Abstract—Cloud computing is an emerging technology that greatly shapes our lives, where users run their jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud service provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs

in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced

and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon

centers which provide system voltage support, reduction in system losses and performance improvement. This paper presents an enhanced approach for DG placement in radial distribution feeders to reduce the real power loss and to improve the voltage profile. The DG placement approach involves

Abstract — Information Technology is widely used in health care for efficiently managing the Personal Health Records (PHR) in a cost effective manner. Under this scenario a computing paradigm (cloud computing) where the resources are provided as services will offer a ubiquitous access to medical

churn prediction model using a standard CRISP-DM methodology based on RFM and Boosted Trees Technique. To enhance the performance of the technique, hybrid approach for building classifiers is used. There is also a comparison between the performances of both techniques. Results show that enhanced boosted

-mance prediction models on different cloud storage services, and combines these models with workload specifications and high-level tenant goals to generate a cost-effective data place-ment and storage provisioning plan. Furthermore, we build Cast++ to enhance Cast’s optimization model by incorpo-rating data reuse

store for the cloud, which comprises both fast and slow storage devices. The resulting hybrid store exposes the tiering to tenants with a dynamic pricing model that is based on the tenants ’ usage and the provider’s desire to maximize profits. The tenants leverage knowledge of their work

ii iv vTo my parents vi vii Acknowledgements I would first and foremost extend my whole-hearted gratitude to my advisors, An-drea Arpaci-Dusseau and Remzi Arpaci-Dusseau. Andrea and Remzi are the reason that I had the opportunity for this exceptional Ph.D. journey. To this day, I still re-member the moment when they took me as their student and the joy and hope in my heart. Andrea and Remzi have showed me what systems research is like and how much fun and challenging it can be. Before this journey with them, I had always liked and believed in the beauty of mathematics and theory. My initial interest in systems research happened when I took Remzi’s Advanced Operating Systems

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree