Fast Coflow Scheduling via Traffic Compression and Stage Pipelining in Datacenter Networks

Qihua Zhou, Kun Wang, Peng Li, Deze Zeng, Song Guo, Baoliu Ye, Minyi Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional scheduling techniques based on improving network resource utilization are subject to limited bandwidth in datacenter networks. To alleviate the shortage of bandwidth, some cluster frameworks employ techniques of traffic compression to reduce transmission consumption. However, they tackle scheduling in a coarse-grained manner at task level and do not perform well in terms of flow-level metrics due to high complexity. Fortunately, the abstraction of coflow pioneers a new perspective to facilitate scheduling efficiency. In this paper, we introduce a coflow compression mechanism to minimize the completion time in data-intensive applications. Due to the NP-hardness, we propose a heuristic algorithm called Fastest-Volume-Disposal-First (FVDF) to solve this problem. For online applicability, FVDF supports stage pipelining to accelerate scheduling and exploits recurrent neural networks (RNNs) to predict compression speed. Meanwhile, we build Swallow, an efficient scheduling system that implements our proposed algorithms. It minimizes coflow completion time (CCT) while guaranteeing resource conservation and starvation freedom. The results of both trace-driven simulations and real experiments show the superiority of our algorithm, over existing one. Specifically, Swallow speeds up CCT and job completion time (JCT) by up to 1.47 × and 1.66 × on average, respectively, over the SEBF in Varys, one of the most efficient coflow scheduling algorithms so far. Moreover, with coflow compression, Swallow reduces data traffic by up to 48.41 percent on average.

Original languageEnglish
Article number8781824
Pages (from-to)1755-1771
Number of pages17
JournalIEEE Transactions on Computers
Issue number12
Publication statusPublished - 1 Dec 2019


  • cluster frameworks
  • Coflow scheduling
  • compression
  • datacenter networks
  • pipelining

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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