Centrality Based Congestion Detection Using Reinforcement Learning Approach for Traffic Engineering in Hybrid SDN

Isravel, Deva Priya; Silas, Salaja; Rajsingh, Elijah Blessing

doi:10.1007/s10922-021-09627-3

Cited by 6 publications

(5 citation statements)

References 49 publications

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Section: B Traffic Prediction Based On Edge Cloud Collaboration With ...mentioning

confidence: 99%

“…When h p t+1 > 0, the size of the local data required to be uploaded to the cloud platform by the access node at the next moment may be set as (10). (10) When h p t+1 ≤ 0, the size of local data required to be uploaded to the cloud platform by the access node at the next moment may be set as k p t+1 = 0. In this case, the parameter issuing module may further send alarm information to the access node p and establish a standby link, so as to divert the traffic accessing the node p to other access nodes as much as possible.…”

Section: B Traffic Prediction Based On Edge Cloud Collaboration With ...mentioning

confidence: 99%

“…For resource allocation, traffic prediction can be regarded as a key and important first-hand operation that would be regrettable to be skipped. Highprecise traffic prediction can instruct people to not only make flexible switch adjustments but also form backup paths in burst traffic [10], which may further enable them to break the limitation in integrated radio and optical networks, so as to improve resource utilization and reduce the blocking rate as well as average queue delay.…”

Section: Introductionmentioning

confidence: 99%

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Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edgecloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.INDEX TERMS Integrated radio and optical networks, resource allocation, edge-cloud collaboration, traffic prediction.

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Section: B Traffic Prediction Based On Edge Cloud Collaboration With ...mentioning

confidence: 99%

Section: B Traffic Prediction Based On Edge Cloud Collaboration With ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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“…The second occurs when an OpenFlow request has been created, containing an ID with information stored in the packet header. Thus, packets can collide with another bound interface, causing the blocking of services [9].…”

Section: Potential Attacks In the Sdnmentioning

confidence: 99%

Overhead Reduction Technique for Software-Defined Network Based Intrusion Detection Systems

2022

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In Software-Defined Networks, the Intrusion Detection System is receiving growing attention, due to the expansion of the internet and cloud storage. This system is vital for institutions that use cloud services and have many users. Although the Intrusion Detection System offers several security features, its performance is lagging behind in large enterprise's networks. Existing approaches are based on centralised processing and use many features to implement a protection system. Therefore, system overload and poor performance occur at the controller and OpenFlow switches. As a result, the current solutions create issues that must be considered, especially when they are implemented on large networks. Furthermore, enhancements in security applications improve the reliability of networks. Following a literature review of the existing Intrusion Detection Systems, this paper presents a new model that offers decentralised processing and exchanges data over a trusted, independent channel, in order to solve issues relating to system overload and poor performance. Our model utilises an appropriate feature selection method to reduce the number of extracted features and minimise the data transmitted over the channels. Additionally, the Naive Bayes algorithm has been employed for flow classification purposes, since it is a fast classifier. We successfully implemented our framework, using the Mininet emulator, which provides a suitable networking environment. Evaluations indicates that our proposed system can detect various attacks with an accuracy of 98.46% and nominal decreasing rates of 1.5% in throughput and 0.7% in latency analyses, when the model is implemented in wide range networks.

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“…The switch employs the OpenFlow [13] protocol to gather connection information across the network to regulate the arriving flows. As the growing technology to address the limits of 5G networks [14], the integration of 5G with SDN [15] is being observed. SDN [16] is a new network design that is often regarded as the primary answer to a variety of problems in older networks.…”

Section: Introductionmentioning

confidence: 99%

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

Aqdus¹,

Amin²,

Ramzan³

et al. 2023

Computers, Materials &Amp; Continua

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The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane's forwarding capabilities. So, the SDN must handle traffic from across the whole network. The flow table depends on Ternary Content Addressable Memorable Memory (TCAM) for storing and a quick search of regulations; it is restricted in capacity owing to its elevated cost and energy consumption. Whenever the flow table is abused and overflowing, the usual regulations cannot be executed quickly. In this case, we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don't fit the flow table at a low rate. This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN, using Feed Forward Neural Network (FFNN), K-Means, and Decision Tree (DT). We generate two network topologies, Fat Tree and Simple Tree Topologies, with the Mininet simulator and coupled to the OpenDayLight (ODL) controller. The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query, propagation delay, overall dropped packets, energy consumption, bandwidth usage, latency rate, and throughput. The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network. By putting the proposed flow method and checking whether a packet may move 1414

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Centrality Based Congestion Detection Using Reinforcement Learning Approach for Traffic Engineering in Hybrid SDN

Cited by 6 publications

References 49 publications

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

Overhead Reduction Technique for Software-Defined Network Based Intrusion Detection Systems

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

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