HQTimer: A Hybrid ${Q}$ -Learning-Based Timeout Mechanism in Software-Defined Networks

Li, Qing; Huang, Nanyang; Wang, Dingmin; Li, Xiaowen; Jiang, Yong; Song, Zhendong

doi:10.1109/tnsm.2018.2890754

Cited by 26 publications

(29 citation statements)

References 37 publications

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“…In line with this, Linlian Zhang et al [23] proposed a method called TimeoutX that sets the idle timeout of a flow based on three parameters: the estimated duration of the flow, the type of flow, and the productivity rate of the flow table. Then, in a 2019 study, Qing Li et al [24] developed a mechanism called HQ-Timer which is based on machine learning. This method assigns different timeout values to different flows based on the dynamicity of the traffic.…”

Section: Review Of Literaturementioning

confidence: 99%

An intelligent method for reducing the overhead of analysing big data flows in Openflow switch

et al. 2022

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Software‐defined networks have been developed to allow the entire network to be managed as a programmable entity. As a well‐known protocol in this field, OpenFlow installs new packet forwarding rules of the distinct packets of Big Data flows (known as flow entries) in the flow tables of network switches in order to implement the desired management policies. Despite the high speed, flow tables have limited capacity to store the information of Big Data flows. As a result of inefficient policy for replacing the entries of the flow table, lack of flow entries corresponding to the incoming packets in the flow table of the switch will increase the references to the controller for forwarding this packet as well as the amount of delay in packet forwarding. The underlying idea of the proposed method is to make use of the popularity of traffic flows in the table to select the intended flow for the replacement. For replacement of flow table entries, a novel and intelligent method is proposed in this research which uses a reference history of flows to assign an importance degree to each table entry. Comparison of the simulation results confirms the superiority of the method for reducing the controller's overflow.

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Section: Review Of Literaturementioning

confidence: 99%

An intelligent method for reducing the overhead of analysing big data flows in Openflow switch

et al. 2022

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“…At present, most researches on the flow table overflow of OpenFlow switch focus on how to solve the flow table overflow problem caused by large traffic flows in a normal network environment. For example, set an appropriate timeout value of flow entries to reduce the total number of flow entries in the flow table space [25][26][27]; balance the utilization of flow table space by redirecting flows from switches with high flow table space utilization to switches with sufficient free flow table space [28][29][30]; aggregate flow entries [31][32][33]; find out the most suitable flow entry to delete when the flow table space is in its saturation status [34][35][36].…”

Section: Security and Communication Networkmentioning

confidence: 99%

A Table Overflow LDoS Attack Defending Mechanism in Software-Defined Networks

Xie

Xing

Zhang

et al. 2021

Security and Communication Networks

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In order to achieve requirements such as fast search of flow entries and mask matching, OpenFlow hardware switches usually use TCAM to store flow entries. Limited by the capacity of TCAM, the current commercial OpenFlow switches can only support hundreds of thousands of flow entries, which makes SDN network using OpenFlow hardware switches vulnerable to the threat of flow table overflow attack. Among them, low-rate DoS (LDoS) attack against table overflow poses a serious threat to SDN networks due to its high attack efficiency and concealed flow, and it is also difficult to detect. In this regard, this paper analyzed two types of LDoS attack flow against table overflow and proposed an attack detection and defense mechanism named SAIA (Small-flow Analysis and Inport-flow Analysis) through the design of table overflow prediction and flow entries deletion strategy. Experiments conducted through the SDN network environment showed that SAIA can effectively detect and suppress LDoS attack flows in the flow table in large-scale network conditions and verified that the deployment of SAIA is lightweight. At the same time, SAIA implemented the flow entry deletion strategy based on LRU when the flow table overflows in a nonattack situation, which further enhances the stability of the network.

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“…On this basis, intelligence is used to decide the right flow to be evicted without having to pay for the storage overhead. Liet al [96] noted that due to high storage load, the flowtable is exploited, which in turn affects the performance of the data plane. Toward this goal, they proposed HQTimer, a Q-learning base for the selection of flow effective timeout values to improve the performance of the data plane.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

et al. 2020

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Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

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HQTimer: A Hybrid ${Q}$ -Learning-Based Timeout Mechanism in Software-Defined Networks

Cited by 26 publications

References 37 publications

An intelligent method for reducing the overhead of analysing big data flows in Openflow switch

An intelligent method for reducing the overhead of analysing big data flows in Openflow switch

A Table Overflow LDoS Attack Defending Mechanism in Software-Defined Networks

Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

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