A Review of Fuzzy Logic Model for Analysis of Computer Network Quality of Experience (original) (raw)

Analysis of Computer Network Quality of Experience Using Fuzzy Logic Model: A Survey

-The estimation of the QoE provides valuable input in order to measure the user satisfaction of a particular service/application. Network QoE estimation is challenging as it tries to measure a subjective metric where the user experience depends on a number of factors that cannot easily be measured. All the Network analysis models can be divided into two major groups: qualitative and quantitative. In recent years many quantitative models have been developed in terms of quantitative measures i.e. use of scale of numbers between 1 to 5 to represent user perception of QoS. The challenge with this model is where user perception is subjective and not precise thus cannot be clearly measured using quantitative methods. On the other side qualitative models are in early stages of exploration. Little has been done on qualitative methods. Basing on previous studies, few models exists that measure qualitative analysis of computer network quality of experience. However none incorporated all the four parameters of integrity of service; throughput, delay, packet loss and jitter as parameters of network QoE. In this work, a literature survey is done on qualitative performance by use of a variety of variables, input and output linguistic terms. After a broad survey of the literature, we tend to propose a fuzzy logic model for analysis of computer network QoE. Likewise, the model combines all the four parameters of network integrity of service parameters since they are the primary factor for QoS quantification of any network [1]. Moreover, by using the fuzzy logic concept, the output linguistic terms shows the user perception about a product or a service (QoE) to certain levels by use of membership functions, in this case triangular membership function which shows the mapping of each linguistic term to certain range of values rather being precise to a particular value. By such means, the developed fuzzy logic model tends to accommodate some degree of uncertainty and vague network values to be used for analysis purposes. The concern is to advance the analysis and evaluation of quality of experience in computer networks by use of fuzzy logic concept. The target population for this model is the ISPs' clients. This will enable ISPs to have the best responsive measures to deal with clients' QOE parameters so as to meet the QOS as per SLAs.

Fuzzy Logic Model for Analysis of Computer Network Quality of Experience

IJCSIS Vol 17 No 4 April Issue, 2019

The estimation of the QoE provides valuable input in order to measure the user satisfaction of a particular service/application. Network QoE estimation is challenging as it tries to measure a subjective metric where the user experience depends on a number of factors that cannot easily be measured. All the Network analysis models can be divided into two major groups: qualitative and quantitative. In recent years many quantitative models have been developed in terms of quantitative measures i.e. use of scale of numbers between 1 to 5 to represent user perception of QoS. The challenge with this model is where user perception is subjective and not precise thus cannot be clearly measured using quantitative methods. On the other side qualitative models are in early stages of exploration. Little has been done on qualitative methods. Basing on previous studies, few models exists that measure qualitative analysis of computer network quality of experience. However none incorporated all the four parameters of integrity of service; throughput, delay, packet loss and jitter as parameters of network QoE. The study's objective is to address this gap by proposing a fuzzy logic model for analysis of computer network QoE. The tools used in the study are Linux MTR tool for data extraction, Ms. Excel for data cleaning and presentation, Visual paradigm for constructing of Unified Modeling language diagrams, mat lab software for plotting of functions/data, implementation of algorithms and creation of user interfaces. Experimental research design and consecutive sampling is applied for 15 samples. The methodology in use is fuzzy logic. In order to deal with fuzziness associated with linguistic variables, inference rules are introduced. Five input linguistic terms are identified: Very High, High, Medium, Low and Very Low. Five output linguistic terms are defined to describe the opinion scores: Excellent, Good, Fair, Poor and Bad. Four variables are used: delay, jitter, packet loss and throughput. This results to a total of 625 rules (5^4). The rules are further condensed to 240 logical rules basing on expert knowledge. The collected data was used for simulation in matlab environment basing on the 240 rules. The results shows, analysis of Computer network QoE is subjective in nature rather than objective thus requires a resilient mechanism like fuzzy logic in order to capture clear-cut results to be used for decision making. The target population for this model is the ISPs' clients. This will enable ISPs to have the best responsive measures to deal with clients' QOE parameters so as to meet the QOS as per SLAs.

