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Title: Flexible Bandwidth Scheduling for Streaming Data Movement Over Dedicated Networks

Abstract

A wide range of scientific disciplines are generating large amounts of data at a high speed, which must be transferred to remote sites for real-time processing. Reserving bandwidths over dedicated channels in high-performance networks (HPNs) within a specified time interval has proved to be an effective solution to such high-demanding data transfer. Given a bandwidth reservation request, if the desired bandwidth within the specified time interval cannot be satisfied, most of the existing scheduling algorithms simply reject the request, which would immediately terminate the application. One reasonable approach to mitigate this issue is to provide an alternative bandwidth reservation option to schedule the desired bandwidth within the time interval closest to the specified one. We propose a flexible bandwidth reservation algorithm that considers both the best and alternative bandwidth reservation options for a given request. Extensive simulations are conducted to show the superior performance of the proposed scheduling algorithm compared with a heuristic approach adapted from existing scheduling algorithms.

Authors:
 [1];  [2];  [3]; ORCiD logo [4];  [5];  [1]
  1. California State University, Dominguez Hills
  2. Montclair State University, New Jersey
  3. New Jersey Institute of Technology
  4. ORNL
  5. Chengdu University of Information Technology
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1474518
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Symposium on Sensor Networks, Systems and Security (ISSNSS 2017) - Lakeland, Florida, United States of America - 8/31/2017 4:00:00 AM-9/2/2017 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Zuo, Liudong, Zhu, Michelle, Wu, Chase Qishi, Rao, Nageswara S., Han, Min, and Wang, Anyi. Flexible Bandwidth Scheduling for Streaming Data Movement Over Dedicated Networks. United States: N. p., 2018. Web. doi:10.1007/978-3-319-75683-7_14.
Zuo, Liudong, Zhu, Michelle, Wu, Chase Qishi, Rao, Nageswara S., Han, Min, & Wang, Anyi. Flexible Bandwidth Scheduling for Streaming Data Movement Over Dedicated Networks. United States. doi:10.1007/978-3-319-75683-7_14.
Zuo, Liudong, Zhu, Michelle, Wu, Chase Qishi, Rao, Nageswara S., Han, Min, and Wang, Anyi. Tue . "Flexible Bandwidth Scheduling for Streaming Data Movement Over Dedicated Networks". United States. doi:10.1007/978-3-319-75683-7_14. https://www.osti.gov/servlets/purl/1474518.
@article{osti_1474518,
title = {Flexible Bandwidth Scheduling for Streaming Data Movement Over Dedicated Networks},
author = {Zuo, Liudong and Zhu, Michelle and Wu, Chase Qishi and Rao, Nageswara S. and Han, Min and Wang, Anyi},
abstractNote = {A wide range of scientific disciplines are generating large amounts of data at a high speed, which must be transferred to remote sites for real-time processing. Reserving bandwidths over dedicated channels in high-performance networks (HPNs) within a specified time interval has proved to be an effective solution to such high-demanding data transfer. Given a bandwidth reservation request, if the desired bandwidth within the specified time interval cannot be satisfied, most of the existing scheduling algorithms simply reject the request, which would immediately terminate the application. One reasonable approach to mitigate this issue is to provide an alternative bandwidth reservation option to schedule the desired bandwidth within the time interval closest to the specified one. We propose a flexible bandwidth reservation algorithm that considers both the best and alternative bandwidth reservation options for a given request. Extensive simulations are conducted to show the superior performance of the proposed scheduling algorithm compared with a heuristic approach adapted from existing scheduling algorithms.},
doi = {10.1007/978-3-319-75683-7_14},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

Conference:
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