A FUZZY LOGIC CLASSIFICATION OF INCOMING PACKET FOR VOIP

The Voice over Internet Protocol (VoIP) technology is cheaper and does not need new infrastructure because it has availables in the global computer (IP) network. Unfortunately, transition from PSTN to VoIP networks have emerged new problems in voice quality. Furthermore, the transmission of voice over IP networks can generate network congestion due to weak supervision of the traffic incoming packet, queuing and scheduling. This congestion affects the Quality of Service (QoS) such as delay, packet drop and packet loss. Packet delay effects will affect the other QoS such as: unstable voice packet delivery, packet jitter, packet loss and echo. Priority Queuing (PQ) algorithm is a popular technique used in the VoIP network to reduce delays. But, the method can result in repetition. This recursive leads to the next queue starved. To solving problems, there are three phases namely queuing, classifying and scheduling. It will be applied to the fuzzy inference system to classify the queuing incoming packet (voice, video and text). To justify the research of the improved PQ algorithm be compared against the algorithm existing

Open FPGA-based development platform for fuzzy systems with applications to communications

Soft computing techniques are gaining momentum as tools for network traffic modeling, analysis and control. Efficient hardware implementations of these techniques that can achieve real-time operation in high-speed communications equipment is however an open problem. This paper describes a platform for the development of fuzzy systems with applications to communications systems, namely network traffic analysis and control. An FPGA development board with PCI interface is employed to support an open platform that comprises open CAD tools as well as IP cores. For the development process, we set up a methodology and a CAD tools chain that cover from initial specification in a high-level language to implementation on FPGA devices. PCI compatible fuzzy inference modules are implemented as SoPC based on the open WISHBONE interconnection architecture. We outline results from the design and implementation of fuzzy analyzers and regulators for network traffic. These systems are shown to satisfy operational and architectural requirements of current and future high-performance routing equipment.Ministerio de Educación y Ciencia TEC2005-04359/MICJunta de Andalucía TIC2006-63

Dynamic QoS Solution for Enterprise Networks Using TSK Fuzzy Interpolation

The Quality of Services (QoS) is the measure of data transmission quality and service availability of a network, aiming to maintain the data, especially delay-sensitive data such as VoIP, to be transmitted over the network with the required quality. Major network device manufacturers have each developed their own smart dynamic QoS solutions, such as AutoQoS supported by Cisco, CoS (Class of Service) by Netgear devices, and QoS Maps on SROS (Secure Router Operating System) provided by HP, to maintain the service level of network traffic. Such smart QoS solutions usually only work for manufacture qualified devices and otherwise only a pre-defined static policy mapping can be applied. This paper presents a dynamic QoS solution based on the differentiated services (DiffServ) approach for enterprise networks, which is able to modify the priority level of a packet in real time by adjusting the value of Differentiated Services Code Point (DSCP) in Internet Protocol (IP) header of network packets. This is implemented by a 0-order TSK fuzzy model with a sparse rule base which is developed by considering the current network delay, application desired priority level and user current priority group. DSCP values are dynamically generated by the TSK fuzzy model and updated in real time. The proposed system has been evaluated in a real network environment with promising results generated

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online

Study of Fuzzy Logic-based Controller for Diff-Serv Bandwidth Broking

Combining both voice and data on the same network infrastructure results in need for an advanced network which is to be simple and scalable.  This resulted in new approach for Ethernet where Differentiated Service (Diff-Serv) is introduced. This is taken into consideration as well as QoS as a way of providing class of service to end users. Therefore, optimizing available bandwidth efficacy is one of the goals of this work which is centered on investigating the impact of crucial factors on performance of implementing a fuzzy logic controller. These factors can be external or internal and will be manipulated by Fuzzy Logic controller that will work as bandwidth broker to give each user his optimal Code Point (CP). In this work, the CP will not only consider packet loss rate as external factor  to check the congestion, but it will also consider the internal factors which are a combination of both service-level agreement (SLA) and the type of application being used. This CP will be marked in the transmitted packets, and then the router will check that and will treat it as agreed between user and administrator