Fuzzy Logic based Quality of Service Models

The continuous monitoring of information systems' quality of service increases importance as business becomes more and more dependent of those systems. In order to obtain that view, quality models need to be defined for those systems. Because of its complexity and today modelling frameworks, quality models tend to result in a poor representation of reality, mainly because of their lack of ability to represent uncertainty. In this work, we investigate the use of fuzzy logic's properties to create a new kind of quality of service models, which handles uncertainty and imprecision naturally. The objective is to obtain models that are a better representation of reality and easier to create and understand. This article presents the investigation on related topics to support the identified problem and motivations, followed by a solution proposal and a validation scenario.

A unified metric for quality of service quantification

2009

Internet service providers usually express the quality of network services through a set of values determined according to several network performance parameters periodically collected or measured. However, for common end-users, these values do not give an overall idea of the quality of the network services as they stand for different units and evaluate different perspectives of each service quality. In this context, this paper proposes the definition of a serviceoriented unified metric which quantifies a global Quality of Service (QoS) indication by processing standard QoS parameters through a fuzzy controller. The proposed methodology, based on fuzzy logic and tested on Xfuzzy 3.0 platform, allows to close the gap between a high-level QoS perspective and the effective QoS measurements at lower protocolar levels. The definition of a single per-service QoS metric can be useful to simplify control tasks such as QoS routing, SLA negotiation and auditing.

A New Approach Based on Intelligent Method to Classify Quality of Service

Informatica

Computer networks are used more frequently for time-sensitive applications like voice over internet protocol and other communications. In computer networks, quality of service (QoS) can be crucial since it makes it easier to assess a network's performance and offers mechanisms for enhancing its performance. As a result, understanding the QoS provided by networks is essential for both network users and service providers to assess how well the transmission requirements of different applications are satisfied and to implement improvements to network performance. Next-generation monitoring systems must not only detect network performance deterioration instantly but also pinpoint the underlying cause of quality of service problems to achieve strict network standards. A brand-new fuzzy logic-based algorithm is suggested as a solution to this issue. Thus, the proposed approach was evaluated and compared with probabilistic neural networks (PNN) and Bayesian classification, as well as network performance measurement, latency, jitter, and packet loss. All approaches correctly classified the QoS categories, although generally, the fuzzy approach outperformed PNN and Bayesian. An improved comprehension of the network performance is acquired by precisely determining its QoS. Povzetek: Razvit je nov algoritem za odkrivanje vzroka za poslabšano kvaliteto storive v omrežjih.

Assessment The Quality Of Telecommunication Services By Fuzzy Inferences System

2015

Fuzzy inference method based approach to the<br> forming of modular intellectual system of assessment the quality of<br> communication services is proposed. Developed under this approach<br> the basic fuzzy estimation model takes into account the<br> recommendations of the International Telecommunication Union in<br> respect of the operation of packet switching networks based on IPprotocol.<br> To implement the main features and functions of the fuzzy<br> control system of quality telecommunication services it is used<br> multilayer feedforward neural network.

Fuzzy Logic Based Quality of Service Broker Model: Simulation in Simulink

2011

Today, increasingly complex control problems abound, for which mathematical modelling representations may be difficult to obtain. This study relied on the reported strength of the fuzzy logic technique in controlling highly nonlinear systems to improve the user perceived Quality of Service (QoS) of the web application. The fuzzy logic based QoS broker model was designed, modelled, and simulated using MATLAB 7.0; its performance was evaluated using two QoS parameters: Response time and throughput. The simulation results revealed an increase in throughput as well as a reduction in response times perceived by the clients.

Modeling Video Streaming Quality of Experience using Taguchi and Fuzzy Logic Methods

Journal of ICT Systems

The popularity of mobile video streaming has increased significantly in recent years, and is expected to account for two-thirds of global internet traffic in the near future. However, determining accurately end-users' satisfaction based on network parameters remains a challenge. Existing research often uses network parameters, such as packet loss, delay, and jitter, to estimate users' Quality of Experience (QoE). However, most models present QoE estimates in Mean Opinion Scores (MoS), which are not easily understood by the customers. In this study, we used the Taguchi approach to conduct QoE experiments over a wireless tested. We investigated the simultaneous effects of packet loss, corruption, delay, and jitter on video streaming QoE, as well as their interaction effects. Furthermore, we developed a Fuzzy logic model in MatlabR2016a to establish the relationship between input variables and video streaming QoE. The model presents the results in an easily understandable lingu...