Quality Of Service Classification Research Articles

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

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 offered by networks is essential for both network users and network service providers in order 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 in order 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.

Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

QOS CATEGORIES ACTIVENESS-AWARE ADAPTIVE EDCA ALGORITHM FOR DENSE IOT NETWORKS

IEEE 802.11 networks have a great role to play in supporting and deploying of the Internet of Things (IoT). The realization of IoT depends on the ability of the network to handle a massive number of stations and transmissions, and to support Quality of Service (QoS). IEEE 802.11 networks enable the QoS by applying the Enhanced Distributed Channel Access (EDCA) with static parameters regardless of existing network capacity or which Access Category (AC) of QoS is already active. Our objective in this paper is to improve the efficiency of the uplink access in 802.11 networks; therefore we proposed an algorithm called QoS Categories Activeness-Aware Adaptive EDCA Algorithm (QCAAAE) which adapts Contention Window (CW) size, and Arbitration Inter-Frame Space Number (AIFSN) values depending on the number of associated Stations (STAs) and considering the presence of each AC. For different traffic scenarios, the simulation results confirm the outperformance of the proposed algorithm in terms of throughput (increased on average 23%) and retransmission attempts rate (decreased on average 47%) considering acceptable delay for sensitive delay services.

Probabilistic classification of quality of service in wireless computer networks

There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as it can facilitate evaluation of their performance and can provide mechanisms to improve their operation. In this study probabilistic neural network (PNN) and Bayesian classification were developed to process delay, jitter and percentage packet loss ratio for VoIP traffic. Both methods successfully categorized the transmission of VoIP packets into low, medium and high QoS categories but overall the Bayesian approach performed more accurately than PNN. By accurately determining the network's QoS, an improved understanding of its performance is obtained.

An Improved Task Scheduling Algorithm Based on Multi-QoS in Cloud Computing

For the issues of quality of service (QoS) in the cloud computing raised by users, this paper proposes a strategy for QoS classification and builds tasks' priority function modeling and through priority scheduling tasks, assigning tasks to the reasonable resources, and finally to complete the task efficiently, improve the utilization of resources.

Implementation of a reconfigurable turbo decoder in 3GPP for flat Rayleigh fading

Quality of service is a critical consideration in the design of mobile systems, since it allows the user to receive high quality services. Therefore, in 3GPP systems, in order to realise a particular service, the quality of service requirements in terms of performance and latency, have to be satisfied. Turbo code features include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving and an associated iterative decoding algorithm. Exploiting the quality of service classification according to the priority of latency or performance, possible examples of service scenarios are examined for flat Rayleigh fading channels with emphasis on the turbo decoding algorithm. Particularly, for two operating environments considering SOVA and log-MAP algorithms due to their data-flow similarities, this paper shows that SOVA is clearly optimal for most of the real-time applications, whereas for nonreal time applications with low data rate and small frames log-MAP is preferred. The use of the optimum algorithm in most scenarios results in a more efficient turbo decoder: applications that otherwise would have failed now can be realised.

Distributed Channel Access for QoS Control in Link Adaptive Wireless LANs

In wireless local area networks (WLANs), the necessity of quality-of-service (QoS) control for uplink flows is increasing because interactive applications are becoming more popular. Fairness between flows transmitted by stations with different physical transmission rates must be ensured in QoS control for link-adaptive WLANs, which are widely used nowadays. We propose a novel distributed access scheme called QC-DCA to satisfy these requirements. QC-DCA adaptively controls the parameters of carrier sense multiple access with collision avoidance (CSMA/CA). QC-DCA has two QoS control functions: guarantee and classification. QC-DCA guarantees target throughputs and packet delays by quickly adjusting CSMA/CA parameters. In QoS classification, the difference of throughputs and packet delays between different QoS classes is maintained. These two functions allow QC-DCA to suppress the unfairness caused by differences of transmission rates in the physical layer. We evaluated the throughput and delay performances of our scheme using computer simulations. The results show the viability of our scheme.

Vector Quantization Based QoS Classification for Admission Control in CDMA Systems

Optimal power control is of great importance for CDMA systems and it can be controlled to provide the desired quality of service (QoS) to mobile hosts in a cellular radio system. The power levels of all the mobile hosts are determined and constantly tuned in order to achieve the required SINR (signal to interference and noise ratio) which changes dynamically. The SINR of all the K mobiles in a cell can be expressed in the form of a k-dimensional vector. It helps determine the operating point of the system and hence it is constantly monitored and updated due to the variability in the wireless channel conditions and user mobility. We view this continuously changing vector as the motion of a point in a higher dimensional Euclidean space, called the QoS space. We apply vector quantization technique to shrink the infinite-point space to a finite-point space by partitioning the former into N regions such that the points within a region reflect almost similar system performance and are identified by what we call a QoS index. We show how the system operating point can be mapped to one of the QoS indices. The location of the point or the region of operability in the QoS space conveys the system status in terms of the current load and the QoS being delivered. The dynamism in the system's input conditions due to wireless link characteristics and user mobility acts like an opposing force against which the system has to operate. The system reacts to all such changes preventing it from going into a region with an undesirable QoS index. We show how the apriori knowledge of the operating region helps in decision making pertaining to call admission and resource allocation in CDMA systems.

Quality-of-service specification in distributed object systems

Traditional object-oriented design methods deal with the functional aspects of systems, but they do not address quality-of-service (QoS) aspects, such as reliability, availability, performance, security and timing. However, deciding which QoS properties should be provided by individual system components is an important part of the design process. Different decisions are likely to result in different component implementations and system structures. Thus, decisions about component-level QoS should commonly be made at design time, before the implementation is begun. Since these decisions are an important part of the design process, they should be captured as part of the design. We propose a general quality-of-service specification language, which we call QML. In this paper we show how QML can be used to capture QoS properties as part of designs. In addition, we extend UML, the de facto standard object-oriented modelling language, to support the concepts of QML. QML is designed to integrate with object-oriented features, such as interfaces, classes and inheritance. In particular, it allows specification of QoS properties through refinement of existing QoS specifications. Although we exemplify the use of QML to specify QoS properties within the categories of reliability and performance, QML can be used for specification within any QoS category - QoS categories are user-defined types in QML. Sometimes, QoS characteristics and requirements change dynamically due to changing user preferences, or changes in the environment. For such situations static specification is insufficient. To allow for dynamic systems that change and evolve over time, we provide a QoS specification runtime representation. This representation enables systems to create, manipulate and exchange QoS information, and thereby negotiate and adapt to changing QoS requirements and conditions.