Distributed applications and services requiring the transfer of large amounts of data have been developed and deployed world wide. The best effort model of the Internet cannot provide these applications with the so much needed quality of service guarantees, making necessary the development of file transfer scheduling techniques, which optimize the usage of network resources. In this paper we consider the high multiplicity scheduling of file transfers over multiple classes of paths with the objective of minimizing the makespan, when the files have divisible sizes. We also consider another objective, that of maximizing the total profit, in the context of some special types of mutual exclusion constraints (tree and clique constraint graphs).

Distributed applications and services requiring the transfer of large amounts of data have been developed and deployed all around the world. The best effort behavior of the Internet cannot offer to these applications the necessary Quality of Service (QoS) guarantees, making the development of data transfer scheduling techniques a necessity. In this paper we propose novel methods of efficiently using some wellknown data structures (e.g. the segment tree and the block partition), which can be implemented in a resource manager (e.g. Grid job scheduler, bandwidth broker) in order to serve quickly large numbers of advance resource reservation and allocation requests.Keywords-data transfer scheduling, block partitioning, segment tree, multidimensional data structures, algorithmic framework, range query, range update.2008 International Symposium on Parallel and Distributed Computing 978-0-7695-3472-5/08 $25.00

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We propose an algorithm that computes the length of a longest path in a cactus graph. Our algorithm can easily be modified to output a longest path as well or to solve the problem on cacti with edge or vertex weights. The algorithm works on rooted cacti and assigns to each vertex a two-number label, the first number being the desired parameter of the subcactus rooted at that vertex. The algorithm applies the divide-and-conquer approach and computes the label of each vertex from the labels of its children. The time complexity of our algorithm is linear in the number of vertices, thus improving the previously best quadratic time algorithm.