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Network Traffic: Models, Challenges and Research Opportunities

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 8284

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Dr. Habib Mostafaei

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Guest Editor

Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: networked systems; network measurements
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Savi

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Guest Editor

Department of Informatics, Systems and Communication, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1 - 20126 Milano, Italy
Interests: SDN; NFV; programmable data planes; network monitoring; edge computing
Special Issues, Collections and Topics in MDPI journals

Dr. Sebastian Troia

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Guest Editor

Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
Interests: Software-Defined Wide Area Networking (SD-WAN); Software-Defined Networking (SDN); machine-learning for networking; Network Function Virtualization (NFV); 5G and Centralized Radio Access Networks (5G-RAN)

Special Issue Information

Dear Colleagues,

Internet service providers today offer a wide spectrum of applications and services, and with the rise of technologies such as 5G, the number of connected devices grows exponentially. Furthermore, applying emerging paradigms such as softwarization and virtualization to the network infrastructure has led to exciting changes in the way such networked systems are built. Insight from traffic flows is the key to unlocking network operators’ capacity to manage and secure networks since it provides complete visibility of the process and events occuring within the network. These insights further allow network operators to devise a set of tools to run their network to meet the stringent Quality of Service (QoS) or Quality of Experience (QoE) requirements.

This Special Issue aims to focus on the models, challenges and research opportunities for network traffic modeling, management and optimization. Potential topics include, but are not limited to, the following:

Modeling of network traffic;
Challenges in network traffic characterization;
Traffic measurement, analysis, and classification;
Traffic Measurements in Software-Defined Networks (SDN) and Network Function Virtualization (NFV);
Data plane programming (e.g. P4, eBPF, XDP) for network traffic analysis;
Applications of Artificial Intelligence (AI) in analyzing network traffic;
Network traffic anomaly detection and mitigation;
Network traffic analytics in edge, cloud and fog computing.

Dr. Habib Mostafaei
Dr. Marco Savi
Dr. Sebastian Troia
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

27 pages, 14709 KiB

Open AccessArticle

Low Latency TOE with Double-Queue Structure for 10Gbps Ethernet on FPGA

by Dan Yang, Xuhan Xu, Tianyang Chen, Yanhao Chen and Junjie Zhang

Sensors 2023, 23(10), 4690; https://doi.org/10.3390/s23104690 - 12 May 2023

Cited by 1 | Viewed by 1265

Abstract

The TCP protocol is a connection-oriented and reliable transport layer communication protocol which is widely used in network communication. With the rapid development and popular application of data center networks, high-throughput, low-latency, and multi-session network data processing has become an immediate need for network devices. If only a traditional software protocol stack is used for processing, it will occupy a large amount of CPU resources and affect network performance. To address the above issues, this paper proposes a double-queue storage structure for a 10G TCP/IP hardware offload engine based on FPGA. Furthermore, a TOE reception transmission delay theoretical analysis model for interaction with the application layer is proposed, so that the TOE can dynamically select the transmission channel based on the interaction results. After board-level verification, the TOE supports 1024 TCP sessions with a reception rate of 9.5 Gbps and a minimum transmission latency of 600 ns. When the TCP packet payload length is 1024 bytes, the latency performance of TOE’s double-queue storage structure improves by at least 55.3% compared to other hardware implementation approaches. When compared with software implementation approaches, the latency performance of TOE is only 3.2% of the software approaches. Full article

(This article belongs to the Special Issue Network Traffic: Models, Challenges and Research Opportunities)

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15 pages, 27438 KiB

Open AccessArticle

Optimization of BBR Congestion Control Algorithm Based on Pacing Gain Model

by Shuang Yang, Yuquan Tang, Wansu Pan, Huadong Wang, Dandan Rong and Zhirong Zhang

Sensors 2023, 23(9), 4431; https://doi.org/10.3390/s23094431 - 30 Apr 2023

Viewed by 3672

Abstract

In 2016, Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR). The BBR congestion control algorithm measures the network bottleneck bandwidth and minimum delay in real-time to calculate the bandwidth delay product (BDP) and then adjusts the transmission rate to maximize throughput and minimize latency. However, relevant research reveals that BBR still has issues such as RTT unfairness, high packet loss rate, and deep buffer performance degradation. This article focuses on its most prominent RTT fairness issue as a starting point for optimization research. Using fluid models to describe the data transmission process in BBR congestion control, a fairness optimization strategy based on pacing gain is proposed. Triangular functions, inverse proportional functions, and gamma correction functions are analyzed and selected to construct the pacing gain model, forming three different adjustment functions for adaptive adjustment of the transmission rate. Simulation and real experiments show that the three optimization algorithms significantly improve the fairness and network transmission performance of the original BBR algorithm. In particular, the optimization algorithm that employs the gamma correction function as the gain model exhibits the best stability. Full article

(This article belongs to the Special Issue Network Traffic: Models, Challenges and Research Opportunities)

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13 pages, 443 KiB

Open AccessArticle

Video Stream Recognition Using Bitstream Shape for Mobile Network QoE

by Darius Chmieliauskas and Šarūnas Paulikas

Sensors 2023, 23(5), 2548; https://doi.org/10.3390/s23052548 - 24 Feb 2023

Cited by 1 | Viewed by 1151

Abstract

Video streaming service delivery is a challenging task for mobile network operators. Knowing which services clients are using could help ensure a specific quality of service and manage the users’ experience. Additionally, mobile network operators could apply throttle, traffic prioritization, or differentiated pricing. However, due to the growth of encrypted Internet traffic, it has become difficult for network operators to recognize the type of service used by their clients. In this article, we propose and evaluate a method for recognizing video streams solely based on the shape of the bitstream on a cellular network communication channel. To classify bitstreams, we used a convolutional neural network that was trained on a dataset of download and upload bitstreams collected by the authors. We demonstrate that our proposed method achieves an accuracy of over 90% in recognizing video streams from real-world mobile network traffic data. Full article

(This article belongs to the Special Issue Network Traffic: Models, Challenges and Research Opportunities)

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19 pages, 3027 KiB

Open AccessArticle

Knowledge Extraction and Discovery about Web System Based on the Benchmark Application of Online Stock Trading System

by Marcin Borowiec, Rafał Piszko and Tomasz Rak

Sensors 2023, 23(4), 2274; https://doi.org/10.3390/s23042274 - 17 Feb 2023

Cited by 1 | Viewed by 1411

Abstract

Predicting workload characteristics could help web systems achieve elastic scaling and reliability by optimizing servers’ configuration and ensuring Quality of Service, such as increasing or decreasing used resources. However, a successful analysis using a simulation model and recognition and prediction of the behavior of the client presents a challenging task. Furthermore, the network traffic characteristic is a subject of frequent changes in modern web systems and the huge content of system logs makes it a difficult area for data mining research. In this work, we investigate prepared trace contents that are obtained from the benchmark of the web system. The article proposes traffic classification on the web system that is used to find the behavior of client classes. We present a case study involving workload analysis of an online stock trading application that is run in the cloud, and that processes requests from the designed generator. The results show that the proposed analysis could help us better understand the requests scenario and select the values of system and application parameters. Our work is useful for practitioners and researchers of log analysis to enhance service reliability. Full article

(This article belongs to the Special Issue Network Traffic: Models, Challenges and Research Opportunities)

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