Source identification of encrypted video traffic in the presence of heterogeneous network traffic

2018 ◽  
Vol 129 ◽  
pp. 101-110 ◽  
Author(s):  
Yan Shi ◽  
Arun Ross ◽  
Subir Biswas
Keyword(s):  
Network Traffic ◽  
Source Identification ◽  
Heterogeneous Network ◽  
Video Traffic

Transporting TDM Service on Metropolitan Bus-Based Optical Packet Switching Networks

2008 ◽  
pp. 653-662
Author(s):  
Viet Hung Nguyen ◽  
Tülin Atmaca
Keyword(s):  
Network Traffic ◽  
Packet Switching ◽  
Service Providers ◽  
Internet Traffic ◽  
Traffic Volume ◽  
Optical Packet Switching ◽  
Video Traffic ◽  
Voice Traffic ◽  
Optical Packet Switching Networks ◽  
Packet Switching Networks

Today’s telecommunication world is seeing dramatic changes in network infrastructures and services. These changes are mainly driven by the ever-growing rate of network traffic. Global Internet traffic is doubling each year due to both tremendous growth in the number of users and rapid increase of bandwidth accessible by each user (e.g., the Global Internet Geography report (2004) stated that in Asia, Internet traffic growth was about 400 percent in the year 2004). Not only is network traffic growing at an unprecedented speed, but the traffic mix is changing greatly. The traditional voice traffic volume has now become very small relative to the huge volume of data and video traffic, due to the deployment of Gigabit technologies in the access part of the service providers’ networks.

scholarly journals Flow-level Load Balancing of HTTP Traffic using OpenFlow

2018 ◽  
Vol 6 (4) ◽  
pp. 75-95 ◽  
Author(s):  
Anees Al-Najjar ◽  
Marius Portmann ◽  
Siamak Layeghy ◽  
Jadwiga Indulska
Keyword(s):  
Load Balancing ◽  
Network Traffic ◽  
Use Cases ◽  
Video Traffic ◽  
Load Balancer ◽  
Client Side

In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can there­fore easily be deployed. We specifically explore flow-based load balanc­ing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

scholarly journals Video Streaming Service Identification on Software-Defined Networking

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Luis Miguel Castañeda Herrera ◽  
Wilmar Yesid Campo-Muñoz ◽  
Alejandra Duque Torres
Keyword(s):  
Video Streaming ◽  
Network Traffic ◽  
Network Performance ◽  
Video On Demand ◽  
Traffic Classification ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Video Traffic ◽  
Tree Model ◽  
Streaming Traffic

It is well known that video streaming is the major network traffic today. Futhermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, SoftwareDefined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm that takes advantage of Artificial Intelligence (AI) by using Machine Learning approaches, which allows integrating behavioural models to detect patterns, like video streaming traffic identification, in SDN traffic. In our initial proof-of-concept, we derive the relevant information of network traffic in the form of flows statistics. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.

scholarly journals Flow-level Load Balancing of HTTP Traffic using OpenFlow

2018 ◽  
Vol 6 (4) ◽  
pp. 75-95
Author(s):  
Anees Al-Najjar ◽  
Marius Portmann ◽  
Siamak Layeghy ◽  
Jadwiga Indulska
Keyword(s):  
Load Balancing ◽  
Network Traffic ◽  
Use Cases ◽  
Video Traffic ◽  
Load Balancer ◽  
Client Side

In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can there­fore easily be deployed. We specifically explore flow-based load balanc­ing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

scholarly journals Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction

2017 ◽  
Vol 6 (4) ◽  
pp. 283 ◽  
Author(s):  
J.P. Kharat
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Traffic Load ◽  
Traffic Prediction ◽  
Network Architectures ◽  
Nonlinear Prediction ◽  
Video Traffic ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Neural Network Architectures

<p>Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed forward and Time Delay Neural Network. The results show that cascade feed forward network produces minimum error as compared to other networks. This paper also compares the results of traditional prediction method of averaging of frames for future frame prediction with neural based methods. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models.</p>

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