Phuoc Tran-Gia

A generic quantitative relationship between quality of experience and quality of service

Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.

A Survey on Quality of Experience of HTTP Adaptive Streaming

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

Quantification of YouTube QoE via Crowdsourcing

This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.

Modeling of customer retrial phenomenon in cellular mobile networks

In the planning of modern cellular mobile communication systems, the impact of customer behavior has to be carefully taken into account. Two models dealing with the call retrial phenomenon are presented. The first model considers a base station with a finite customer population and repeated attempts. A Markov chain modeling is proposed, and an efficient recursive solution of the state probabilities is presented. The second model focuses on the use of the guard channel concept to prioritize the handover traffic. Again, the retrial phenomenon plays an important role. The influence of the repeated attempt effect on the quality of service experienced by the mobile customers is discussed by means of numerical results.

Heuristic Approaches to the Controller Placement Problem in Large Scale SDN Networks

Software Defined Networking (SDN) marks a paradigm shift towards an externalized and logically centralized network control plane. A particularly important task in SDN architectures is that of controller placement, i.e., the positioning of a limited number of resources within a network to meet various requirements. These requirements range from latency constraints to failure tolerance and load balancing. In most scenarios, at least some of these objectives are competing, thus no single best placement is available and decision makers need to find a balanced trade-off. This work presents POCO, a framework for Pareto-based Optimal COntroller placement that provides operators with Pareto optimal placements with respect to different performance metrics. In its default configuration, POCO performs an exhaustive evaluation of all possible placements. While this is practically feasible for small and medium sized networks, realistic time and resource constraints call for an alternative in the context of large scale networks or dynamic networks whose properties change over time. For these scenarios, the POCO toolset is extended by a heuristic approach that is less accurate, but yields faster computation times. An evaluation of this heuristic is performed on a collection of real world network topologies from the Internet Topology Zoo. Utilizing a measure for quantifying the error introduced by the heuristic approach allows an analysis of the resulting trade-off between time and accuracy. Additionally, the proposed methods can be extended to solve similar virtual functions placement problems which appear in the context of Network Functions Virtualization (NFV).

Spatial traffic estimation and characterization for mobile communication network design

This paper presents a new method for the estimation and characterization of the expected teletraffic in mobile communication networks. The method considers the teletraffic from the network viewpoint. The traffic estimation is based on the geographic traffic model, which obeys the geographical and demographical factors for the demand for mobile communication services. For the spatial teletraffic characterization, a novel representation technique is introduced which uses the notion of discrete demand nodes. We show how the information in geographical information systems can be used to estimate the teletraffic demand in an early phase of the network design process. Additionally, we outline how the discrete demand node representation facilitates the application of demand-based automatic mobile network design algorithms.

Interfaces, attributes, and use cases: A compass for SDN

The term Software Defined Networking (SDN) is prevalent in todays discussion about future communication networks. As with any new term or paradigm, however, no consistent definition regarding this technology has formed. The fragmented view on SDN results in legacy products being passed off by equipment vendors as SDN, academics mixing up the attributes of SDN with those of network virtualization, and users not fully understanding the benefits. Therefore, establishing SDN as a widely adopted technology beyond laboratories and insular deployments requires a compass to navigate the multitude of ideas and concepts that make up SDN today. The contribution of this article represents an important step toward such an instrument. It gives a thorough definition of SDN and its interfaces as well as a list of its key attributes. Furthermore, a mapping of interfaces and attributes to SDN use cases is provided, highlighting the relevance of the interfaces and attributes for each scenario. This compass gives guidance to a potential adopter of SDN on whether SDN is in fact the right technology for a specific use case.

SDN-Based Application-Aware Networking on the Example of YouTube Video Streaming

Application-Aware Networking is a promising approach to provide good application quality to users in scenarios with limited network resources, like todays access networks. With SDN, a particularly interesting method to enable flowbased traffic management in networks has become available. In this work we take a look at how a specific application, i.e., YouTube Streaming, can benefit from such an SDN-based Application-Aware Network. We implement and investigate an approach based on Deep Packet Inspection (DPI) and one based on direct information input from the application in an OpenFlow testbed in order to show, how these different types of application information can be exploited to enhance the Quality of Experience (QoE). Furthermore, we determine the overhead caused by each of the presented approaches.

Pareto-optimal resilient controller placement in SDN-based core networks

Recently, Software Defined Networking (SDN) has gained a lot of attention, even for the use in core communication networks. When deploying SDN in large core networks, the number and location of controllers must be carefully planned. A first study on this topic by Heller et al. [3] was followed by more detailed publications that included resilience or dynamic controller provisioning. In our previous work [2], we provide an overview over related work and include different resilience issues in the controller placement process. We argued that it is not sufficient to look only at node-to-controller latencies but a controller placement should also fulfill certain resilience constraints especially for the control plane.

Source Traffic Modeling of Wireless Applications

Summary Data applications are expected to play an increasingly important role in the next generation mobile communication services. To plan these networks, detailed models of data users with diverse applications are required. The purpose of this study is to provide practically usable traffic models for data users in wireless networks, e.g. GPRS. We start with a discussion of well-known models in the literature, where the focus is on their applicability in mobile networks. A practical model of mobile HTTP users, based on an extrapolation of WWW users in current wire-line networks, is presented; this model is expected to be accurate enough to be used in simulation studies for network planning purposes. Finally, models for other emerging data applications are also taken into account.