At QoMEX 2024, a stereoscopic 360-degree hybrid conference experience was demonstrated. The camera itself was standing some few meters away from the test person, which meant that the latter could see her-/himself with a latency of one to two seconds. While ACR scores of overall, image quality, depth perception were positive and in line with each other, the latency-related DCR scores were more ambiguous. Corresponding statistics will be shown in this paper along with qualitative feedback from the test persons. The demonstration in 2025 takes the step to a more realistic use case where the stereoscopic 360-degree camera will be placed in Sweden, about 2200 km away from the QoMEX 2025 location in Madrid. Immediate quantitative and qualitative insights are expected to demonstrate the trade-off between image quality and delay perceptions that come with Trans-European Event Broadcasting (TEEB) over various networks.

The 2024 edition of QoMEX has the motto "Towards Immersive Digiphysical Experiences". While the conference as such is organised as an on-site event, the keynotes and demonstration session(s) will be hybrid, and the demonstrations will be complemented by specific hybrid experiences. For the first time in the history of QoMEX, streaming of the keynotes will be performed using a stereoscopic 360-degree format, which can be enjoyed by a set of remote participants using Oculus headsets. This paper provides an overview of the technical solution behind this hybrid experience. It also reports on tests and qualitative user experiences obtained so far. The visitors of the demonstration and hybrid experience session will have the opportunity to take a closer look at the system, experience it in action and report the quality of their experience.

The performance of remotely rendered Virtual Re-ality (VR) is sensitive to temporal disturbances in communication channels. An earlier Quality of Experience (QoE) study of tem-poral impacts in the form of frame stalls has revealed difficulties with subjective disturbance ratings while performing a task in an interactive 6-degrees-of-freedom (DOF) VR environment. This study follows up on above observation by comparing QoE ratings in the presence and absence of a task. The exploratory findings show that the task-free subjective tests yield lower ratings compared to the subjective tests with task. This indicates that the participants became more sensitive to temporal impairments in the absence of a task. Also, the positive impact of reprojection on the QoE ratings decreased in the task-free environment. The simulator sickness results for individual symptoms were on similar low levels in both settings. The total score (TS) of sickness severity was higher after than before the subjective tests with task while the difference between the TS before and after the task-free subjective tests was insignificant,

Remotely rendered Virtual Reality (VR) typically has to rely upon less capable and less reliable communication channels as compared to locally rendered VR. Such communication channels pose the risk of impairing the VR experience with temporary disturbances in form of frame stalls that would not be present in local VR. However, it is not obvious to which extent the durations of temporary stalls affect the Quality of Experience (QoE) in interactive 6-degrees-of-freedom (DOF) VR, and neither to which extent present reprojection/warping techniques mitigate any adverse effects of such stalls on user perception. In this paper, subjective experiments were conducted with N =29 users to quantify the relationship between QoE and single, isolated stall durations in interactive 6-DOF remote VR when stalls either freeze the frame in place, or when frames are reprojected by the VR driver. Results indicate that given a 90 fps headset and a simple task that requires moderate head-movement; (1) short, isolated stalls of up to 4 frames go unnoticed on average, no matter whether reprojection is used or not, (2) the number of perceived stalls when using reprojection is the same or lower than the reference (no stall) in the range 1–4 frame stalls, (3) reprojection entails significantly higher user ratings for stalls ranging from 8 frames and beyond, (4) the improvement of using reprojection over freezes in terms of making more stalling events imperceptible is highest in the range 8–16 frames.

A dataset has been created from an experiment regarding the subjective Quality of Experience (QoE) in relation to stalling video feeds in interactive 6-Degrees-of-freedom Virtual Reality (6-DOF VR). The main purpose of collecting this data is to provide insight into the QoE of stalls that may occur in interactive remote-rendered 6-DOF VR. The data contains information regarding user ratings coupled with various stall lengths and for both frozen or reprojected images. Additionally, user motion and pixel changes (in the form of Mean Square Errors) in successive images in the vicinity of the stall are also stored. In summary, 29 users participated in testing, each rating 16 scenes, amounting to a total of 464 rated scenes.

