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advance reservation

advance reservation - 1 Advance Reservation and Scheduling...

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1 Advance Reservation and Scheduling for Bulk Transfers in Research Networks Kannan Rajah, Sanjay Ranka and Ye Xia Abstract Data-intensive e-science collaborations often require the transfer of large files with predictable performance. To meet this need, we design novel admission control 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 paths 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 admission control and scheduling decisions. Our design combines the following elements into a cohesive optimization-based framework: advance reservation, multi-path routing, and bandwidth reassignment via periodic re-optimization. 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. I. INTRODUCTION The advance of communication and networking technologies, together with the computing and storage technolo- gies, is dramatically changing the ways how scientific research is conducted. A new term, e-science , has emerged to describe the “large-scale science carried out through distributed global collaborations enabled by networks, requiring access to very large scale data collections, computing resources, and high-performance visualization” [1]. Well- quoted e-science (and the related grid computing [2]) examples include high-energy nuclear physics (HEP), radio astronomy, geoscience and climate studies. The need for transporting large volume of data in e-science has been well-argued [3], [4]. For instance, the HEP data is expected to grow from the current petabytes (PB) ( 10 15 ) to exabytes ( 10 18 ) by 2012 to 2015. In particular, the Large Hadron Collider facility at CERN is expected to generate petabytes of experimental data every year, The authors are with the Computer and Information Science and Engineering Department, University of Florida, Gainesville, FL. Ye Xia is the corresponding author. Email: [email protected], Phone: 352-392-2714, Fax:352-392-2714 This work was supported in part by the National Science Foundation (NSF) under Grant ITR 0325459 and 0427110. Any findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF. The authors would like to thank Rick Cavanaugh and Paul Avery for several discussions and insights.
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2 for each experiment. In addition to the large volume, as noted in [5], e-scientists routinely request schedulable
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