Evaluating the impact of SDN-induced frequent route changes on TCP flows

Carpa, Radu; Assunção, Marcos Dias de; Glück, Olivier; Lefèvre, Laurent; Mignot, Jean-Christophe

doi:10.23919/cnsm.2017.8256021

Cited by 15 publications

(6 citation statements)

References 18 publications

(22 reference statements)

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“…Although the use of low values of the sampling period (e.g., 0.01 s) exhibits low delays and packet losses (with the subsequent increase in energy consumption), values lower than 0.5 s are hardly implementable in practice, since they result in a huge (and unmanageable) overhead of control traffic. Moreover, for low sampling periods, this frequent rerouting can harm the performance of TCP, as studied in [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay

Fondo‐Ferreiro

Rodríguez-Pérez

Fernández‐Veiga

et al. 2018

Sensors

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Both economic and environmental costs are driving much research in the area of the energy efficiency of networking equipment. This research has produced a great amount of proposals. However, the majority of them remain unimplemented due to the lack of flexibility of current hardware devices and a certain lack of enthusiasm from commercial vendors. At the same time, Software-Defined Networking (SDN) has allowed customers to control switching decisions with a flexibility and precision previously unheard of. This paper explores the potential convergence between the two aforementioned trends and presents a promising power saving algorithm that can be implemented using standard SDN capabilities of current switches, reducing operation costs on both data centers and wired access networks. In particular, we focus on minimizing the energy consumption in bundles of energy-efficient Ethernet links leveraging SDN. For this, we build on an existing theoretical algorithm and adapt it for implementing with an SDN solution. We study several approaches and compare the resulting algorithms not only according to their energy efficiency, but also taking into account additional QoS metrics. The results show that the resulting algorithm is able to closely match the theoretical results, even when taking into account the requirements of delay-sensitive traffic.

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Section: Resultsmentioning

confidence: 99%

Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay

Fondo‐Ferreiro

Rodríguez-Pérez

Fernández‐Veiga

et al. 2018

Sensors

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show abstract

“…This predicament manifests as a considerable volume of data flows being rerouted to accommodate fluctuations in traffic demands, subsequently resulting in pronounced disturbances across the network (e.g., service disruption). The impact of the network disturbance problem has been examined in a study conducted by [3]. The findings from this study indicate that the substantial redirection of flow traffic can lead to a substantial decline of up to 50% of total network throughput.…”

Section: The High Network Disturbance Problemmentioning

confidence: 99%

“…However, these approaches often suffer from two main problems. Firstly, there is a problem of high network disturbance or a large number of rerouting flows, leading to a degradation in the overall network's Quality of Service (QoS) [3]. Most of the proposed solutions only address the routing problem in a single snapshot (which is called a "time-step" in this paper) or use a short-term traffic prediction to calculate the routing rules without considering the long time horizon [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

Le,

Ji,

Tran

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Traffic engineering (TE) is one of the most critical issues in networking, as it enables efficient and reliable network operations. With the advent of Machine Learning (ML) techniques, many ML-based TE methods have emerged in recent years, especially those employing Deep Neural Networks for future traffic prediction to enhance the performance of traditional approaches. However, current methods suffer from two major issues. Firstly, most prior works only solve the TE problem based on short-term traffic prediction, neglecting the network traffic dynamics over an extended time period. This oversight results in high network disturbance when numerous traffic flows need to be rerouted to adapt to traffic changes. Secondly, although traffic prediction models rely on historical traffic data to perform future prediction, ML-based TE studies often ignore the high overhead for network traffic monitoring.To address these issues, we propose a traffic prediction-based routing algorithm in which the routing rules can be applied to multiple time-steps without requiring changes, ultimately leading to reduced network disturbance. We employ the segment routing (SR) technique as the routing algorithm and formulate the multi-time-step segment routing method that incorporates future traffic prediction. To address the high monitoring overhead, we present an approach that combines partial traffic prediction and compressive sensing techniques to estimate unmeasured data. Through extensive experiments on real backbone network traffic datasets, we demonstrate that our proposal can achieve more than 80% of the optimal performance in reducing maximum link utilization while significantly reducing the number of routing changes and traffic monitoring cost.

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“…In RDCNs, the effect of packet re-ordering on TCP throughput has also been analyzed and known for a very long period of time [44]. Recently, Cârpa et al have built on top of this work and analyzed the effect of switch reconfigurations on TCP performance [45]. This work considers multiple flows competing on the same bottleneck.…”

Section: Related Workmentioning

confidence: 99%

When TCP Meets Reconfigurations: A Comprehensive Measurement Study

Aykurt,

Zerwas,

Blenk

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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The diversity of deployed applications in data centers leads to a complex traffic mix in the network. Reconfigurable Data Center Networks (RDCNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations, transport layer protocols, and congestion control (CC) algorithms. This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?This paper focuses on TCP and presents a measurement study of TCP performance in RDCNs. In particular, it evaluates diverse traffic mixes combining TCP variants, UDP, and QUIC transport protocols. The quantitative analysis of the measurements shows that migrated TCP flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.

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Evaluating the impact of SDN-induced frequent route changes on TCP flows

Cited by 15 publications

References 18 publications

Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay

Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

When TCP Meets Reconfigurations: A Comprehensive Measurement Study

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