Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

Article Computer Science, Artificial Intelligence

Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning

Xu Zhou et al.

Summary: This paper proposes a transfer learning strategy based on graph convolution neural network for large-scale traffic prediction, and designs STA-GCN model with attention mechanism. Experimental results validate its effectiveness.

NEURAL COMPUTING & APPLICATIONS (2022)

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Article Computer Science, Information Systems

Predictive Capacity Planning for Mobile Networks-ML Supported Prediction of Network Performance and User Experience Evolution

Igor Tomic et al.

Summary: Network performance prediction is crucial for agile capacity planning in mobile networks, with the Channel Quality Indicator (CQI) being the key factor. This paper examines different machine learning models and selects XGBoost as the best performing model, while also introducing several features to improve modeling accuracy.

ELECTRONICS (2022)

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Article Economics

M5 accuracy competition: Results, findings, and conclusions

Spyros Makridakis et al.

Summary: This study presents the results and best methods of the M5 Accuracycompetition, aiming to accurately predict hierarchical unit sales for Walmart. The competition required the submission of point forecasts and provides implementation details, major findings, and suggestions for future research.

INTERNATIONAL JOURNAL OF FORECASTING (2022)

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Article Engineering, Multidisciplinary

5G multi-tier radio access network planning based on voronoi diagram

Jun Su et al.

Summary: This paper proposes an improved method of using LTE/Wi-Fi technologies for 5G, which enhances spectral and energy efficiency through optimizing the structure of radio access level (RAL) and implementing dynamic frequency reuse. The method is effective both at low and high network load.

MEASUREMENT (2022)

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Review Computer Science, Artificial Intelligence

Cellular traffic prediction with machine learning: A survey

Weiwei Jiang

Summary: Cellular networks are vital for the success of modern communication systems, and with the advancement of artificial intelligence, they are becoming smarter. This survey reviews the relevant studies on cellular traffic prediction, categorizing the prediction problems and models. It also summarizes various applications based on cellular traffic prediction and points out future research directions.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Review Computer Science, Artificial Intelligence

Graph neural network for traffic forecasting: A survey

Weiwei Jiang et al.

Summary: Traffic forecasting is crucial for intelligent transportation systems. Graph neural networks have been widely used to model spatial and temporal dependencies in traffic forecasting problems, achieving state-of-the-art performance. This survey reviews the research on different graph neural networks in various traffic forecasting problems and provides a comprehensive list of open data and source codes for each problem, as well as identifies future research directions.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Proceedings Paper Computer Science, Hardware & Architecture

LossLeaP: Learning to Predict for Intent-Based Networking

Alan Collet et al.

Summary: Intent-Based Networking requires automatic interpretation and implementation of high-level human-understandable intents. Current forecasting models for network demands do not fulfill the needs of anticipatory Intent-Based Networking. To address this gap, a novel forecasting model called LossLeaP is proposed, which autonomously learns the relationship between prediction and target management objective.

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2022) (2022)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Forecasting for Network Management with Joint Statistical Modelling and Machine Learning

Leonardo Lo Schiavo et al.

Summary: Forecasting is becoming increasingly important for mobile network operations, enabling anticipatory decisions and supporting zerotouch service and network management models. This research presents a hybrid approach that combines statistical modeling and machine learning for predictor design in mobile networks. Experimental results demonstrate that the new model outperforms current state-of-the-art predictors in network resource allocation and mobile traffic anomaly prediction.

2022 IEEE 23RD INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS (WOWMOM 2022) (2022)

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Proceedings Paper Computer Science, Information Systems

A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges

Pengfei Cao et al.

Summary: This paper provides a summary of data, methods, and challenges in traffic prediction. It categorizes the data into three types and explores three significant neural networks used in traffic prediction. The paper also discusses current challenges and development prospects in this field.

CLOUD COMPUTING, CLOUDCOMP 2021 (2022)

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Article Computer Science, Hardware & Architecture

A New Network Traffic Prediction Approach in Software Defined Networks

Yuanqi Yang

Summary: Software Defined Networking (SDN) is a centralized management network architecture, and the proposed network traffic prediction method using STFT and traffic modeling outperforms other methods.

MOBILE NETWORKS & APPLICATIONS (2021)

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Article Multidisciplinary Sciences

Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network

Tao Deng et al.

Summary: This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural network with rectified linear unit. The results show that the model has the smallest prediction error for both the upstream and downstream traffic, achieving better forecasting precision and helping to regulate the load of communication station to reduce resource consumption.

SN APPLIED SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Computer Science, Hardware & Architecture

A lightweight model with spatial-temporal correlation for cellular traffic prediction in Internet of Things

Wei-Che Chien et al.

Summary: This study proposes an input data conversion method based on CNN architecture and optimizes a lightweight neural network model to reduce computational costs while evaluating prediction accuracy. Experimental results show that the model can reduce parameters significantly while maintaining high prediction accuracy.

JOURNAL OF SUPERCOMPUTING (2021)

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Article Computer Science, Information Systems

NSGA-II-Based Granularity-Adaptive Control-Flow Attestation

Jing Zhan et al.

