Self-Adaptive Configuration of Visualization Pipeline Over Wide-Area Networks

Abstract

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.

Integration of sensing and computing in an intelligent decision support system for homeland security defense

“…In practical applications, the bandwidth of a network transport path can be measured using an active traffic measurement technique based on a linear regression model as described in [42], and the processing time of a computing or visualization module can be measured using similar techniques [40]. These measuring methods achieve high accuracy in performance estimation as evidenced by extensive experimental results produced in real networks.…”

GEVP: Grid Enabled Visualization Pipeline

“…The introduced techniques result most often in building a cluster of nodes to provide the necessary computational power [1], [2], [3], [4], [5], [6], [6]. Other implementation focuses on remote visualization such as [7], [8], [9], [10], [11], [12].…”

“…In addition to other ongoing implementations that focuses on providing prediction strategies and methods, such as [22], [23] and [24]. However, [10] presented a self-adapt framework over wide-area networks. Their work was based on performance estimations and predicting pipeline decomposition on the network.…”