Big Iron, Big Data, and Big Identity

While High-Performance Computing (HPC) typically focuses on very large, parallel machines, i.e., Big Iron, running massive numerical codes, the importance of extracting knowledge from massive amounts of information, i.e., Big Data, has been clearly recognized. While many massive data sets can be produced within a single administrative domain, many more massive data sets can be, and must be, assembled from multiple sources. Aggregating data from multiple sources can be a tedious task. First, the locations of the desired data must be known. Second, access to the data sets must be allowed. For publicly accessible data, this may not pose a serious problem. However, many application domains and user groups may wish to facilitate, and have some degree of control over, how their resources are discovered and shared. Such collaboration requirements are addressed by federation management technologies. In this paper, we argue that effective, widely-adopted federation management tools, i.e., Big Identity, are critical for enabling many Big Data applications, and will be central to how the Internet of Things is managed. To this end, we re-visit the NIST cloud deployment models to extract and identify the fundamental aspects of federation management: crossing trust boundaries, trust topologies, and deployment topologies. We then review possible barriers to adoption and relevant, existing tooling and standards to facilitate the emergence of a common practice for Big Identity.