Intelligent traffic control for QoS optimization in hybrid SDNs

Huang, Xiaotao; Zeng, Man; Xie, Kun

doi:10.1016/j.comnet.2021.107877

Cited by 16 publications

(5 citation statements)

References 18 publications

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“…A flexible method was put forth in Reference 29 for controlling network traffic and enhancing speed between network operators in SDN. How to find an efficient and appropriate routing policy in a combined SDN is essential for promoting SDN development.…”

Section: Related Workmentioning

confidence: 99%

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

Rezaei

Ghaffari

2023

Concurrency and Computation

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Summary Recently, with the rapid development of the Internet of Things (IoT) and its real‐time applications, the network is required to guarantee the differential quality of service (QoS) requirements of the various data flows (telemedicine, video, and live streaming) of various IoT services. Software‐defined network (SDN) as emerging network technology separates the control logic from data planes of networks and is a promising technique to guarantee the QoS requirements of different services. In SDN, the controller has global visibility of the whole network devices such as switches and routers. Hence, controllers can dynamically optimize QoS requirements and resources of the network. On the other hand, providing a QoS‐aware routing scheme is an important challenge. Hence, in this paper by capitalizing on the fuzzy analytic hierarchy process (AHP), a new QoS‐aware routing method is proposed in SDNs. The proposed scheme considers the combination of different QoS criteria such as bandwidth, route length, reliability, and bit error rate for selecting an appropriate route between sender and receiver nodes. The simulation results indicated that the proposed method improves throughput and end‐to‐end delay. Comparison with other routing algorithms shows that the proposed method performs better load balancing under different network topologies.

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Section: Related Workmentioning

confidence: 99%

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

Rezaei

Ghaffari

2023

Concurrency and Computation

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“…Figure 8 indicates that the transaction process between transaction roles contains nine steps: (1) account registration; (2) transaction roles trade in the blockchain network; (3) transaction storage; (4) block searching; (5) searching of POF of transaction information by miner nodes; (6) whole network broadcast; (7) identification of the block-effectiveness by nodes; (8) blockchain extension; and (9) trade between retailers and customers through the network platform [ 16 , 36 ].…”

Section: Bt and Rm Technologymentioning

confidence: 99%

“…(3) transaction storage; (4) block searching; (5) searching of POF of transaction information by miner nodes; (6) whole network broadcast; (7) identification of the block-effectiveness by nodes; (8) blockchain extension; and (9) trade between retailers and customers through the network platform [16,36].…”

Section: Design Of a Trusted Traceability Survey On The Blockchainmentioning

confidence: 99%

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Jiang

Lei

2022

Computational Intelligence and Neuroscience

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To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), T e t r i s ∗ , Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

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“…Results show better performance is achieved as compared to different DDPG variants. Another DDPG based DRL routing optimization is presented in Reference 16 for a hybrid SDN network. The hybrid SDN network combines traditional legacy devices and SDN devices.…”

Section: Related Workmentioning

confidence: 99%

Enhanced routing using recurrent neural networks in software defined‐data center network

Modi

Swain

2022

Concurrency and Computation

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Software Defined-DCN (SD-DCN) is a layered topology with logically centralized control which provides intelligence thro-ugh programmability. The controller in SD-DCN provides different network management skills by decoupling of control plane and data plane. However, the controller applies traditional artificial intelligence approaches and neural network approaches for routing strategies in recent systems. To manage dynamic changes in the network, deep learning approaches provide deep analysis and prediction to enhance the existing network performance. In convolutional neural network (CNN) deep learning model, the learning process executes based on present traffic data and forgets the inputs for the future learning phase. To overcome CNN limitation, in this article, different recurrent neural network (RNN) deep learning models are used to improve the routing computation for SD-DCN topology. The long short-term memory (LSTM)-RNN and bi-directional long short-term memory (BiLSTM)-RNN deep learning models provide forget gate to analyze periodic network traffic datasets and improve routing by delivering dynamic and intelligent routing paths in the network. The proposed system is implemented in a simulation scenario, and the proposed work improves the network performance in hot-spot traffic as compared to the existing mechanisms. Moreover, error analysis of different deep learning models is presented in the evaluation results.

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Intelligent traffic control for QoS optimization in hybrid SDNs

Cited by 16 publications

References 18 publications

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Enhanced routing using recurrent neural networks in software defined‐data center network

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