A Method for Evaluation of Quality of Service in Computer Networks

Tomasz Bujlow, Sara Ligaard Hald, M. Tahir Riaz, Jens Myrup Pedersen

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Resumé

Monitoring of Quality of Service (QoS) in high-speed Internet infrastructures is a challenging task. However, precise assessments must take into account the fact that the requirements for the given quality level are service-dependent. The backbone QoS monitoring and analysis requires processing of large amounts of data and knowledge of which kinds of applications the traffic is generated by. To overcome the drawbacks of existing methods for traffic classification, we proposed and evaluated a centralized solution based on the C5.0 Machine Learning Algorithm (MLA) and decision rules. The first task was to collect and to provide to C5.0 high-quality training data divided into groups, which correspond to different types of applications. It was found that the currently existing means of collecting data (classification by ports, Deep Packet Inspection, statistical classification, public data sources) are not sufficient and they do not comply with the required standards. We developed a new system
to collect training data, in which the major role is performed by volunteers. Client applications installed on volunteers' computers collect the detailed data about each flow passing through the network interface, together with the application name taken from the description of system sockets. This paper proposes a new method for measuring the level of Quality of Service in broadband networks. It is based on our Volunteer-Based System to collect the training data, Machine Learning Algorithms to generate the classification rules and the application-specific rules for assessing the QoS level. We combine both passive and active monitoring technologies. The paper evaluates different possibilities of implementation, presents the current implementation of particular parts of the system, their initial runs and the obtained results, highlighting parts relevant from the QoS point of view.
OriginalsprogEngelsk
TidsskriftICACT Transactions on the Advanced Communications Technology
Antal sider9
StatusUdgivet - 17 jul. 2012
Begivenhed2012 14th International Conference on Advanced Communication Technology - PyeongChang, Sydkorea
Varighed: 19 feb. 201222 feb. 2012

Konference

Konference2012 14th International Conference on Advanced Communication Technology
LandSydkorea
ByPyeongChang
Periode19/02/201222/02/2012

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Computer networks
Quality of service
Learning algorithms
Learning systems
Monitoring
Broadband networks
Interfaces (computer)
Inspection
Internet
Processing

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    A Method for Evaluation of Quality of Service in Computer Networks. / Bujlow, Tomasz; Hald, Sara Ligaard; Riaz, M. Tahir; Pedersen, Jens Myrup.

    I: ICACT Transactions on the Advanced Communications Technology, 17.07.2012.

    Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

    TY - GEN

    T1 - A Method for Evaluation of Quality of Service in Computer Networks

    AU - Bujlow, Tomasz

    AU - Hald, Sara Ligaard

    AU - Riaz, M. Tahir

    AU - Pedersen, Jens Myrup

    N1 - Paper ID 112

    PY - 2012/7/17

    Y1 - 2012/7/17

    N2 - Monitoring of Quality of Service (QoS) in high-speed Internet infrastructures is a challenging task. However, precise assessments must take into account the fact that the requirements for the given quality level are service-dependent. The backbone QoS monitoring and analysis requires processing of large amounts of data and knowledge of which kinds of applications the traffic is generated by. To overcome the drawbacks of existing methods for traffic classification, we proposed and evaluated a centralized solution based on the C5.0 Machine Learning Algorithm (MLA) and decision rules. The first task was to collect and to provide to C5.0 high-quality training data divided into groups, which correspond to different types of applications. It was found that the currently existing means of collecting data (classification by ports, Deep Packet Inspection, statistical classification, public data sources) are not sufficient and they do not comply with the required standards. We developed a new system to collect training data, in which the major role is performed by volunteers. Client applications installed on volunteers' computers collect the detailed data about each flow passing through the network interface, together with the application name taken from the description of system sockets. This paper proposes a new method for measuring the level of Quality of Service in broadband networks. It is based on our Volunteer-Based System to collect the training data, Machine Learning Algorithms to generate the classification rules and the application-specific rules for assessing the QoS level. We combine both passive and active monitoring technologies. The paper evaluates different possibilities of implementation, presents the current implementation of particular parts of the system, their initial runs and the obtained results, highlighting parts relevant from the QoS point of view.

    AB - Monitoring of Quality of Service (QoS) in high-speed Internet infrastructures is a challenging task. However, precise assessments must take into account the fact that the requirements for the given quality level are service-dependent. The backbone QoS monitoring and analysis requires processing of large amounts of data and knowledge of which kinds of applications the traffic is generated by. To overcome the drawbacks of existing methods for traffic classification, we proposed and evaluated a centralized solution based on the C5.0 Machine Learning Algorithm (MLA) and decision rules. The first task was to collect and to provide to C5.0 high-quality training data divided into groups, which correspond to different types of applications. It was found that the currently existing means of collecting data (classification by ports, Deep Packet Inspection, statistical classification, public data sources) are not sufficient and they do not comply with the required standards. We developed a new system to collect training data, in which the major role is performed by volunteers. Client applications installed on volunteers' computers collect the detailed data about each flow passing through the network interface, together with the application name taken from the description of system sockets. This paper proposes a new method for measuring the level of Quality of Service in broadband networks. It is based on our Volunteer-Based System to collect the training data, Machine Learning Algorithms to generate the classification rules and the application-specific rules for assessing the QoS level. We combine both passive and active monitoring technologies. The paper evaluates different possibilities of implementation, presents the current implementation of particular parts of the system, their initial runs and the obtained results, highlighting parts relevant from the QoS point of view.

    KW - broadband networks

    KW - data collecting

    KW - Machine Learning Algorithms

    KW - performance monitoring

    KW - Quality of Service

    KW - traffic classification

    KW - volunteer-based system

    M3 - Conference article in Journal

    JO - ICACT Transactions on the Advanced Communications Technology

    JF - ICACT Transactions on the Advanced Communications Technology

    SN - 2288-0003

    ER -