Rendering VR-content generally requires large image resolutions. This is both due to the display being positioned close to the eyes of the user and to the super-sampling typically used in VR. Due to the requirements of low latency and large resolutions in VR, remote rendering can be difficult to support at sufficient speeds in this medium.In this paper, we propose a method that can reduce the required resolution of non-panoramic VR images from a codec perspective. Because VR images are viewed close-up from within a headset with specific lenses, there are regions of the images that will remain unseen by the user. This unseen area is referred to as the Hidden-Area Mesh (HAM) and makes up 19% of the screen on the HTC Vive VR headset as one example. By remapping the image in a specific manner, we can cut out the HAM, reduce the resolution by the size of the mesh and thus reduce the amount of data that needs to be processed by encoder and decoder. Results from a prototype remote renderer show that by using the proposed Hidden-Area Mesh Remapping (HAMR), an implementation-dependent speed-up of 10-13% in encoding, 17-18% in decoding and 7-11% in total can be achieved while the negative impact on objective image quality in terms of SSIM and VMAF remains small.

This paper shows the impact of bitrate settings on objective quality measures when streaming non-panoramic remote-rendered Virtual Reality (VR) images. Non-panoramic here refers to the images that are rendered and sent across the network, they only cover the viewport of each eye, respectively.

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating “good or better”, etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.

Some of the most important aspects for content creators and service providers are the content appeal and the time consumed by the end users on a particular application or service. Gender and age can influence the Quality of Experience (QoE) ratings of multimedia based on the nature of the shown content, yet few studies have quantized this notion. In this paper, we zoom in on the influence of gender on user ratings in a video QoE study (N=89) with packet loss as the main system influence factor. We have analyzed the impact of gender on QoE subjective ratings both as a standalone influence factor and in coherence of temporal traits like the frequency of watching online content. We have observed significant trends to highlight the importance of systematically checking and reporting on the impact of basic human factors, such as gender in relation to quality perception with respect to different types of content.

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance.

For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may not always be immediately visible from underlying service- or network-level monitoring data. Thus, there is the challenge to pinpoint such moments of change in provisioning, and model the impact on instantaneous QoE, as a lead in root cause analysis. This work investigates the potential of Machine Learning (ML) of deriving time-interval-based models for instantaneous QoE ratings, obtained from a set of publicly available rating traces. In particular, the paper demonstrates the capability of the ML algorithm M5P to model trends of instantaneous QoE through model trees, consisting of piecewise linear functions over time. It is shown how and to which extent these functions can be used to estimate moments of change. Furthermore, the model trees support earlier assumptions about exponential shapes of instantaneous QoE over time as reactions to sudden changes of provisioning, such as video freezes.

Virtual Reality (VR) applications put high demands on software and hardware in order to enable an immersive experience for the user and avoid causing simulator sickness. As soon as networks become part of the Motion-To-Photon (MTP) path between rendering and display, there is a risk for extraordinary delays that may impair Quality of Experience (QoE). This short paper provides an overview of latency measurements and models that are applicable to the MTP path, complemented by demands on user and network levels. It specifically reports on freeze duration measurements using a commercial TPCAST wireless VR solution, and identifies a corresponding stochastic model of the freeze length distribution, which may serve as disturbance model for VR QoE studies.

This short paper presents the recently published Dagstuhl Manifesto `QoE Vadis?`. The Manifesto is the result of a set of three Dagstuhl Seminars and one Dagstuhl Perspectives Workshop, aimed at shaping understanding, development and application of the Quality of Experience (QoE) notion and concept. Its task is to convey the current status, promising developments and future projections for different stakeholders. The latter are summarised in a set of eleven recommendations to academia, industry and funding organisations.

Companies are interested in knowing how users experience and perceive their products. Quality of Experience (QoE) is a measurement that is used to assess the degree of delight or annoyance in experiencing a software product. To assess QoE, we have used a feedback tool integrated into a software product to ask users about their QoE ratings and to obtain information about their rationales for good or bad QoEs. It is known that requests for feedback may disturb users; however, little is known about the subjective reasoning behind this disturbance or about whether this disturbance negatively affects the QoE of the software product for which the feedback is sought. In this paper, we present a mixed qualitative-quantitative study with 35 subjects that explore the relationship between feedback requests and QoE. The subjects experienced a requirement-modeling mobile product, which was integrated with a feedback tool. During and at the end of the experience, we collected the users’ perceptions of the product and the feedback requests. Based on the users’ rational for being disturbed by the feedback requests, such as “early feedback,” “interruptive requests,” “frequent requests,” and “apparently inappropriate content,” we modeled feedback requests. The model defines feedback requests using a set of five-tuple variables: “task,” “timing” of the task for issuing the feedback requests, user’s “expertise-phase” with the product, the “frequency” of feedback requests about the task, and the “content” of the feedback request. Configuration of these parameters might drive the participants’ perceived disturbances. We also found that the disturbances generated by triggering user feedback requests have negligible impacts on the QoE of software products. These results imply that software product vendors may trust users’ feedback even when the feedback requests disturb the users.

Most Quality of Experience (QoE) studies report only the mean opinion scores (MOS) and existing models typically map Quality of Service (QoS) parameters to the MOS. However, service providers may be interested in the share of users that are not at all satisfied, and their corresponding QoE levels. From the QoE management point of view, the circumstances leading to the QoE levels perceived by a certain percentage of users, e.g. the 10% most annoyed users, are of utmost importance. Proper metrics are the 10%-quantiles of QoE values. Knowledge of those quantiles helps service providers to estimate the need for countermeasures in order to prevent annoyed users from churning on one hand, and to avoid overprovisioning on the other hand. The contribution of this paper is the derivation of quantiles from existing MOS-QoS relations. This allows to reuse existing subjective MOS results and MOS models without rerunning the experiments. We consider exemplary the IQX model (describing the MOS-QoS relation) for the derivation of the quantile-QoS relation. A practical guideline for the computation of the quantiles is provided.

Quality of Experience (QoE) management is a prime topic of research community nowadays as video streaming, online gaming and security applications are completely reliant on the network service quality. Moreover, there are no standard models to map Quality of Service (QoS) into QoE. HTTP media streaming is primarily used for such applications due to its coherence with the Internet and simplified management. The most common video codecs used for video streaming are H.264/AVC and Google's VP8. In this paper, we have analyzed the performance of these two codecs from the perspective of QoE. The most common end-user medium for accessing video content is via home based wireless networks. We have emulated an error-prone wireless network with different scenarios involving packet loss, packet delay and delay variation. The focus of this paper is evaluating the end user satisfaction with the multimedia content by subjective assessment using Mean Opinion Score (MOS). We have observed the overall superiority of H.264/AVC but the VP8 codec strongly gains ground in highly error-prone networks in terms of user satisfaction.

In this paper, we study the influence of video stalling on QoE. We provide QoE models that are obtained in realistic scenarios on the smartphone, and provide energy-saving approaches for smartphone by leveraging the proposed QoE models in relation to energy. Results show that approximately 5J is saved in a 3 minutes video clip with an acceptable Mean Opinion Score (MOS) level when the video frames are skipped. If the video frames are not skipped, then it is suggested to avoid freezes during a video stream as the freezes highly increase the energy waste on the smartphones.

Quality requirements, an important class of non-functional requirements, are inherently difficult to elicit. Particularly challenging is the definition of good-enough quality. The problem cannot be avoided though, because hitting the right quality level is critical. Too little quality leads to churn for the software product. Excessive quality generates unnecessary cost and drains the resources of the operating platform. To address this problem, we propose to elicit the specific relationships between software quality levels and their impacts for given quality attributes and stakeholders. An understanding of each such relationship can then be used to specify the right level of quality by deciding about acceptable impacts. The quality-impact relationships can be used to design and dimension a software system appropriately and, in a second step, to develop service level agreements that allow re-use of the obtained knowledge of good-enough quality. This paper describes an approach to elicit such quality-impact relationships and to use them for specifying quality requirements. The approach has been applied with user representatives in requirements workshops and used for determining Quality of Service (QoS) requirements based the involved users' Quality of Experience (QoE). The paper describes the approach in detail and reports early experiences from applying the approach.

