Abstract
88Sheer volumes of data, now frequently termed as “big data,” are being generated from various emerging applications of large-scale simulations, scientific experiments, and global-scale communications. Such extremely large amounts of data are normally generated at one data center and then need to be transferred to distributed data centers for data storage and analysis, within which fast, predictable, and reliable data transfer with guaranteed performance has become crucial to ensure success. Fortunately, reserving bandwidth as needed along selected paths in high-performance networks (HPNs) has proved to be an effective way to satisfy the requirements of such high-demanding data transfer. In this chapter, we first present the introduction and background of bandwidth reservation service in HPNs for big data transfer along with the challenges. The related works, and concepts and mechanisms of bandwidth reservation strategies are provided in Section 5.2 and Section 5.3, respectively. We show our algorithm’s design and illustration through simple examples for easy comprehension in Section 5.4, and conclude our work in Section 5.5.
| Original language | English |
|---|---|
| Title of host publication | Big Data and Computational Intelligence in Networking |
| Publisher | CRC Press |
| Pages | 87-106 |
| Number of pages | 20 |
| ISBN (Electronic) | 9781498784870 |
| ISBN (Print) | 9781498784863 |
| DOIs | |
| State | Published - 1 Jan 2017 |
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Efficient big data transfer using bandwidth reservation service in high-performance networks. / Zuo, Liudong; Zhu, Michelle.
Big Data and Computational Intelligence in Networking. CRC Press, 2017. p. 87-106.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Efficient big data transfer using bandwidth reservation service in high-performance networks
AU - Zuo, Liudong
AU - Zhu, Michelle
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 88Sheer volumes of data, now frequently termed as “big data,” are being generated from various emerging applications of large-scale simulations, scientific experiments, and global-scale communications. Such extremely large amounts of data are normally generated at one data center and then need to be transferred to distributed data centers for data storage and analysis, within which fast, predictable, and reliable data transfer with guaranteed performance has become crucial to ensure success. Fortunately, reserving bandwidth as needed along selected paths in high-performance networks (HPNs) has proved to be an effective way to satisfy the requirements of such high-demanding data transfer. In this chapter, we first present the introduction and background of bandwidth reservation service in HPNs for big data transfer along with the challenges. The related works, and concepts and mechanisms of bandwidth reservation strategies are provided in Section 5.2 and Section 5.3, respectively. We show our algorithm’s design and illustration through simple examples for easy comprehension in Section 5.4, and conclude our work in Section 5.5.
AB - 88Sheer volumes of data, now frequently termed as “big data,” are being generated from various emerging applications of large-scale simulations, scientific experiments, and global-scale communications. Such extremely large amounts of data are normally generated at one data center and then need to be transferred to distributed data centers for data storage and analysis, within which fast, predictable, and reliable data transfer with guaranteed performance has become crucial to ensure success. Fortunately, reserving bandwidth as needed along selected paths in high-performance networks (HPNs) has proved to be an effective way to satisfy the requirements of such high-demanding data transfer. In this chapter, we first present the introduction and background of bandwidth reservation service in HPNs for big data transfer along with the challenges. The related works, and concepts and mechanisms of bandwidth reservation strategies are provided in Section 5.2 and Section 5.3, respectively. We show our algorithm’s design and illustration through simple examples for easy comprehension in Section 5.4, and conclude our work in Section 5.5.
UR - http://www.scopus.com/inward/record.url?scp=85052703328&partnerID=8YFLogxK
U2 - 10.1201/b21278
DO - 10.1201/b21278
M3 - Chapter
AN - SCOPUS:85052703328
SN - 9781498784863
SP - 87
EP - 106
BT - Big Data and Computational Intelligence in Networking
PB - CRC Press
ER -