Summary: This paper proposes a granularity-adaptive control-flow attestation for programs in embedded devices based on NSGA-II, balancing security and performance requirements by reducing program complexity and generating strategies. Additionally, runtime protection through SGX is proposed to protect data integrity and confidentiality. Experiments demonstrate the stability of this approach and the significant security/performance benefits over other existing methods in different threat scenarios.

SECURITY AND COMMUNICATION NETWORKS (2021)

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Article Computer Science, Information Systems

Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Sonali Appasaheb Patil et al.

Summary: This paper investigates IoT traffic prediction using machine learning methods and neural network algorithms. The proposed GRU-NN model solves the issue of gradient disappearance, achieving more accurate traffic prediction and outperforming other predictors in terms of statistical performance evaluation parameters.

SECURITY AND COMMUNICATION NETWORKS (2021)

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Article Computer Science, Information Systems

Regression-based fragmentation metric and fragmentation-aware algorithm in spectrally-spatially flexible optical networks

Piotr Lechowicz

Summary: Spectrally-spatially flexible optical networks (SS-FONs) are considered the next frontier in optical backbone networks for supplying high-capacity transmission. Before reaching maturity for commercial applications, crucial issues need to be solved. This study focuses on the fragmentation problem for dynamic traffic in SS-FONs with lane changes and proposes a novel weighted fragmentation metric for reducing network fragmentation.

COMPUTER COMMUNICATIONS (2021)

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Article Computer Science, Hardware & Architecture

Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

Qiong Wu et al.

Summary: This paper proposes a traffic-aware dynamic BS sleep control framework, named DeepBSC, which accurately predicts mobile traffic demand using a data-driven learning approach, solving the issue of BS active/sleep modes selection, and significantly outperforms existing methods.

IEEE-ACM TRANSACTIONS ON NETWORKING (2021)

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Article Computer Science, Information Systems

Spatio-temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities

Laha Ale et al.

Summary: A smart city uses IoT technology to improve operational efficiency and living comfort, with Mobile Edge Computing (MEC) reducing latency and improving service quality for resource-constrained IoT devices. Understanding the spatio-temporal distribution of service demands is crucial for efficiently deploying MEC resources and meeting service requirements.

ACM TRANSACTIONS ON INTERNET TECHNOLOGY (2021)

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Article Computer Science, Hardware & Architecture

A network traffic forecasting method based on SA optimized ARIMA-BP neural network

Hanyu Yang et al.

Summary: This paper proposes a network traffic prediction method based on SA optimized ARIMA-BPNN, which improves the accuracy of network traffic prediction by comprehensively using linear and non-linear models as well as optimization algorithm.

COMPUTER NETWORKS (2021)

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Article Computer Science, Information Systems

Cellular Traffic Prediction Based on an Intelligent Model

Fawaz Waselallah Alsaade et al.

Summary: The development of an intelligent model for predicting mobile network load traffic is crucial for improving network service quality. By combining different single-exponential smoothing and long short-term memory models, an effective prediction method is proposed. Experimental results show that the method performs well in terms of prediction accuracy, closely matching actual observations.

MOBILE INFORMATION SYSTEMS (2021)

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Article Computer Science, Information Systems

Time-Wise Attention Aided Convolutional Neural Network for Data-Driven Cellular Traffic Prediction

Wenxin Shen et al.

Summary: A novel TWACNet structure is proposed to address the limitations of RNN in predicting cellular traffic, combining time-wise attention mechanism and convolutional neural network to effectively capture long-term temporal dependencies and spatial correlations. Experimental results demonstrate that this approach performs significantly better than existing methods in terms of RMSE and training time.

IEEE WIRELESS COMMUNICATIONS LETTERS (2021)

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Proceedings Paper Computer Science, Information Systems

Traffic Prediction by Augmenting Cellular Data with Non-Cellular Attributes

Tejashri Kuber et al.

Summary: The study focuses on predicting user traffic in cellular networks using deep learning techniques, specifically utilizing a long-short-term memory module. By augmenting cellular traffic data with information from neighboring cells and other non-cellular attributes, the study improves prediction accuracy. Two types of analyses are conducted - one-step prediction and trend analysis, with the latter showing gains in accuracy with augmented data.

2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) (2021)

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Proceedings Paper Computer Science, Hardware & Architecture

Accurate Load Prediction Algorithms Assisted with Machine Learning for Network Traffic

Yin Gao et al.

Summary: This paper investigates two load prediction models, one is a linear ensemble model and the other is a convolutional neural network based ResNet model. Both models show good performance in predicting traffic load, improving energy efficiency and calculation efficiency.

IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) (2021)

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Proceedings Paper Engineering, Electrical & Electronic

Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes

Schyler C. Sun et al.

Summary: Wireless traffic prediction is crucial for proactive network optimization in 5G and beyond, enabling better energy efficiency and outage prevention. Designing a feature embedding kernel for Gaussian Process model allows for trade-off between overall forecast accuracy and peak-trough accuracy, providing probabilistic uncertainty quantification for decision-making in optimization modules.

2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING) (2021)

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

Zhongda Tian

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Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks

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A deep learning method based on an attention mechanism for wireless network traffic prediction

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Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

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