Genetic algorithm-based cost minimization pricing model for on-demand IaaS cloud service

Cloud computing has emerged as the most effective distributed computing paradigm. It has grabbed the attention of many organizations owing to its business prospects and significant features like agility and flexibility. Dynamic scalability of cloud services and availability of number of homogenous cloud service providers in market make it difficult for the provider to fix the prices of cloud services, especially on-demand Infrastructure-as-a-Service cloud service instances. It is quite problematic for provider to map the dynamics of prices with the variation in service requirement and satisfying the users’ quality of service requirement simultaneously. At the same time, it is very essential for the provider to determine the lower bound price of the services beyond which he could not afford the provisioning of the services. This paper presents dynamic demand-based pricing model for on-demand IaaS cloud service instances that will assist the provider to dynamically determine the price of provisioning the cloud services by considering the provider’s and users’ utility concurrently. Genetic algorithm is applied for the optimized evaluation users’ request parameters and provider’s computation capacity that will minimize the cost of execution. Experimental results demonstrate that price evaluation is more efficient and users’ utility increases considerably using the proposed framework in comparison with the existing utility-based pricing model.