Traffic Prediction Techniques Research Articles

Network traffic prediction by learning time series as images

Network traffic prediction is crucial for cost-effective network management, resource allocation, and security in the emergent software-defined and zero-touch networks. Machine Learning (ML) models such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown great promise in traffic prediction. This work proposes a hybrid ML model consisting of pre-trained AlexNet and ResNet models, the 2D Convolutional Neural Network (CNN), Vision Transformer (ViT), and LSTM/GRU models and their variants to predict network traffic. This work explores two approaches for converting the time series data to images by allowing more precise feature extraction and then performing traffic prediction on an image dataset, thus increasing accuracy. Given their proficiency in extracting image features we use three pre-trained models: the AlexNet, the ResNet combined with CNN, and the ViT. Then, we use those models as inputs to the LSTM, Bi-LSTM, and the GRU, Bi-GRU models for predicting network traffic data. The models are evaluated on the GÉANT and Abilene traffic networks and assessed based on the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Comparison of results with existing network traffic prediction techniques shows considerable improvements in all performance metrics revealed by the Wilcoxon test. Our best model results for the GÉANT and Abilene datasets are respectively; MSE of 0.00058 and 0.03078, RMSE of 0.02415 and 0.17544, and MAE of 0.00774 and 0.13384.

Concurrent service auto-scaling for Knative resource quota-based serverless system

Serverless computing platforms currently provide application developers with two ways to control their services’ resource usage cost: resource usage-based and resource quota-based. In usage-based systems, resources are assigned to services based on the amount they consume. Meanwhile, a fixed resource amount is reserved and shared between services for quota-based systems. Several studies have been proposed to enhancing the default serverless auto- scaling algorithms to optimize resource usage and service quality. However, almost all previous works targeted usage-based systems and optimized auto-scaling performance for each separate service. In contrast, this work targets auto-scaling of concurrent services in serverless quota-based systems. In serverless quota-based systems, over-quota resource usage and latency Service Level Objective (SLO) violation may occur when auto-scaling new instances during burst traffic moments. Hence, we aim to find an optimal hybrid auto-scaling decision to minimize over-quota resource usage and latency SLO violation. To solve this problem, We applied deep reinforcement learning and traffic prediction techniques. Our solution is developed and implemented based on the characteristics of the most popular open-source serverless platform Knative. For evaluation, we compared our solution with the default Knative auto-scaler and our previously proposed usage-based hybrid Knative auto-scaler. We configured the default Knative auto-scaler with two different settings that can optimize the total resource usage of all services or minimize service latency. Compared to the optimized resource usage version, our solution traded a slightly higher resource usage for an 11.3% latency SLO violation duration reduction. Meanwhile, compared with the optimized latency version, our solution reduced the over-quota resource usage rate by 18.25% while achieving a similar latency SLO preservation performance. Compared to the usage-based hybrid auto-scaler, the latency SLO violation rate and over-quota resource usage were approximately reduced by half.

Exploring the effects of pandemics on transportation through correlations and deep learning techniques.

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

Deep Learning-Based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.

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

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.

A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo $Q$ -learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo $Q$ -learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

Distance-To-Mean Continuous Conditional Random Fields: Case Study in Traffic Congestion

Traffic prediction techniques are classified as having parametric, non-parametric, and a combination of parametric and non-parametric characteristics. The extreme learning machine (ELM) is a non-parametric technique that is commonly used to enhance traffic prediction problems. In this study, a modified probability approach, continuous conditional random fields (CCRF), is proposed and implemented with the ELM and then utilized to assess highway traffic data. The modification is conducted to improve the performance of non-parametric techniques, in this case, the ELM method. This proposed method is then called the distance-to-mean continuous conditional random fields (DM-CCRF). The experimental results show that the proposed technique suppresses the prediction error of the prediction model compared to the standard CCRF. The comparison between ELM as a baseline regressor, the standard CCRF, and the modified CCRF is displayed. The performance evaluation of the techniques is obtained by analyzing their mean absolute percentage error (MAPE) values. DM-CCRF is able to suppress the prediction model error to ~ 17.047 % , which is twice as good as that of the standard CCRF method. Based on the attributes of the dataset, the DM-CCRF method is better for the prediction of highway traffic than the standard CCRF method and the baseline regressor.

