Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80445
Title: Joint scheduling of tasks and network flows in big data clusters
Authors: Yang, L
Liu, XX
Cao, JN 
Wang, ZY
Keywords: Task scheduling
Flow scheduling
Data centers
Software defined networks
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2018, v. 6, p. 66600-66611 How to cite?
Journal: IEEE access 
Abstract: As an increasing number of big data processing platforms like Hadoop, Spark, and Storm appear and normally share the resources in the data center, it has been important and challenging to schedule various jobs from these platforms onto the underlying data center resources such that the overall job completion time is minimized. To solve the problem, the existing work either focus on the task-level scheduling techniques, such as Quincy and delay scheduling, or focus on the network flow scheduling techniques, such as D3 and preemptive distributed quick. These works deal with the scheduling of tasks and network flows separately and cannot achieve optimal performance. The reason is that the task scheduling without regard of the available network bandwidths may generate the task placement that causes serious network congestions and thus leads to long data transmission time. In this paper, we propose the joint scheduling technique by coordinating the task placement and the scheduling of network flows arising from these tasks. We develop a software-defined network (SDN)-based online scheduling framework which selects the task placement based on the available bandwidth on the SDN switches and at meanwhile optimally allocates the bandwidth to each data flow. Comprehensive trace-driven simulations show that the joint scheduling technique can take full use of the network bandwidth and thus reduce the job completion time by 55% on average compared with the benchmark methods.
URI: http://hdl.handle.net/10397/80445
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2878864
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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The following publication Yang, L., Liu, X. X., Cao, J. N., & Wang, Z. Y. (2018). Joint scheduling of tasks and network flows in big data clusters. IEEE Access, 6, 66600-66611 is available at https://dx.doi.org/10.1109/ACCESS.2018.2878864
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