technologies (START) Centre.
  33. Gallager, R. G. (1978, September). Conflict resolution in random access broadcast networks. In Proc. of the AFOSR Workshop in Communication Theory and Applications (pp. 74-76).
  34. Mosely, J. (1979). An efficient contention resolution algorithm for multiple access channels (No. LIDS-TH-918). MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS.
  35. Vvedvenskaya, N.D., and Pinsker, M.S., “The Capacity of FCFS Algorithms,” in Proc. 3rd Intern. Seminar on Convolutional Codes and Multiuser Communication, Sochi, U.S.S.R, 1987, pp. 32-34, [in Russian].
Index Terms

Computer Science
Information Sciences

Keywords

Resource provisioning RSA FCFS algorithm Round-Robin algorithm Throttled algorithm fuzzy logic SB-PSO Cloud.

Powered by PhDFocusTM