Concurrent Bandwidth Reservation Strategies for Big Data Transfers in High-Performance Networks

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Because of the deployment of large-scale experimental and computational scientific applications, big data is being generated on a daily basis. Such large volumes of data usually need to be transferred from the data generating center to remotely located scientific sites for collaborative data analysis in a timely manner. Bandwidth reservation along paths provisioned by dedicated high-performance networks (HPNs) has proved to be a fast, reliable, and predictable way to satisfy the transfer requirements of massive time-sensitive data. In this paper, we study the problem of scheduling multiple bandwidth reservation requests (BRRs) concurrently within an HPN while achieving their best average transfer performance. Two common data transfer performance parameters are considered: the Earliest Completion Time (ECT) and the Shortest Duration (SD). Since not all BRRs in one batch can oftentimes be successfully scheduled, the problem of scheduling all BRRs in one batch while achieving their best average ECT and SD are converted into the problem of scheduling as many BRRs as possible while achieving the average ECT and SD of scheduled BRRs, respectively. The aforementioned two problems are proved to be NP-complete problems. Two fast and efficient heuristic algorithms with polynomial-time complexity are proposed. Extensive simulation experiments are conducted to compare their performance with two proposed naive algorithms in various performance metrics. Performance superiority of these two fast and efficient algorithms is verified.



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