End-to-end network QoS via scheduling of flexible resource reservation requests
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Design and implementation of an intelligent end-to-end network QoS system
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Resource co-allocation framework based on hybrid gaming model in grid environments
International Journal of Grid and Utility Computing
Improving user QoS by relaxing resource reservation policy in high-performance grid environments
International Journal of Grid and Utility Computing
International Journal of Computational Science and Engineering
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Data-intensive e-science collaborations often require the transfer of large files with predictable performance. To meet this need, we design novel admission control (AC) and scheduling algorithms for bulk data transfer in research networks for e-science. Due to their small sizes, the research networks can afford a centralized resource management platform. In our design, each bulk transfer job request, which can be made in advance to the central network controller, specifies a start time and an end time. If admitted, the network guarantees to complete the transfer before the end time. However, there is flexibility in how the actual transfer is carried out, that is, in the bandwidth assignment on each allowed path of the job on each time interval, and it is up to the scheduling algorithm to decide this. To improve the network resource utilization or lower the job rejection ratio, the network controller solves optimization problems in making AC and scheduling decisions. Our design combines the following elements into a cohesive optimization-based framework: advance reservations, multipath routing, and bandwidth reassignment via periodic reoptimization. We evaluate our algorithm in terms of both network efficiency and the performance level of individual transfer. We also evaluate the feasibility of our scheme by studying the algorithm execution time.