Recent Internet of Things (IoT) research has been aiming at interoperability of devices and the integration of sensor networks. The Future Internet - Private Public Partnership (FI-PPP) has created a whole array of different purpose-oriented modules with defined specifications, better known as Generic Enablers. This article gives an overview of legal, ethical and technical norms and standards to be considered when planning, developing and implementing modular medical architectures, integrating the Internet of Things (IoT) and Generic Enablers (GEs) in cutting edge, latest generation medical data networks.

Mobile devices with ever-increasing functionality and the ubiquitous availability of wireless communication networks are driving forces behind innovative mobile applications enriching our daily life. One of the performance measures for a successful application deployment is the ability to support application-data flows by heterogeneous networks within certain delay boundaries. However, the quantitative impact of this measure is unknown and practically infeasible to determine at real time due to the mobile device resource constraints. We research practical methods for measurement-based performance evaluation of heterogeneous data communication networks that support mobile application-data flows. We apply the lightweight Comparative Output-Input Analysis (COIA) method estimating an additional delay based on an observation interval of interest (e.g., 1 second) induced on the flow. An additional delay is the amount of delay that exceeds nonavoidable, minimal end-to-end delay caused by the networks propagation, serialization, and transmission. We propose five COIA methods to estimate additional delay, and we validate their accuracy with measurements obtained from the existing healthcare and multimedia streaming applications. Despite their simplicity, our methods prove to be accurate in relation to an observation interval of interest, and robust under a variety of network conditions. The methods offer novel insights into application-data delays with regards to the performance of heterogeneous data communication networks.

ABSTRACT The vision towards the Network of the Future cannot be separated from the fact that today's networks, and networking services are subject to sophisticated and very effective attacks. When these attacks first appeared, spoofing and distributed denial‐of‐service attacks were treated as apocalypse for networking. Now, they are considered moderate damage, whereas more sophisticated and inconspicuous attacks, such as botnets activities, might have greater and far reaching impact. As the Internet is expanding to mobile phones and ‘smart dust’ and as its social coverage is liberalized towards the realization of ubiquitous computing (with communication), the concerns on security and privacy have become deeper and the problems more challenging than ever. Re‐designing the Internet as the Network of the Future is self‐motivating for researchers, and security and privacy cannot be provided again as separate, external, add‐on, solutions. In this paper, we discuss the security and privacy challenges of the Network of the Future and try to delimit the solutions space on the basis of emerging techniques. We also review methods that help the quantification of security and privacy in an effort to provide a more systematic and quantitative treatment of the area in the future. Copyright © 2011 John Wiley & Sons, Ltd.

Traffic shapers are used by researchers to emulate the behavior of networks and applications in test environments, typically with user-defined traffic shaping parameters such as throughput and loss. Also, traffic shapers are used for the enforcement of SLA, so they are of interest for Internet Service Providers. However, output given by traffic shapers may not be as accurate as desired. Therefore, it is important to assess the accuracy of the implementation of traffic shapers. In this paper, we evaluate two traffic shapers with regard to the performance of their throughput shaping. For this evaluation, traces were collected. The properties of the resulting throughput at the outlet of the shaper are compared to the properties of the throughput at the inlet in combination with the preset shaper parameters. In this sense, we also compare shapers installed on Advance Micro Devices (AMD) and Intel platforms, and we use different PDU sizes and load levels to test the influence of those parameters on the shaping. We are furthermore able to deduct internal shaper parameters such as packet rate and buffer size from our measurements, and we analyse the statistical properties of the packet departure process. The extensive measurement results in this paper allow for a detailed assessment of the question whether the shaper performance is up to mark for a desired timescale. In general, the performance of both shapers and hardware platforms can be considered satisfactory on the investigated time scales between 1 ms and 1 s, with a slight advantage for NetEm on AMD.

