A novel hybrid prediction algorithm to network traffic

Jiang, Dingde; Xu, Zhengzheng; Xu, Hongwei

doi:10.1007/s12243-015-0465-8

Cited by 36 publications

(14 citation statements)

References 29 publications

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“…Matlab2010 is exploited performed the detailed simulation experiments. PCA [3], WABR [7], and HMPA [2] algorithms for the end-to-end network traffic modeling have been reported as the better performance. In this paper, we compare BTMA with them in detail.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

et al. 2016

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Abstract.Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically distributed normal process. Then the Bases theory is used to characterize the end-to-end network traffic. By calculating the parameters, the model is determined correctly. Simulation results show that our approach is feasible and effective.

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Section: Simulation Results and Analysismentioning

confidence: 99%

“…Step 2: According to statistical theory, use initial value tr x to attain the experience distribution parameters   and   of the end-to-end network traffic in Equations (1)- (2). Accordingly, obtain the experience distribution…”

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confidence: 99%

A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

et al. 2016

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“…43,44 SRE explains the estimation error of each individual OD flow during the time. Estimation errors can be calculated as the relative estimation error of each OD flow individually during the time, or the relative estimation error of all OD flows in each time slot.…”

Section: Performance Measuresmentioning

confidence: 99%

Evaluation of mathematical models to estimate end‐to‐end traffic in a backbone network

Emami

Akbari

Zamani

2018

Int J Communication

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End-to-end network traffic analysis is one of the most important factors in design, development, and traffic engineering of large-scale backbone networks. Despite the importance of end-to-end traffic, it is not applicable to measure it directly using protocols such as NetFlow in large-scale networks due to high computational cost. One technique to tackle this problem is estimating endto-end traffic using easily measured neighboring link loads. Tomography model relates these two parameters with linear equations. In this way, the problem turns into an inverse problem. Since the number of link loads is usually smaller than the number of end-to-end flows in a computer network, traffic matrix estimation is considered to be an ill-posed problem. In this paper, we propose two different methods for estimation of end-to-end traffic matrix by means of link load measurements. One of the methods deploys Kalman filter and the other is developed based on Tikhonov regularization with respect to the existence of temporal autocorrelation among traffics. We also propose a best estimation model based on least squares, which explores the temporal autocorrelation in end-to-end traffics. This model is used as the best estimator and has been compared with other methods. We have evaluated the efficiency of the proposed methods using real datasets from Abilene and GÉANT backbone networks. It is worth noting that the proposed methods in this paper also can be used in any inverse problem with temporal autocorrelation

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“…Proposed by Li et al [19], Artificial Fish-swarm Algorithm (AFA) is a new modern intelligent optimization algorithm [2,3,6,13,34,36,40]. It has some special characteristics different from other classic modern intelligent optimization algorithms, such as genetic algorithm [30] and simulated annealing algorithm [17].…”

Section: Artificial Fish-swarm Algorithm and Virtual Streammentioning

confidence: 99%

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Zhang

Jia

2016

Multimed Tools Appl

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Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

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A novel hybrid prediction algorithm to network traffic

Cited by 36 publications

References 29 publications

A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

Evaluation of mathematical models to estimate end‐to‐end traffic in a backbone network

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

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