
A Multi-Objective Genetic Algorithm for Cloud Service Reservation
Mr. Monilal S.*
Lecturer, Dept. of Computer Science, Govt Polytechnic College, Ezhukone, Kollam, India
Email: monilal.s@gmail.com
Abstract
Cloud is one of the emerging technologies in the computer industry. Several companies migrate to this technology due to reduction in maintenance cost. Several organizations provide cloud services such as SaaS, IaaS, and PaaS. Different organizations provide the same service with different service charges and waiting time. So customers can select services from these cloud providers according to their criteria like cost and waiting time. By using the ‘demand pricing’ strategy, providers can provide services with minimum cost without losing any income or valuable resource time. But the existing system does not provide any automated job scheduling considering consumer cost, provider benefit, consumer waiting, and provider idle time. This paper proposes a multi-objective genetic algorithm for solving this multivariable optimization problem. This system provides a new cloud brokering mechanism with cloud service discovery using this optimization technique. This paper considers IaaS. In this system, the user submits a job to the cloud. The cloud provides infrastructure to run this job and gives output to the user. Here, the aim of the user is to obtain output with minimum time and minimum cost. At the same time, the aim of the provider is to increase the income. For that, the provider runs more jobs within unit time. So we have to minimize consumer cost, consumer waiting time, and provider idle time, and maximize provider benefit.
Keywords
Cloud, Adhoc Genetic Algorithm, IaaS
References
- Ze Li, Student Member, IEEE, and Haiying Shen, Member, IEEE. “A QoS-Oriented Distributed Routing Protocol for Hybrid Wireless Networks”. IEEE Transactions on Mobile Computing, vol. 13, no. 3, March 2014.