A network application model with operational process feature

Sun, Lina; Huang, Ning; Wang, Lei; Wang, Qingguo; Zhang, Yue

doi:10.1016/j.jfranklin.2019.04.030

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…Leland et al studied characteristics of network traffic and found that it has self‐similarity 51 . Other scholars also confirmed this conclusion 52,53 . The Hurst exponent can measure the self‐similarity of the time series.…”

Section: Related Workmentioning

confidence: 90%

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Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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Summary Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.

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Section: Related Workmentioning

confidence: 90%

“…51 Other scholars also confirmed this conclusion. 52,53 The Hurst exponent can measure the self-similarity of the time series. In addition, the predictability of time series can be evaluated by the Hurst exponent, which is expressed as H. 54 when H is 0.5, time series is random and unpredictable.…”

Section: Hurst Exponent Analysis Of Componentsmentioning

confidence: 99%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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“…Ning Huang et al [1], [28] concentrated on network applications research for a long time and yielded some positive results. A three-layer architecture of network systems is established, including infrastructure layer, control layer and application layer, as shown in Fig.…”

Section: A Conceptmentioning

confidence: 99%

“…N OWADAYS, as NaaS (Network as a service) is widely accepted, many networks, such as transportation networks, 5G networks, IoT networks, cyber physical systems (CPS), financial networks etc. are developing toward application-driven direction [1]. Networks satisfy usersâȂŹ demands by completing various application requests.…”

Section: Introductionmentioning

confidence: 99%

A Service Deployment Method Considering Application Reliability of Networks

et al. 2021

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Service deployment often needs to guarantee QoS of service compositions for networks which compose services to complete users' application requests. Because evaluation methods for QoS of service compositions rely on fixed-process-based formal models which are not suitable for dynamic service composition processes in service deployment, current service deployment methods can only guarantee QoS of single service and are insufficient to guarantee QoS of service compositions. In this paper, a service deployment method considering application reliability is proposed. First, application reliability is introduced to measure QoS of service compositions and an evaluation method based on application flow is presented. Then a service deployment optimization model with the goal of application reliability is established. To solve this model efficiently, an improved genetic algorithm by a new method generating initial population is adopted. Finally, 5G network case study verifies that the improved genetic algorithm converges quickly and our proposed method significantly outperforms the existing method in terms of application reliability by comparison.

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