scholarly journals Bandwidth Utilization Prediction in LAN Network Using Time Series Modeling

2019 ◽  
pp. 78-89
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Network Performance ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Bandwidth Utilization ◽  
Research Issues ◽  
Network Traffic Analysis ◽  
Network Bandwidth ◽  
And Performance

monitoring the behavior of computer networks is essential for problem identification and optimal management. Part of this behavior to be monitored is the utilization of the network bandwidth. Several techniques are used to model and forecast network traffic such as time series models, modern data mining techniques, soft computing approaches, and neural networks are used for network traffic analysis and prediction. Efficient bandwidth utilization and optimization are very interesting research issues in effective networks because bandwidth is one of the most required and expensive Internet components needed today. It is generally known that the higher the bandwidth available, the better the network performance, thus an essential aid for network design and bandwidth wastage control and a need for traffic models which can capture the characteristics is necessary. In this paper, a time series prediction models were proposed for LAN office network bandwidth utilization. The proposed prediction models are tested by using evaluation metrics used in time series such as MSE and performance evaluation plot. Testing results show that the proposed models can enhance the detection of bandwidth traffic and provide an efficient tool for bandwidth utilization.

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

Airline Delay Time Series Differentials

2014 ◽  
Vol 1 (2) ◽  
pp. 64-76
Author(s):  
Ronald Wesonga ◽  
Fabian Nabugoomu ◽  
Brian Masimbi
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Model Development ◽  
Differential Analysis ◽  
Management Styles ◽  
Flight Delays ◽  
Daily Data ◽  
Passenger Travel ◽  
Time Series Modelling

Flight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.

scholarly journals A Virtual Network Resource Allocation Framework Based on SR-IOV

2019 ◽  
Vol 9 (1) ◽  
pp. 137
Author(s):  
Zhiyong Ye ◽  
Yuanchang Zhong ◽  
Yingying Wei
Keyword(s):  
Network Performance ◽  
Virtual Machines ◽  
Dynamic Allocation ◽  
Network Resources ◽  
Network Resource ◽  
Throughput Rate ◽  
Network Bandwidth ◽  
On Demand ◽  
Performance Requirements ◽  
And Performance

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

scholarly journals Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment (Preprint)

2018 ◽  
Author(s):  
Amir Talaei-Khoei ◽  
James M Wilson ◽  
Seyed-Farzan Kazemi
Keyword(s):  
Time Series ◽  
Change Point ◽  
Influenza A ◽  
Time Series Prediction ◽  
Viral Meningitis ◽  
Change Point Analysis ◽  
Northern Nevada ◽  
Point Analysis ◽  
Syncytial Virus ◽  
And Performance

BACKGROUND The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. OBJECTIVE This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. RESULTS Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. CONCLUSIONS The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.

scholarly journals Neural Network Based Prefetching Control Mechanism

2019 ◽  
Vol 9 (2) ◽  
pp. 1361-1366
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Control Mechanism ◽  
Web Pages ◽  
Bandwidth Utilization ◽  
Web Page ◽  
Chronological Order ◽  
Network Bandwidth ◽  
Web Access ◽  
Use Of Internet

An important issue incurred by users that limits the use of internet is the long web access delays. Most efficient way to solve this problem is to use “Prefetching”. This paper is an attempt to dynamically monitor the network bandwidth for which a neural network-based model has been worked upon. Prefetching is an effective and efficient technique for reducing users perceived latency. It is a technique that predicts & fetches the web pages in advance corresponding to the clients’ request, that will be accessed in future. Generally, this prediction is based on the historical information that the sever maintains for each web page it serves in a chronological order. This is a speculative technique where if predictions are incorrect then prefetching adds extra traffic to the network, which is seriously negating the network performance. Therefore, there is critical need of a mechanism that could analyze the network bandwidth of the system before prefetching is done. Based on network conditions, this model not only guides if the prefetching should be done or not but also tells number of pages which are to be prefetched in advance so that network bandwidth can be effectively utilized. Proposed control mechanism has been validated using NS-2 simulator and thus various adverse effects of prefetching in terms of response time and bandwidth utilization have been reduced.

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