Fast network re-optimization schemes for MPLS and optical networks

Bhatia, Randeep; Kodialam, Murali; Lakshman, T. V.

doi:10.1016/j.comnet.2005.03.004

“…Condition (5) says that, for each job, the total supply (or, equivalently, total demand), when summed over all time slices, must be equal to Z times the job size, where Z is the variable to be maximized. Condition (6) says that the capacity constraints must be satisfied for all edges on every time slice. Note that the allocated rate on link (l, k) for job i on slice j is f i (l,k) (j)/LEN t (j), where LEN t (j) is the length of slice j.…”

Section: Node-arc Formmentioning

confidence: 99%

Scheduling bulk file transfers with start and end times

Rajah

¹

,

Ranka

²

,

Xia

³

2008

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The advancement of optical networking technologies has enabled e-science applications that often require transport of large volumes of scientific data. In support of such data-intensive applications, we develop and evaluate control plane algorithms for scheduling bulk file transfers, where each transfer has a start time and an end time. We formulate the scheduling problem as a special type of the multi-commodity flow problem. To cope with the start and end time constraints of the file-transfer jobs, we divide time into uniform time slices. Bandwidth is allocated to each job on every time slice and is allowed to vary from slice to slice. This enables periodical adjustment of the bandwidth assignment to the jobs so as to improve a chosen performance objective: throughput of the concurrent transfers. In this paper, we study the effectiveness of using multiple time slices, the performance criterion being the tradeoff between achievable throughput and the required computation time. Furthermore, we investigate using multiple paths for each file transfer to improve the throughput. We show that using a small number of paths per job is generally sufficient to achieve near optimal throughput with a practical execution time, and this is significantly higher than the throughput of a simple scheme that uses single shortest path for each job.

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“…Condition (5) says that, for each job, the total supply (or, equivalently, total demand), when summed over all time slices, must be equal to Z times the job size, where Z is the variable to be maximized. Condition (6) says that the capacity constraints must be satisfied for all edges on every time slice. Note that the allocated rate on link (l, k) for job i on slice j is f…”

Section: Node-arc Formmentioning

confidence: 99%

Scheduling Bulk File Transfers with Start and End Times

Rajah

¹

,

Ranka

²

,

Xia

³

2007

Sixth IEEE International Symposium on Network Computing and Applications (NCA 2007)

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The advancement of optical networking technologies has enabled e-science applications that often require transport of large volumes of scientific data. In support of such data-intensive applications, we develop and evaluate control plane algorithms for scheduling bulk file transfers, where each transfer has a start time and an end time. We formulate the scheduling problem as a special type of the multi-commodity flow problem. To cope with the start and end time constraints of the file-transfer jobs, we divide time into uniform time slices. Bandwidth is allocated to each job on every time slice and is allowed to vary from slice to slice. This enables periodical adjustment of the bandwidth assignment to the jobs so as to improve a chosen performance objective: throughput of the concurrent transfers. In this paper, we study the effectiveness of using multiple time slices, the performance criterion being the tradeoff between achievable throughput and the required computation time. Furthermore, we investigate using multiple paths for each file transfer to improve the throughput. We show that using a small number of paths per job is generally sufficient to achieve near optimal throughput with a practical execution time, and this is significantly higher than the throughput of a simple scheme that uses single shortest path for each job.

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“…In [26] periodic re-optimization is used to determine new bandwidth allocation in optical networks. However, it is not assumed that users make advance reservations with requested end times.…”

Section: B Grid Network Reservationsmentioning

confidence: 99%

“…However, it is not assumed that users make advance reservations with requested end times. As a result, [26] does not have the admission control step. In WDM optical networks, some authors [27], [28] study advance reservation and re-routing of lightpaths at the granularity of a wavelength, but they do not consider the deadline constraint.…”

Section: B Grid Network Reservationsmentioning

confidence: 99%

Grid Network Dimensioning by Modeling the Deadline Constrained Bulk Data Transfers

Munir

¹

,

Primet²,

Welzl

³

2009

2009 11th IEEE International Conference on High Performance Computing and Communications

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Abstract-Grid applications need to move large amounts of data between distributed resources within deterministic time frames. In most cases it is possible to specify the volume and the deadline in advance. We formally define and analyze a mechanism of network reservations of bulk data transfer requests having opportunistic utilization of residual network capacity and analyze it using an M/M/1/N − RP S queue. We compare the analytical results of our mechanism with the analytical results obtained from an M/M/N/N queue, for the case in which there is no opportunistic sharing of residual capacity. We validate the analytical results through simulations in C++. The analytical model is also validated against ns-2 simulations taking into account network level details (IP and TCP protocols), showing remarkably good coherence even under heavy loads. The model is orders of magnitude faster than simulation, which enables its application for capacity planning of Grid networks (also known as network dimensioning), and to enforce Connection Admission Control (CAC) under the practical hypothesis of a dominating bottleneck on the transfer route.

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Fast network re-optimization schemes for MPLS and optical networks

Cited by 19 publications

References 24 publications

Scheduling bulk file transfers with start and end times

Scheduling bulk file transfers with start and end times

Scheduling Bulk File Transfers with Start and End Times

Grid Network Dimensioning by Modeling the Deadline Constrained Bulk Data Transfers

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