# Scalable Rate Control for Traffic Engineering with Aggregated Flows in   Software Defined Networks

**Authors:** Jian-Jhih Kuo, Chih-Hang Wang, Cheng-Da Tsai, De-Nian Yang, and, Wen-Tsuen Chen

arXiv: 1704.04182 · 2017-08-16

## TL;DR

This paper introduces F²ARM, a scalable SDN traffic engineering architecture that controls a minimal subset of flows to guarantee minimum rates, addressing issues with flow aggregation and TCP rate mismatches.

## Contribution

Proposes a novel architecture and optimization framework for controlling a minimal set of flows in SDN to improve scalability while maintaining rate guarantees.

## Key findings

- JFSRD algorithm performs near-optimally in small networks.
- Controlled flows can be reduced by 50% in real network simulations.
- Addresses flow rate guarantee issues in aggregated SDN flows.

## Abstract

To increase the scalability of Software Defined Networks (SDNs), flow aggregation schemes have been proposed to merge multiple mouse flows into an elephant aggregated flow for traffic engineering. In this paper, we first notice that the user bit-rate requirements of mouse flows are no longer guaranteed in the aggregated flow since the flow rate decided by the TCP allocation is usually different from the desired bit-rate of each user. To address the above issue, we present a novel architecture, named Flexible Flow And Rate Management (F$^2$ARM), to control the rates of only a few flows in order to increase the scalability of SDN, while leaving the uncontrolled flows managed by TCP. We formulate a new optimization problem, named Scalable Per-Flow Rate Control for SDN (SPFRCS), which aims to find a minimum subset of flows as controlled flows but ensure that the flow rates of all uncontrolled flows can still satisfy the minimum required rates by TCP. We prove that SPFRCS is NP-hard and design an efficient algorithm, named Joint Flow Selection and Rate Determination (JFSRD). Simulation results based on real networks manifest that JFSRD performs nearly optimally in small-scale networks, and the number of controlled flows can be effectively reduced by 50% in real networks.

## Full text

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## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/1704.04182/full.md

## References

21 references — full list in the complete paper: https://tomesphere.com/paper/1704.04182/full.md

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Source: https://tomesphere.com/paper/1704.04182