Scalable, Data- intensive Network Computation

To enable groups of collaborating researchers at different locations to effectively share large datasets and investigate their spontaneous hypotheses on the fly, we are interested in de- veloping a distributed system that can be easily leveraged by a variety of data intensive applications. The system is composed of (i) a number of best effort logistical depots to en- able large-scale data sharing and in-network data processing, (ii) a set of end-to-end tools to effectively aggregate, manage and schedule a large number of network computations with attendant data movements, and (iii) a Distributed Hash Table (DHT) on top of the generic depot services for scalable data management. The logistical depot is extended by following the end-to-end principles and is modeled with a closed queuing network model. Its performance characteristics are studied by solving the steady state distributions of the model using local balance equations. The modeling results confirm that the wide area network is the performance bottleneck and running concurrent jobs can increase resource utilization and system throughput. As a novel contribution, techniques to effectively support resource demanding data- intensive applications using the ¯ne-grained depot services are developed. These techniques include instruction level scheduling of operations, dynamic co-scheduling of computation and replication, and adaptive workload control. Experiments in volume visualization have proved the effectiveness of these techniques. Due to the unique characteristic of data- intensive applications and our co-scheduling algorithm, a DHT is implemented on top of the basic storage and computation services. It demonstrates the potential of the Logistical Networking infrastructure to serve as a service creation platform

Concept-driven visualization for terascale data analytics

Over the past couple of decades the amount of scientific data sets has exploded. The science community has since been facing the common problem of being drowned in data, and yet starved of information. Identification and extraction of meaningful features from large data sets has become one of the central problems of scientific research, for both simulation as well as sensory data sets. The problems at hand are multifold and need to be addressed concurrently to provide scientists with the necessary tools, methods, and systems. Firstly, the underlying data structures and management need to be optimized for the kind of data most commonly used in scientific research, i.e. terascale time-varying, multi-dimensional, multi-variate, and potentially non-uniform grids. This implies avoidance of data duplication, utilization of a transparent query structure, and use of sophisticated underlying data structures and algorithms.Secondly, in the case of scientific data sets, simplistic queries are not a sufficient method to describe subsets or features. For time-varying data sets, many features can generally be described as local events, i.e. spatially and temporally limited regions with characteristic properties in value space. While most often scientists know quite well what they are looking for in a data set, at times they cannot formally or definitively describe their concept well to computer science experts, especially when based on partially substantiated knowledge. Scientists need to be enabled to query and extract such features or events directly and without having to rewrite their hypothesis into an inadequately simple query language. Thirdly, tools to analyze the quality and sensitivity of these event queries itself are required. Understanding local data sensitivity is a necessity for enabling scientists to refine query parameters as needed to produce more meaningful findings.Query sensitivity analysis can also be utilized to establish trends for event-driven queries, i.e. how does the query sensitivity differ between locations and over a series of data sets. In this dissertation, we present an approach to apply these interdependent measures to aid scientists in better understanding their data sets. An integrated system containing all of the above tools and system parts is presented

Visualization Viewpoints: Dynamic Sharing of Large-Scale Visualization

Visualization is a research tool that computational scientists use for qualitative exploration, hypothesis verification, and result presentation. Driven by needs for large user groups to collaborate across geographical distances, visualization must now also serve as an effective means to share concrete data as well as abstract ideas over the Internet. Yet there is simply no expeditious and practical way for users collaborating in this wide area to share large visualizations in a dynamic fashion. Using distributed heterogeneous resources as a basic parallel infrastructure to compute visualization could provide great potential usability, scalability, and cost efficiency. To justify our viewpoint, we describe a sample system of this nature and demonstrate its efficacy with a recently generated real-world large data se