A computational framework for quality of service measurement, visualization and prediction in mission critical communication networks
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Description
Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently due to ever increasing use of the Internet and other public and private communication networks. Fast and precise QoS analysis is a vital task in mission-critical communication networks (MCCNs), where providing a certain level of QoS is essential for national security, safety or economic vitality. In this thesis, the details of all aspects of a comprehensive computational framework for QoS analysis in MCCNs are provided. There are three main QoS analysis tasks in MCCNs; QoS measurement, QoS visualization and QoS prediction. Definitions of these tasks are provided and for each of those, complete solutions are suggested either by referring to an existing work or providing novel methods.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.