Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., http://www.caida.org/workshops/isma/0312/slides/rprasad-best.pdf; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005, our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.

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Modern large-scale grid computing for processing advanced science and engineering applications relies on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Our experiments consider the accuracy of the estimation, the convergence time, their level of intrusion in the grid links, and the ability to handle multiple simultaneous estimations. Overall, pathload represents a good solution to estimate available bandwidth in grid environments.