Estimating SDN traffic matrix based on online adaptive information gain maximization method

Li, Dongyang; Xing, Changyou; Dai, Ningyun; Dai, Fei; Zhang, Guomin

doi:10.1007/s12083-018-0646-0

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…The most informative flow from traffic is determined for estimation of TMs. This approach increases the accuracy with a small increase in measurement resources [27]. For a large network, estimating TM takes high computation time for which division of network is one of the solutions.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

Pachuau

Krishna

Saha

2021

SCPE

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Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.

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Section: Related Workmentioning

confidence: 99%

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

Pachuau

Krishna

Saha

2021

SCPE

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“…In their work the aggregated rules are disaggregated to improve the estimation accuracy. Li et al [29] developed a method to determine which flows are most informative to construct a measurement flow set iteratively until an accuracy requirement is satisfied or a measurement resource constraint is reached. FlowMon [30] provided a sample and fetch based mechanism to detect large flows.…”

Section: Related Workmentioning

confidence: 99%

“…This function searches the set L to provide these data. Using them, the elements of LP can be generated (lines [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. For each value returned from the getPorts() function, an entry in LP is added.…”

Section: ) Generate Lp Set Algorithmmentioning

confidence: 99%

“…A common problem faced by previous TM estimation methods is related to aggregating the collected traffic data statistics [4,28,29,37]. For real-time measurement, depending on the amount of collected data, calculating aggregated link, switch, and route traffic throughput may be time-consuming, making methods unable to meet real-time constraints.…”

Section: Introductionmentioning

confidence: 99%

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A MapReduce Approach for Traffic Matrix Estimation in SDN

2020

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A traffic matrix (TM) is a source of critical traffic throughput information for traffic engineering activities and network management tasks such as traffic prediction, capacity planning, network provisioning, and anomaly detection. However, estimating TM poses several challenges for network engineers. One of the challenges is that traffic data statistics are constantly changing, and their aggregation for real-time monitoring becomes a difficult task. This paper presents a near real-time TM estimation approach for OpenFlow (OF) networks. It makes use of Big Data techniques based on MapReduce operations to tackle the aggregation problem. The proposed method uses traffic data statistics collected from OF switches through an SDN controller as input and aggregates these data in a Big Data streaming processing environment. This paper explores the benefits of the distributed MapReduce computing model to provide an estimate of the TM for all origin-destination (OD) pairs of hosts in the network in two ways: 1) the accumulated throughput and 2) the throughput between two sequential TM estimates. This procedure enables network engineers to monitor the behavior and evolution of the throughput on each OD pair in the network and on each link in the path between each OD pair. The generated TM is persisted in a NoSQL database and can be made available for a variety of network traffic monitoring applications. The results of the simulations show the potential of the proposed MapReduce approach for TM estimation.

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“…There is a lot of research on about the TM in SDN . Choudhury et al proposed the TM prediction in SDN‐enabled internet protocol (IP)/optical networks with machine learning method; Liu et al studied an adaptive flow measurement and inference with online learning in SDN based on the TM; Li et al proposed the method for estimating SDN TM based on online adaptive information gain maximization. Tang et al proposed a deep learning‐based traffic load (TL) prediction algorithm to forecast future TL and congestion in network.…”

Section: Introductionmentioning

confidence: 99%

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

Huo

Jiang

et al. 2019

Computational Intelligence

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Internet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine‐grained and accurate traffic information is important for IoT network management. Software‐defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine‐grained traffic information in SDN‐based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse‐grained traffic of flows and fine‐grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non‐deterministic polynomial‐hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine‐grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.

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Estimating SDN traffic matrix based on online adaptive information gain maximization method

Cited by 13 publications

References 16 publications

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

A MapReduce Approach for Traffic Matrix Estimation in SDN

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

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