SDN-Based Traffic Matrix Estimation in Data Center Networks through Large Size Flow Identification

Guiyan Liu, Songtao Guo, Bin Xiao, Yuanyuan Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDN-enabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-to-destination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

Original languageEnglish
Pages (from-to)675-690
Number of pages16
JournalIEEE Transactions on Cloud Computing
Volume10
Issue number1
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Data center networks
  • machine learning
  • software defined networking
  • traffic matrix estimation
  • traffic measurement

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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