[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] In recent years, most scientific research in both academia and industry has become increasingly data-driven. According to market estimates, spending related to supporting scientific data-intensive research is expected to increase to $5.8 billion by 2018. Particularly for data-intensive scientific fields such as bioscience, or particle physics within academic environments, data storage/processing facilities, expert collaborators and specialized computing resources do not always reside within campus boundaries. With the growing trend of large collaborative partnerships involving researchers, expensive scientific instruments and high performance computing centers, experiments and simulations produce peta-bytes of data viz., Big Data, that is likely to be shared and analyzed by scientists in multi-disciplinary areas. Federated multi-cloud resource allocation for data-intensive application workflows is generally performed based on performance or quality of service (i.e., QSpecs) considerations. At the same time, end-to-end security requirements of these workflows across multiple domains are considered as an afterthought due to lack of standardized formalization methods. Consequently, diverse/heterogenous domain resource and security policies cause inter-conflicts between application's security and performance requirements that lead to sub-optimal resource allocations, especially when multiple such applications contend for limited resources. In this thesis, a joint performance and security-driven federated resource allocation scheme for data-intensive scientific applications is presented. In order to aid joint resource brokering among multi-cloud domains with diverse/heterogenous security postures, the definition and characterization of a data-intensive application's security specifications (i.e., SSpecs) is required. Next, an alignment technique inspired by Portunes Algebra to homogenize the various domain resource policies (i.e., RSpecs) along an application's workflow lifecycle stages is presented. Using such formalization and alignment, a near optimal cost-aware joint QSpecs-SSpecs-driven, RSpecs-compliant resource allocation algorithm for multi-cloud computing resource domain/location selection as well as network path selection, is proposed. We implement our security formalization, alignment, and allocation scheme as a framework, viz., "OnTimeURB" and validate it in a multi-cloud environment with exemplar data-intensive application workflows involving distributed computing and remote instrumentation use cases with different performance and security requirements.
Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments
