Szabolcs Malomsoky

On the validation of traffic classification algorithms

Detailed knowledge of the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Traffic classification aims at identifying the traffic mixture in the network. Several different classification approaches can be found in the literature. However, the validation of these methods is weak and ad hoc, because neither a reliable and widely accepted validation technique nor reference packet traces with well-defined content are available. In this paper, a novel validation method is proposed for characterizing the accuracy and completeness of traffic classification algorithms. The main advantages of the new method are that it is based on realistic traffic mixtures, and it enables a highly automated and reliable validation of traffic classification. As a proof-of-concept, it is examined how a state-of-the-art traffic classification method performs for the most common application types.

Connection admission control in UMTS radio access networks

On transport links of UMTS Terrestrial Radio Access Networks, but especially on those connecting base stations and radio network controllers (i.e. on the Iub interface), resource allocation is complex, because packet delay and loss requirements are strict and the amount of transmission resources is relatively low. In this paper a novel connection admission control (CAC) algorithm is provided, which is applicable on the Iub interface with both ATM/AAL2 and IP transport options. The CAC algorithm is validated by mathematical analysis and computer simulation.

Connection Admission Control in the UTRAN Transport Network

In this paper we propose a Connection Admission Control (CAC) algorithm to provide Quality of Service (QoS) in UMTS Terrestrial Radio Access Networks (UTRAN). In UTRAN, the main QoS requirement is to ensure low packet delays. The CAC algorithm works with priority scheduling, which is used for QoS differentiation. We give a detailed investigation on the performance implications of applying priority scheduler. We provide novel closed-form formulae which are fast, simple and accurate enough for practical implementation of the CAC. A comprehensive performance evaluation study with illustrative numerical examples is also presented. The results are validated by simulations.

When To take what: QoE-aware resource redistribution among web browsing users and the potential of prioritizing QoE sensitive content

In this paper we propose evaluation models and calculate range of potential gains for enhancing the average web-browsing QoE of all the users in a mobile network with given capacity. We investigate a method that takes capacity from users during QoE insensitive periods. The gained capacity is distributed among users being in QoE affecting state. Our models of web page network prioritization show that the potential gain for QoE affecting periods highly depends on the correlation between data downloaded during QoE affecting and QoE insensitive periods. The numerical evaluation of wide range of scenarios with web traffic shows that the achievable gain is up to 104% i.e., halving the download time of QoE affecting part. Network prioritization of web pages has good potential to improve web-browsing QoE.

Potential Gains of Reactive Video QoE Enhancement by App Agnostic QoE Deduction

We propose an app agnostic QoE deduction method that can be used for on-line traffic prioritization. Our implemented method extracts QoE information directly from the screen of mobile devices and this information is utilized for video traffic prioritization to improve user QoE. We evaluate our method and show that it can realize 80-100% of the achievable gain of the state-of-the- art methods with 50% less traffic that needs to be prioritized to achieve this. We provide deployment alternatives for traffic prioritization and evaluate performance impact on achievable gain.

Mobility and Traffic Analysis for WCDMA Networks

In cellular mobile communication systems, the mobility of vehicles affects some important parameters, such as handover rates, channel occupancy times and blocking probabilities. The present work is based on a model proposed in [1] that suggests a convenient and practical approach to build an analytic traffic model, which also includes the effect of vehicle mobility.