Far, Behrouz H.Wang, MeaMireslami, Seyedehmehrnaz2018-08-272018-08-272018-08-21Mireslami, S. (2018). Cost and Performance Optimization for Cloud-Based Web Applications Deployment (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32837http://hdl.handle.net/1880/107657Cloud computing offers a pool of various cloud resources, including scalable computing instances, database instances, storage, network bandwidth, etc. which are delivered to customers in an on-demand or reserved manner. In recent years, cloud computing has become a major enablement for businesses and researchers to reduce the deployment costs by externalizing their resources in the cloud environment. Achieving an optimal set of cloud resources for web application deployment among different public cloud providers is a challenge that becomes more difficult when cloud customers tend to optimize both deployment costs and Quality of Service (QoS). Furthermore, due to lack of understanding of the pricing model and the cloud IaaS, a customer may pay more than necessary or may not fully utilize the purchased resources. In this thesis, to tackle these challenges, first, a QoS-aware cost optimization algorithm is proposed that finds the most cost-effective cloud resources for web application deployment. The proposed algorithm maps the minimum required resources for the web application to minimize the deployment costs according to the price model set by the cloud providers. In the next stage, a multi-cloud datacenters cost optimization algorithm is proposed to distribute the cloud resources in different cloud datacenters to improve the web application availability and maintain QoS for geographically distributed user demands. To solve the cloud-based deployment problem from the cloud customer’s point of view, it is vital to balance the two conflicting objectives of deployment costs and QoS performance. Therefore, in this research, a multi-objective optimization algorithm is proposed that minimizes cost and maximizes QoS performance simultaneously by providing a balanced trade-off. Finally, a hybrid method to allocate resources according to the dynamic user demands is developed which includes the reservation and dynamic provision phases. The total deployment cost of each phase is formulated as the optimization objective and a stochastic optimization approach is developed to model the uncertainties in the user demands as random variables.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Cloud ComputingResource AllocationQuality of serviceWeb application deploymentGeometric programmingOptimizationEducation--MathematicsComputer ScienceEngineeringdoctoral thesis10.11575/PRISM/32837