OSTB: Optimizing Fairness and Efficiency for Coflow Scheduling without Prior Knowledge

Qu, Handong; Xu, Renhai; Li, Wenxin; Qu, Wenyu; Zhou, Xiaobo

doi:10.1109/hpcc/smartcity/dss.2019.00218

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…However, due to its ease of implementation, the notion of fairness by static coflow progress guarantees has been preferred in the literature. Other authors [26] define the fairness degree of a data-transfer scheme with respect to standard schemes such as Hug [20] or DFR [22].…”

Section: Related Workmentioning

confidence: 99%

“…More recently, a relative fairness concept has been introduced in order to compare coflow schedulers to benchmarks such as Hug or DFR [26]. In particular, [10] has proposed a long term isolation guarantee by providing a bound on CCTs relative to DRF performance.…”

Section: Related Workmentioning

confidence: 99%

“…The above LP is obtained by considering any rate allocation for which the constraints of PS are respected: (26) has replaced the constraints ( 6) and ( 7) in PS. Since LP is in P this completes the proof of the first part of the statement.…”

Section: Appendix Proof Of Thmmentioning

confidence: 99%

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Fair Coflow Scheduling via Controlled Slowdown

Pellegrini¹,

Gupta²,

Azouzi³

et al. 2022

Preprint

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The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control.This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the σ-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%