A Joint Balancing Flow Table and Reducing Delay Scheme for Mice-Flows in Data Center Networks

Fu, Qiongxiao; Sun, Enchang; Wang, Qianwen; Wang, Zhuwei; Zhang, Yanhua

doi:10.1109/globecom38437.2019.9014100

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…However, this study provides no investigation in terms of FCT of mice flows. Fu et al [14] proposes a balancing flow table scheme to mitigate FCT of mice flows by computing the best path for each new mice flow, which incurs overhead since the number of mice flows is big in DCN. Therefore, Sieve is a new distributed solution, but it does not involve endhosts contacting.…”

Section: Related Workmentioning

confidence: 99%

Class-based Flow Scheduling Framework in SDN-based Data Center Networks

Zaher¹,

Alawadi²,

Molnár³

2020

Preprint

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The emerging technologies leveraging Data Center Networks (DCN) and their consequent trafﬁc patterns impose more necessity for improving Quality of Service (QoS). In this paper, we propose Sieve, a new distributed SDN framework that efﬁciently schedules ﬂows based on the available bandwidth to improve Flow Completion Time (FCT) of mice ﬂows. In addition, we propose a lightweight sampling mechanism to sample a portion of ﬂows. In particular, Sieve schedules the sampled ﬂows, and it reschedules only elephant ﬂows upon threshold hits. Furthermore, our framework allocates a portion of the ﬂows to ECMP, so that the associated overhead can be mitigated in the control plane and ECMP-related packet collisions are fewer as well. Mininet has been used to evaluate the proposed solution, and Sieve provides better FCT up to 50% in comparison to the existing solutions like ECMP and Hedera.

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Section: Related Workmentioning

confidence: 99%