An autonomous QoE-driven network management framework

https://doi.org/10.1016/j.jvcir.2013.11.010Get rights and content

Highlights

  • A QoE management framework for packet-switched networks is presented.

  • The framework is application and technology-independent.

  • A prototype is implemented which demonstrates the use of the framework.

  • The prototype manages network access point traffic for OTT multimedia services.

  • The prototype improves perceived quality and optimizes resource usage.

Abstract

Recently, network researchers have taken a great interest in quality of experience (QoE) and in the new aspects it brings in the study of the link between network conditions and user satisfaction. Also, the realization that the information of users’ satisfaction can be directly applied in the network management in a real-time manner has resulted in a fair amount of publications. Although the systems and frameworks presented in these publications tackle the subject of QoE-driven management quite successfully, they often concentrate on certain applications or technologies. We present a generic QoE management framework, which is applicable to a broad range of systems. We also demonstrate an instantiation of this framework as a network access point management system for RTP-based video. This system is not only able to positively affect the perceived quality of the multimedia application considered, but also to reduce over-prioritization and optimize resource usage.

Introduction

Multimedia services, and in particular video, make up a large and ever-increasing portion of the total Internet traffic. The popularity of the so-called “Over-the-Top” (OTT) services, such as YouTube, Netflix, Hulu, and other Web-based video services has exploded and will continue to do so (e.g. with the adoption of WebRTC [1] for real-time browser-based communications), as has their importance to users and business. This fact, coupled with the availability of fast and cheap mobile connectivity and mobile devices capable of displaying high-definition content, poses a serious challenge to mobile operators, as users become accustomed to more resource-demanding services and demand better quality. In contrast to operator-run media services (such as IPTV, mobile TV, or IMS-based ones), where the operator has control over the whole chain of transmission, OTT services come from outside the operator’s network, and the operators have very little, if any, control over them (some content providers such as Netflix work with operators to provide caching servers within the operator’s network, but this is not generally the case with all content providers).

The operator’s challenges are thus many; firstly, it has to deal with the increasing demand on their infrastructure, notably so over the last hop (base stations and WiFi hotspots, for example). Secondly, OTT video is most commonly delivered over HTTP, which makes it hard to separate from other web traffic. Finally, from the operator’s point of view, OTT services are hard to monetize (the content providers get the revenue and the operator just sees an increased use of resources) and at the same time, if the quality of these services is not good enough for the users, the operator will face higher user churn.

In this paper we address these challenges by providing traffic control mechanisms based on a combination of Quality of Experience (QoE)1 estimations, and subscriber and application-based traffic differentiation. We present a framework to instrument QoE-driven network management mechanisms, and in the context of this framework, we implement a concrete prototype for QoE-driven control. We expand upon our previous work [2] by incorporating network performance models, allowing the proposed approach to always make the right decision by predicting the possible outcomes, instead of just reacting to a drop in quality and hoping that the reaction will result in a positive change. The goal of the proposed work is to allow operators to properly address the needs of their users (in terms of QoE), while introducing subscriber differentiation as a means of increasing revenues and simplifying resource allocation (i.e. customers who pay more are prioritary). The proposed approach is able to (a) identify the relevant media flows, (b) estimate their current QoE, (c) select the appropriate priority for the flows based on their application type, subscriber class, current QoE for it and other media flows, and expected QoE after the control mechanism kicks in (based on network performance models) and (d) perform access control on new flows based on the current quality for existing flows, and the incoming flow’s application and subscriber class.

The rest of the paper is organized as follows. Section 2 provides an overview of related works. Section 3 describes the proposed traffic management system. Sections 4 and 5 present a prototype implementation of the system and a testbed where the prototype was tested in. Section 6 presents its performance results. Finally, Section 7 presents our conclusions and future research lines associated with this work.

Section snippets

Related work

Although the majority of the research papers concerning QoE are related to QoE assessment and monitoring [3], [4], [5], [6], QoE management has recently gained more attention from the research community. Thus, different QoE management systems and frameworks for different network technologies and applications may be found in the literature.

In [2] we proposed a simpler version of the approach proposed herein, whereby control decisions were made based on current quality estimates. The main

QoS/QoE Management system

The QoS/QoE management system presented in this paper is a part of a complete customer experience management system (CEMS) framework, which it is illustrated in Fig. 1. The CEMS framework contains three layers: data acquisition, monitoring, and control. In order to fully define the QoS/QoE management system, the presence of the data acquisition and monitoring levels is essential. However, they are only briefly described in this paper, as the focus is on the control layer.

All raw data collection

Prototype implementation

Following the conceptual framework presented in Section 3, we developed an autonomous network access point management software, which is able to monitor and manage traffic traversing from the Internet to end users’ devices. The system can be placed at the edges of a 3G or 4G network before the radio channel, but because the system operates on the network layer and above, the underlying technology is not restricted in other way than requiring it to have an IP layer. This solution demonstrates

Evaluation setup

The testbed used in this demonstration is illustrated in Fig. 6. We used two client computers to emulate up to 5 (using one end device for each client would have the same result, as the management software differentiates flows and subscription types, not end devices). The access point software was implemented in a laptop (shown in the center of the figure). An external computer was used as a D-ITG and VLC media player server, which were used to transmit bulk traffic and video streams,

Results

To show the impact of the developed QoE-driven access point management system, we present some testbed results in this section. First, we present two simple test cases where traffic and client differentiation are demonstrated. Then we introduce a performance test which measures the reaction time of the system. In the last two tests we show how the admission control prevents traffic from over-burdening the AP, and how the system is able to balance the load of different traffic types in a more

Conclusions

In this paper, we presented a generic QoS/QoE framework for enabling quality control in packet-switched networks. We focused especially in the management component of this framework to show how data collected from a network can be refined into knowledge of quality perceived by users, and how to take corrective measures when necessary. We also showcased this framework by introducing and demonstrating a network access point management software, built around the proposed framework’s architecture.

Acknowledgments

The research behind this paper was conducted within the IPNQSIS (IP Network Monitoring for Quality of Service Intelligent Support), a Celtic Call 7 project and QuEEN, a Celtic Call 8 project. The authors would like to thank Tekes, the Finnish Funding Agency for Technology and Innovation, for financially supporting this research.

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