D-SRTF : Distributed Shortest Remaining Time First Scheduling for Data Center Networks
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 562-575 |
Journal / Publication | IEEE Transactions on Cloud Computing |
Volume | 9 |
Issue number | 2 |
Online published | 2 Nov 2018 |
Publication status | Published - Apr 2021 |
Link(s)
Abstract
Many recent works utilize scheduling to minimize the Flow Completion Time (FCT) in Data Center Networks (DCN), like PIAS using Shortest Job First (SJF) scheduling and pFabric using Shortest Remaining Size First (SRSF) scheduling. However, they only consider the flow size information, without consideration of available bandwidth of the network, leading to inferior performance when the network is congested. Besides, information on flow size is hard to obtain in practice. Moreover, although a centralized scheduler may have optimal scheduling decisions, it suffers from high system overhead. Therefore, a new DCN scheme is expected which is deployment-friendly and implements SRTF scheduling in a distributed manner. In this paper, we propose D-SRTF, a light-weight yet effective DCN scheme to implement SRTF scheduling. D-SRTF determines the remaining time of each flow according to the estimated remaining flow size and the available bandwidth, in order to determine the priority of each flow. Switches perform Strict Priority (SP) scheduling according to the priority of each flow, in order to realize SRTF scheduling. Experiments show that D-SRTF performs better than the currently best implementable scheme, PIAS, and could perform better than pFabric if information on flow size is available.
Research Area(s)
- Bandwidth, Cloud computing, Data center networks, Data centers, MIMICs, network protocols, Optimal scheduling, Processor scheduling, Scheduling, scheduling
Citation Format(s)
D-SRTF: Distributed Shortest Remaining Time First Scheduling for Data Center Networks. / Gao, Chengxi; Lee, Victor C. S.; Li, Keqin.
In: IEEE Transactions on Cloud Computing, Vol. 9, No. 2, 04.2021, p. 562-575.
In: IEEE Transactions on Cloud Computing, Vol. 9, No. 2, 04.2021, p. 562-575.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review