Physical Sciences and Mathematics Commons^™

Performance Optimization And Energy Efficiency Of Big-Data Computing Workflows, Tong Shu

Dissertations

Next-generation e-science is producing colossal amounts of data, now frequently termed as Big Data, on the order of terabyte at present and petabyte or even exabyte in the predictable future. These scientific applications typically feature data-intensive workflows comprised of moldable parallel computing jobs, such as MapReduce, with intricate inter-job dependencies. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on workflow completion time, energy consumption, and financial cost if executed in clouds, which remains largely unexplored. This dissertation conducts an in-depth investigation into the properties of moldable jobs and provides an …

Protecting Sensitive Data In Clouds Using Active Data Bundles And Agent-Based Secure Multi-Party Computation, Akram Y. Sarhan

Dissertations

Protection of data in cloud computing including distributed environments is a critical concern for many enterprises. This study proposes a solution that protects sensitive data outsourced to a cloud throughout their entire life cycle—both in the cloud as well as outside of the cloud (e.g., during transmission to or from the cloud). The solution enhances the existing data protection approach known as Active Bundle scheme, which uses a Trusted Third Party (ABTTP).

The Active Data Bundle (ADB) was formerly called an Active Bundle (AB). It is a software construct that encapsulates data, metadata, and a virtual machine (VM). The metadata …