Optimal Resource Estimation Policy Selection for Ecommerce Applications in Cloud

Cloud Computing is growing exponentially across organizations in various domains and it has a vast impact on the way software gets developed and tested. The web based applications these days have configuration settings different from deployment requirements. The main focus of Cloud Computing is to deliver reliable, secured, fault-tolerant and elastic infrastructures for hosting an E-Commerce application. Scheduling policies and allocation policies for resources which affect the performance and utilization of cloud infrastructure (i.e. hardware, software services) for various E-Commerce application under varying load and system size is highly challenging problem to deal with. Performance analysis and optimal resource management policies allows cloud service providers to improve their Quality of Service (QOS). This work focuses on the process of selecting the best resource estimation policy for a given workload from various policies such as, Maximum Log-Likelihood, Maximum Product of Spacing Estimator, and Probability Weighted Movements. Detailed experimentation has been carried out for using the Amazon Web Service(AWS) public cloud, mimicking it on the cloudsim simulator. The modeling of workload is quiet challenging due to the unavailability of trace logs for analysis. Hence, in this paper, workload is generated based on application model for E-commerce application considering varying user behavior relating to different user profiles. The amount of resources consumed is closely monitored and a resource usage model has been developed and validated to choose the best estimation policy.