TY - JOUR
T1 - QoS provisioning for various types of deadline-constrained bulk data transfers between data centers
AU - Hou, Aiqin
AU - Wu, Chase Q.
AU - Qiao, Ruimin
AU - Zuo, Liudong
AU - Zhu, Michelle M.
AU - Fang, Dingyi
AU - Nie, Weike
AU - Chen, Feng
N1 - Funding Information:
This research is sponsored by National Natural Science Foundation of China under Grant No. U1609202 , Key Research and Development Plan of Shaanxi Province, China under Grant No. 2018GY-011 , and Xi’an Science and Technology Plan Project under Grant No. GXYD18.2 with Northwest University, China. The authors would also like to acknowledge the anonymous reviewers’ constructive comments. Aiqin Hou received the Ph.D. degree in computer science from Northwest University, Xi’an, China, in 2018. She is currently a faculty member with the School of Information Science and Technology, Northwest University, Xi’an, China. Her research interests include big data, high-performance network, and bandwidth scheduling. Chase Q. Wu completed his Ph.D. dissertation with Oak Ridge National Laboratory, Oak Ridge, TN, USA, and received the Ph.D. degree in computer science from Louisiana State University, Baton Rouge, LA, USA, in 2003. He was a Research Fellow with Oak Ridge National Laboratory during 2003–2006 and an Associate Professor with the University of Memphis, Memphis, TN, USA, during 2006–2015. He is currently a Professor of computer science and the Director of the Center for Big Data, New Jersey Institute of Technology, Newark, NJ, USA. His research interests include big data, parallel and distributed computing, high-performance networking, sensor networks, and cybersecurity. Ruimin Qiao received the B.S. degree in mathematics from Northwest University, Xi’an, China, in 2017. She is currently an M.S. student in the School of Information Science and Technology at Northwest University, Xi’an, China. Her research interests include big data computing and high-performance networking. Liudong Zuo received the Ph.D. degree in computer science from Southern Illinois University Carbondale in 2015. He received the B.E. degree in computer science from University of Electronic Science and Technology of China in 2009. He is currently an assistant professor in Computer Science Department at California State University, Dominguez Hills. His research interests include computer networks, algorithm design, and big data. Michelle M. Zhu received her Ph.D. degree in computer science from Louisiana State University in 2005. She finished her dissertation research in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. She was an associate professor in the Computer Science Department at Southern Illinois University, Carbondale, until 2016. She is currently an associate professor in the Department of Computer Science at Montclair State University. Her research interests include high-performance computing, grid and cloud computing, and big data. Dingyi Fang is currently a Professor with the School of Information Science and Technology, Northwest University, Xi’an, China. His current research interests include mobile computing and distributed computing systems, network and information security, localization, social networks, and wireless sensor networks. Weike Nie received the B.S. degree in electronic engineering, the M.S. degree in electronic and information engineering, and the Ph.D. degree in information and telecommunication engineering from XiDian University, Xi’an, China, in 1997, 2004, and 2009, respectively. Since September 2009, he has been with the Department of Information Science and Technology School, Northwest University, Xi’an, China, where he is currently an Associate Professor. He was a visiting scholar with New Jersey Institute of Technology from February 2017 to February 2018. His current research interests include array signal processing, blind signal processing, and wireless sensor network localization. Feng Chen received the M.S. degree in computer science from Northwest University, Xi’an, China, in 2007, and the Ph.D. degree in computer science from Northwestern Polytechnical University, Xi’an, in 2012. He is currently a faculty member with Northwest University, Xi’an. His research interests are in the area of wireless networks, social networks, and Internet of Things.
Funding Information:
This research is sponsored by National Natural Science Foundation of China under Grant No. U1609202, Key Research and Development Plan of Shaanxi Province, China under Grant No. 2018GY-011, and Xi'an Science and Technology Plan Project under Grant No. GXYD18.2 with Northwest University, China. The authors would also like to acknowledge the anonymous reviewers? constructive comments.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/4
Y1 - 2020/4
N2 - An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.
AB - An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.
KW - Bandwidth scheduling
KW - Big data
KW - Data center
KW - High-performance networks
KW - Software-defined networking
UR - http://www.scopus.com/inward/record.url?scp=85075971010&partnerID=8YFLogxK
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U2 - 10.1016/j.future.2019.11.039
DO - 10.1016/j.future.2019.11.039
M3 - Article
AN - SCOPUS:85075971010
SN - 0167-739X
VL - 105
SP - 162
EP - 174
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -