Document Type

Conference Proceeding

Publication Date

2-21-2019

Journal / Book Title

IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS)

Abstract

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for the movement of big data 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 QoS 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 NP-complete and design a heuristic solution. Extensive simulation results show that our scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.

DOI

10.1109/INDIS.2018.00009

Published Citation

Hou, A., Wu, C. Q., Fang, D., Zuo, L., Zhu, M. M., Zhang, X., ... & Yin, X. (2018, November). Bandwidth scheduling for big data transfer with deadline constraint between data centers. In 2018 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS) (pp. 55-63). IEEE.

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