In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are data- intensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitational- wave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in disk- space constrained environments, but can also improve the overall workflow performance.

The wide variety of remote sensors used in Intelligent Transportation Systems (ITS) applications (loops, probe vehicles, radar, cameras, etc.) has created a need for general methods by which data can be shared among agencies and users who own disparate computer systems. In this paper, we present a methodology that demonstrates that it is possible to create, encode, and decode a self-describing data stream using: 1) existing data description language standards; 2) parsers to enforce language compliance; 3) a simple content language that flows out of the data description language; and 4) architecture neutral encoders and decoders based on ASN.1.

Computational grids consisting of large and diverse sets of distributed resources have recently been adopted by organizations such as NASA and the NSF. One key component of a computational grid is an information service that provides information about resources, services and applications to users and their tools. This information is required to use a computational grid and therefore should be available in a timely and reliable manner. In this work, we describe the Globus information service, describe how this service is used, analyze its current performance, and perform trace-driven simulations to evaluate alternative implementations of this grid information service. We find that the majority of the transactions with the information service are changes to the data maintained by the service. We also find that of the three servers we evaluate, one of the commercial products provides the best performance for our workload and that the response time of the information service was not improved during the single experiment we performed with data distributed across two servers.

Abstract This paper describes an efficient method for constructing a tiling between a pair of planar contours. The problem is of interest in a number of domains, including medical imaging, biological research and geological reconstructions. Our method, based on ideas from multiresolution analysis and wavelets, requires O(n) space and appears to require O(n log n) time for average inputs, compared to the O(n2) space and O(n2 log n) time required by the optimizing algorithm of Fuchs, Kedem and Uselton1. The results computed by our algorithm are in many cases nearly the same as those of the optimizing algorithm, but at a small fraction of the computational cost. The performance improvement makes the algorithm usable for large contours in an interactive system. The use of multiresolution analysis provides an efficient mechanism for data compression by discarding wavelet coefficients smaller than a threshold value during reconstruction. The amount of detail lost can be controlled by appropriate choice of the threshold value. The use of lower resolution approximations to the original contours yields significant savings in the time required to display a reconstructed object, and in the space required to store it.

This paper is concerned with the problem of reconstructing the surfaces of three-dimensional objects, given a collection of planar contours representing cross-sections through the objects. This problem has important aplications in biomedical research and instruction, solid modeling, and industrial inspection. The method we describe produces a triangulated mesh from the data points of the contours which is then used in conjunction with a piecewise parametric surface-fitting algorithm to produce a reconstructed surface. The problem can be broken into four subproblems: the correspondence problem (which contours should be connected by the surface?), the tiling problem (how should the contours be connected?), the branching problem (what do we do when there are branches in the surface?), and the surface-fitting problem (what is the precise geometry of the reconstructed surface?) We describe our system for surface reconstruction from sets of contours with respect to each of these subproblems. Special attention is given to the correspondence and branching problems. We present a method that can handle sets of contours in which adjacent contours share a very contorted boundary, and we describe a new approach to solving the correspondence problem using a Minimum Spanning Tree generated from the contours.