International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.10, No.6, Jun. 2018

A Neoteric Optimization Methodology for Cloud Networks

Full Text (PDF, 512KB), PP.27-34

Views:108   Downloads:2


Tayibia Bazaz, Sherin Zafar

Index Terms

End to End Delay;Genetic Algorithm (GA);Meta-heuristic Algorithm; Optimization;Packet Delivery Ratio;Quality of Service (QOS)


Cloud computing is distinctively marked by its capability of providing on demand virtualized IT resources in a pay as you go fashion. Due to its popularity, the cloud computing users are increasing day by day which has become an important challenge for cloud providers. They need to serve their users in a best possible manner. The providers should not only provide their users a secure access to resources but also need to maintain a proper balance of QOS parameters like throughput, end-to-end delay, packet delivery ratio, jitter, response time, etc. The paper proposes an approach of using a meta-heuristic algorithm called Genetic Algorithm (GA) to optimize QOS parameters like packet delivery ratio and end to end delay in cloud networks. The intelligent optimization algorithms address several shortcomings of existing protocols by improving QOS parameters in an optimum manner. The results are simulated through MATLAB based simulator and the simulated results of proposed approach exhibit optimized parameters when compared to conventional method of shortest path cloud routing approach.

Cite This Paper

Tayibia Bazaz, Sherin Zafar, " A Neoteric Optimization Methodology for Cloud Networks", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 27-34, 2018.DOI: 10.5815/ijmecs.2018.06.04


[1]Q. Zhang, L. Cheng and R. Boutab, “Cloud computing: state-of-the art and research challenges” Journal of Internet Services and Applications, Volume 1, 2010, pp. 7-18.

[2]P. Kaur, T. Kaur “A New Era to improve the QOS on cloud using hybrid approach” International Journal Of Engineering And Computer Science, Volume 5, Issue 6, 2016, pp. 16808-16814.

[3]J. M. Pedersen, M. T.  Riaz, J. C. Junior, B. Dubalski, D. Ledzinski, A. Patel “Assessing Measurements of QOS for global Cloud Computing Services” Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing, 2011, pp. 682-689.

[4]F. Glover, K. Sorensen "Meta-heuristics", 2016. [Online]. Available: #Definition.

[5]S. Gupta, D. S. N. Panda, D. B. Bhushan “Hash Key Based Effective Algorithm for Security in Cloud Infrastructure” International Journal of Computer Science and Technology, Volume 6, Issue 3, 2015, pp. 25-30. 

[6]K. Alhamazani, R. Ranjan, P. Jayaraman, K. Mitra, F. Rabhi, D. Georgakopoulos, L. Wang "Cross-Layer Multi-Cloud Real-Time Application QOS Monitoring and Benchmarking As-a-Service Framework" IEEE Transactions on Cloud Computing, 2015..

[7]B. Chitra, M. Srikrishna, A. Naveenkumar "A Survey on Optimizing the QOS during Service Level Agreement in Cloud" International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 3, 2013.

[8]T. Chen, R. Bahsoon, G. Theodoropoulos “Dynamic QOS Optimization Architecture for Cloudbased DDDAS" International Conference on Computational Science, Procedia Computer Science, 2013, pp. 1881-1890..

[9]K. Dasgupta, B. Mandal, P. Dutta, J. Mandal, S. Dam “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing" International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), 2013, pp. 340-347.

[10]K. Bhatt, D. M. Bundele “Study and Impact of CloudSim on the run of PSO in Cloud Environment” International Journal of Innovations in Engineering and Technology, Volume 2, Issue 4, 2013, pp. 254-262.

[11]K. Xiong, H. Perros "Service Performance and Analysis in Cloud Computing", Annual SRRI Global Conference, 2012, pp.  693-700.

[12]D. Aggarwal, H. Jaiswal, I. Singh, k. Chandrasekaran “An Evolutionary Approach to Optimizing Cloud Services” Journal of Computer Engineering and Intelligent Systems, Volume 3, Issue 4, 2012, pp. 47-54.

[13]C. Wook, R. S. Ramakrishna “A Genetic Algorithm for Shortest Path Routing Problem and the Sizing of Populations” IEEE Transactions On Evolutionary Computation, Volume 6, Issue 6, 2002, pp.  566-579.

[14]P. Ponnusamy, S. Abinaya “Virtual network Routing in Cloud Computing Environment” International Conference on Computer Communication and Informatics (ICCCI), INDIA, 2016.  

[15]S. Zafar, M. K. Soni, M. M. S. Beg “An Optimized Genetic Stowed Biometric Approach to Potent QOS in MANET” Procedia Computer Science 62, pp. 2015, 410-418. 

[16]S. Zafar, M.K. Soni “Biometric Stationed Authentication Protocol (BSAP) Inculcating MetaHeuristic Genetic Algorithm” I.J. Modern Education and Computer Science, 2014, pp. 28-35.

[17]S. Zafar, M. K. Soni "A Novel Crypt-Biometric Perception Algorithm to Protract Security in MANET” I.J. Computer Network and Information Security, 2014, pp. 64-71.

[18]S. Zafar, M. K. Soni “Secure Routing in MANET through Crypt-Biometric Technique” Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), 2014.

[19]S. Zafar, M. K. Soni, M. M. S. Beg “QOS Optimization in Networks through Meta-Heuristic Quartered Genetic Approach” ICSCTI, IEEE, 2015.

[20]V. Singh, V. Kumar and K. Bansal “Research On Application of Perceived QOS Guarantee Through Infrastructure Specific Traffic Parameter Optimization” International Journal of Computer Network and Information Security, Volume 6, Issue 3, 2014, pp. 59-65.

[21]A. Bardsiri, S. Hashemi “QOS Metrics for Cloud Computing Services Evaluation” Intrnational Journal Of Intelligent System and Applications, Volume 6, Issue 12, 2014, pp. 27-33.

[22]T. Bazaz “Security Enhancement in Cloud Networks By Neoteric Techniques” IJARCS, Volume 8, Issue 2, 2017, pp. 29-32.