A Neoteric Optimization Methodology for Cloud Networks

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Tayibia Bazaz 1,* Sherin Zafar 2

1. Department of Computer Science and Engineering, School of Engineering Science and Technology, Jamia Hamdard (Hamdard University), India, New Delhi

2. Department of Computer Science and Engineering, School of Engineering Science and Technology, Jamia Hamdard (Hamdard University), New Delhi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2018.06.04

Received: 24 Mar. 2018 / Revised: 1 Apr. 2018 / Accepted: 10 Apr. 2018 / Published: 8 Jun. 2018

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


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