In this work, we present a systematic study of how the traffic of different transport protocols (UDP, TCP and ICMP) is treated, in three operational Swedish 3G networks. This is done by studying the impact that protocol and packet size have on the one-way-delay (OWD) across the networks. We do this using a special method that allows us to calculate the exact OWD, without having to face the usual clock synchronization problems that are normally associated with OWD calculations. From our results we see that all three protocols are treated similarly by all three operators, when we consider packet sizes that are smaller than 250 bytes and larger than 1100 bytes. We also show that larger packet sizes are given preferential treatment, with both smaller median OWD as well as a smaller standard deviation. It is also clear that, ICMP is given a better performance compared to TCP and UDP.

Packet delay variation plays an important role in network performance degradation and affects the user-perceptual quality, especially in case of real-time services such as video streaming, VoIP etc. Lightweight methods for detecting network performance issues are desirable as compared to task-intensive measurement and analysis. On this background, this paper discusses the applicability of the Coefficient of Throughput Variation (CoTV) to quickly and reliably detect bottleneck behavior between two arbitrary points in a network. The CoTV can be used as a Reduced Reference Metric. It is relatively simple to calculate, compare and interpret, yet powerful to provide control feedback when facing changes in network performance. In this paper, we demonstrate above mentioned properties of the CoTV through a formula relating it to the sample interval of the throughput and the variability of the delay. The latter is shown to be detectable on time scales that are significantly larger than that of the variability itself. This observation is a key enabler for reducing the load on the device that performs the analysis due to the possibility to use large sample intervals. We also describe how difference and ratio of CoTV at outlet and inlet can be used to identify network performance issues.

The satisfaction of end-users is important when evaluating services and products. Visualizing the network behavior in mobile streaming as well as modeling the correlation between Quality of Service (QoS) and Quality of Experience (QoE) is expected to improve user satisfaction of services on the Internet. Given that network and human factors are the elements that affect the final output of the measurements, it is important to take the Packet Delay Variation (PDV) into consideration. In this paper we observe the PDV during a series of real-life experiments on a 3rd Generation (3G) network while streaming videos. User Rating (UR) values from user input are recorded accordingly. With this aim, we implemented a QoE assessment tool that measures network metrics in kernel space, while simultaneously logging user ratings with regards to the perception of an ongoing realtime video on an Android phone. The primary goal is to identify a clear trend in the QoE modeling, so that we can assess the satisfaction of a user by deriving the QoE from measurable QoS metrics. We find that PDV degrades user perception. Observations show that, during the experiments, sudden breaks and restarts in the packet flow exist. Even though the use of Exponentially Weighted Moving Average (EWMA) assists in finding a model that shows the relationship between PDV and UR, on-off flushing is a major threat affecting this relationship. The results show that correlations with different quality can be reached with various modifications of the proposed matching model.

This demo shows a prototype of a user-centric mobility framework that provides handover for macro-mobility on handheld devices. The framework is designed for mobile-controlled handover and does not require modification of the Internet infrastructure. The end-users are so able to control the roaming process governed by user considerations in addition to business objectives. To achieve seamless mobility UDP tunneling is used as a basis for the handover process. Additionally, measurements are performed on the tunnels to acquire Quality of Service (QoS) metrics and hence serve for roaming decision making processes.

Cognitive Radio Networks (CRNs) are emerging as a viable solution for exploiting the available spectrum and solving overcrowding in the operating bands. Several dimensions in the process of identification, management and routing need to be handled to overcome the challenges associated with the management of CRNs. A very important topic in achieving an effective and flexible management solution is related to the data representation of the different CRN dimensions. Scalability, high tolerance to churn and low latencies in accessing the stored data are fundamental characteristics in order to keep up with the dynamic nature of a mobile environment. Accordingly, to achieve a robust data representation that can handle all these requirements in an efficient way we focus our attention on peer-to-peer (P2P) systems. These systems are well known for providing an efficient platform to develop scalable and robust applications. Hence, in this paper we investigate using geometric multi-dimensional data representation and addressing system based on basic CAN functionality. Results obtained in our experiments are reported, providing useful information to improve the resilience to churn of our system.