Lightpath Management in SDN-Based Elastic Optical Networks With Power Consumption Considerations

Elastic optical networks (EONs) are increasingly used to interconnect data centers. As interdatacenter traffic is expected to continue to grow at high rates for the foreseeable future, it becomes imperative to address the respective growth in power consumption within EONs. In this paper, we take the first steps toward enhancing the energy efficiency of interdatacenter EONs. Specifically, we use traffic prediction techniques for centralized lightpath management that leverages the capabilities of software defined networking (SDN) technology. Our objective is to eliminate unnecessary lightpath termination and re-establishment operations, and in turn decrease switching power consumption within the network. Our evaluation study confirms that the proposed algorithm is effective in achieving substantial savings in power consumption while maintaining a bandwidth blocking ratio at levels comparable to those of earlier algorithms.

Mining the Situation: Spatiotemporal Traffic Prediction With Big Data

With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions.

Improving Real-Time Route Suggestions in Automotive Navigation Systems using Vehicle Cluster Behavior

Automotive navigation systems are widely used by drivers to help them reach a particular destination. Mostly, such systems are designed to suggest routes which will help the driver to reach destination by covering less distance or consuming less time. This paper presents a technique that will select least congested path by using a vehicular traffic prediction technique that utilizes Global positioning data paired with Speed and Accelerometer Telemetry provided by a GPS unit, mounted on a vehicle, to improve the results of existing graph search methodologies currently being implemented to provide navigation data to users. Clustering of vehicles is done, based on similar positional and directional behavior. These clusters will have similar congestion levels. Traffic data identified through clustering is used to manipulate the path cost of the corresponding road, on an existing road network graph. It tries to improve real-time route suggestions by selecting less congested routes dynamically.

Performance Analysis of Selective Opportunistic Spectrum Access With Traffic Prediction

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In cognitive radio (CR) networks, the ability to capture a frequency slot for transmission in an idle channel has a significant impact on the spectrum efficiency and quality of service (QoS) of a secondary user (SU). The radio frequency (RF) front-ends of an SU have limited bandwidth for spectrum sensing with the target frequency bands dispersed in a discontinuous manner. This results in the SU having to sense multiple target frequency bands in a short period of time before selecting an appropriate idle channel for transmission. This paper addresses this technical challenge by proposing a selective opportunistic spectrum access (SOSA) scheme. With the aid of statistical data and traffic prediction techniques, our SOSA scheme can estimate the probability of a channel appearing idle based on the statistics and choose the best spectrum-sensing order to maximize spectrum efficiency and maintain an SU's connection. By means of doing so, this SOSA scheme can preserve the QoS of an SU while improving the system efficiency. In contrast to previous work, we consider the practical issues encountered by an SU in a wireless environment, such as discontinuous target frequency bands and limited spectrum-sensing ability. We examine the spectrum-sensing scheme in terms of packet loss ratio (PLR) and throughput. The simulation results show that the proposed SOSA scheme can decrease the probability of packet losses in the discontinuous spectrum environment and improve the spectrum efficiency. </para>

Detecting Network Attacks in the Internet via Statistical Network Traffic Normality Prediction

The information technology advances that provide new capabilities to the network users and providers, also provide powerful new tools for network intruders that intend to launch attacks on critical information resources. In this paper we present a novel network attack diagnostic methodology, based on the characterization of the dynamic statistical properties of normal network traffic. The ability to detect network anomalies and attacks as unacceptable when significant deviations from the expected behavior occurs. Specifically, to provide an accurate identification of the normal network traffic behavior, we first develop an anomaly-tolerant nonstationary traffic prediction technique that is capable of removing both single pulse and continuous anomalies. Furthermore, we introduce and design dynamic thresholds, where we define adaptive anomaly violation conditions as a combined function of both magnitude and duration of the traffic deviations. Finally numerical results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach under the presence of different attacks, such as mail-bombing attacks and UDP flooding attacks.