GlobeSnap: An Efficient Globally Consistent Statistics Collection for Software-Defined Networks

Sandhya Rathee, Nitin Varyani, K. Haribabu, Aakash Bajaj, Ashutosh Bhatia, Ram Jashnani, Zhi Li Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Software defined networking (SDN) controller requires crucial statistics like flow-wise statistics from the switches to make decisions related to routing, load balancing, and QoS provisioning. These statistics, when viewed across the switches are likely to be inconsistent if a specific order is not enforced while collecting statistics. Collecting consistent statistics requires coordination among all the participating switches. A few approaches in the literature collect globally consistent statistics of a network in the SDN domain. However, these approaches are not time-efficient, robust, and synchronous for OpenFlow based networks. We propose, GlobeSnap, a time-efficient, robust, and synchronous method to collect globally consistent statistics for OpenFlow networks. GlobeSnap collects consistent statistics for all flows in a single round and is therefore, time-efficient. Moreover, GlobeSnap is robust since it resumes the statistics collection process from where it left in case of interruption. GlobeSnap also provides a near-synchronous snapshot of statistics of the switches traversed by a given flow. We also propose a mechanism to persistently store states in OpenFlow based networks using registers, multiple flow tables, and multiple pipelines. We find that GlobeSnap outperforms the state-of-the-art approaches in consistency evaluation. Further we present two use-cases which are sensitive to inconsistent flow statistics, that is, computing packet loss and identifying bottleneck links, to show the time-efficiency, robustness, and synchronicity of GlobeSnap. GlobeSnap provides 100% consistency in OpenFlow based SDN networks. Whereas the existing methods achieve a maximum of 59.89% consistency.

Original languageEnglish (US)
Article number35
JournalJournal of Network and Systems Management
Volume29
Issue number3
DOIs
StatePublished - Jul 2021

Bibliographical note

Funding Information:
This research was supported in part by NSF under grants CNS-1618339, CNS-1617729, CNS-1814322, CNS-1831140, CNS-1836772, and CNS-1901103.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Consistent statistics
  • OpenFlow
  • Special control packet

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