Self-adaptive configuration of visualization pipeline over wide-area networks

Next-generation scientific applications require the capabilities to visualize large archival datasets or on-going computer simulations of physical and other phenomena over wide-area network connections. To minimize the latency in interactive visualizations across wide-area networks, we propose an approach that adaptively decomposes and maps the visualization pipeline onto a set of strategically selected network nodes. This scheme is realized by grouping the modules that implement visualization and networking subtasks, and mapping them onto computing nodes with possibly disparate computing capabilities and network connections. Using estimates for communication and processing times of subtasks, we present a polynomial-time algorithm to compute a decomposition and mapping to achieve minimum end-to-end delay of the visualization pipeline. We present experimental results using geographically distributed deployments to demonstrate the effectiveness of this method in visualizing datasets from three application domains.