Mobile devices with ever increasing functionality are an important driving force behind innovative mobile applications that enrich our daily life. The ubiquitous availability of wireless data communication networks is an additional driving force. Their ability to support application data flows is one of the performance criteria for successful mobile application deployment. Nevertheless, the quantitative impact of this performance is unknown and practically infeasible to determine at real-time at the application-level due to mobile device resource constraints. We research practical methods for measurement-based application-level performance evaluation of data communication networks that support mobile application data flows. In this paper, we apply the lightweight Comparative Output-Input Analysis (COIA) method that estimates additional delay at observation time scale of interest (e.g. 1 s) induced on the application data flow. The additional delay is the amount of delay exceeding non-avoidable, minimal end-to-end data delay caused by data communication network propagation and transmission delays. We propose five COIA methods to estimate the additional delay. We validate their accuracy with measurements obtained from an m-health application, namely health telemonitoring provided by the MobiHealth system. Despite their simplicity, our methods prove to be accurate in relation to the observation time scale of interest, and robust under a variety of network conditions. The methods offer novel insights into the impact of data communication network performance on the application data flow delay behaviour.

In mobile multimedia networks, different traffic classes require various levels of quality of service (QoS). In this paper, we propose a cross-layer design to provide diverse QoS provisions for integrated realtime/non-realtime traffic by jointly taking into account the physical layer, data link layer, and application layer. Specifically, at the physical layer, adaptive modulation and coding (AMC) is employed to cope with the time-varying nature of fading channels whereas impairments due to queuing effects are considered at the data link layer. At the application layer, realtime and non-realtime traffic are classified into high and low priority classes, respectively, with strict priority levels. The cross-layer scheme is characterized as a discrete Markov modulated Poisson process. Solving the steady state probability of this Markov model readily enables us to investigate several important performance metrics such as packet drop rate due to buffer overflow and packet error rate due to transmission errors and network impairments. Our proposed cross-layer design is verified by comparing the analytical results with Monte-Carlo simulations. Furthermore, it has been shown that the proposed cross-layer design can flexibly guarantee QoS requirements for each traffic class.

The IP Multimedia Subsystem (IMS) is regarded as one of the most prominent enablers for successful service provisioning across different access network technologies and devices. While new paradigms, e.g. seamless communication, enter the IMS, existing solutions, e.g. for authentication, need to be re-defined, which is one of the major activities within the EUREKA!-funded Mobicome project, involving operators, manufacturers and academia. As there exist several candidate solutions for providing seamless authentication, there is a need for a set of criteria that helps to select the candidate that fulfils those criteria in a best-possible way. Given this background, this position paper proposes a framework of criteria for the evaluation of authentication schemes in IMS. The primary criteria are security, user-friendliness and simplicity. In-between these criteria, the secondary criteria can be found. These are awareness, usability and algorithms. Each criterion, both primary and secondary, is then also divided into one or several substantiating sub-criteria. The discussion of the criteria is followed by a description of the evaluation methodology, which comprises both qualitative and quantitative evaluations such as SWOT analysis, use of NIST and ISO guidelines, user rankings, and measurements of authentication times. The paper is concluded with an outlook on future work, including studies and experiments.

The complexity of today's computer networks requires highly trained professionals to verify the end-to-end connectivity between two communication partners. The concept of autonomic networks has been proposed in an effort to make such networks easier to manage. In this context, we present a plug-and-play mechanism to check the connectivity between two end points of the network. It will significantly decrease the amount of mundane work, which is usually involved in such a task. The main goals of our work are to autonomically set-up the measurement environment, to capture the current conditions between the two communication partners and to visualize the results in an easy-to-interpret way.

In the Internet era, there is a need to understand how traditional public switched telephone network (PSTN) services and the new Internet services can coexist. The best-effort based IP network will have to maintain the level of service that customers from the PSTN world expect. This study contributes to the traffic engineering of such a system by presenting an analytical model for integrating voice over IP (VoIP) traffic with multi-fractal data traffic. This model is based on the stochastic fluid flow model. Effects on delay and loss performance of adding VoIP traffic to a data link as well as capacity requirements to maintain a certain quality of service are discussed.