TY - JOUR
T1 - Bandwidth Scheduling for Energy Efficiency in High-Performance Networks
AU - Shu, Tong
AU - Wu, Chase Q.
N1 - Funding Information:
Manuscript received May 20, 2016; revised October 14, 2016 and February 10, 2017; accepted April 6, 2017. Date of publication April 12, 2017; date of current version August 14, 2017. This research is sponsored by U.S. Department of Energy’s Office of Science under Grant No. DE-SC0015892 and National Science Foundation under Grant No. CNS-1560698 with New Jersey Institute of Technology. Some preliminary results of this paper were presented at the IEEE LCN 2013 [28]. The associate editor coordinating the review of this paper and approving it for publication was L. Chen. (Corresponding author: Chase Q. Wu.) The authors are with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: ts372@njit.edu; chase.wu@njit.edu).
Publisher Copyright:
© 1972-2012 IEEE.
PY - 2017/8
Y1 - 2017/8
N2 - The transfer of big data in various applications across high-performance networks (HPNs) in a national or international scope consumes a significant amount of energy on a daily basis. However, most existing bandwidth scheduling algorithms only consider traditional objectives, such as data transfer time minimization, and very limited efforts have been devoted to energy efficiency in HPNs. In this paper, we consider two widely adopted power models, i.e., power-down and speed-scaling, and formulate two instant bandwidth scheduling problems to minimize energy consumption under data transfer deadline and reliability constraints. We prove the NP-completeness of both problems, and design a fully polynomial time approximation scheme for the problem using the power-down model. We also design an approximation algorithm and a heuristic approach that considers the tradeoff between objective optimality and time cost in practice for the problem using the speed-scaling model. The performance superiority of the proposed solutions is illustrated by extensive results based on both simulated and real-life networks in comparison with existing methods.
AB - The transfer of big data in various applications across high-performance networks (HPNs) in a national or international scope consumes a significant amount of energy on a daily basis. However, most existing bandwidth scheduling algorithms only consider traditional objectives, such as data transfer time minimization, and very limited efforts have been devoted to energy efficiency in HPNs. In this paper, we consider two widely adopted power models, i.e., power-down and speed-scaling, and formulate two instant bandwidth scheduling problems to minimize energy consumption under data transfer deadline and reliability constraints. We prove the NP-completeness of both problems, and design a fully polynomial time approximation scheme for the problem using the power-down model. We also design an approximation algorithm and a heuristic approach that considers the tradeoff between objective optimality and time cost in practice for the problem using the speed-scaling model. The performance superiority of the proposed solutions is illustrated by extensive results based on both simulated and real-life networks in comparison with existing methods.
KW - High-performance networks
KW - bandwidth scheduling
KW - energy efficiency
UR - http://www.scopus.com/inward/record.url?scp=85029537724&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029537724&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2017.2693262
DO - 10.1109/TCOMM.2017.2693262
M3 - Article
AN - SCOPUS:85029537724
SN - 0090-6778
VL - 65
SP - 3359
EP - 3373
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 8
M1 - 7896517
ER -