Dynamic bandwidth allocation for multiple network connections

Wamser, Florian; Zinner, Thomas; Tran-Gia, Phuoc; Zhu, Jiang

doi:10.1145/2630088.2630095

Cited by 7 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modelling and performance analysis of application‐aware resource management

et al. 2015

Self Cite

View full text Add to dashboard Cite

Summary Application‐aware resource management is the approach to tailor access networks to have characteristics beneficial for the running applications and services. This is achieved through the monitoring and integration of key performance indicators from the application layer within the network resource management. The aim is to increase user‐perceived quality and network resource efficiency by traffic engineering with the help of these indicators. Using analytic and simulative approaches, this paper provides analysis methods for network operators to quantify the performance gains of alternative resource allocation algorithms that implement the application‐aware concept. Network operators can use the proposed methods to evaluate possible performance gain trade‐offs between investing in a pure capacity increase (over‐provisioning) and the realization of an application‐aware resource allocation. For this purpose, we model and analyse the application quality trade‐offs of four algorithms for application‐aware resource management at a single link in varying traffic situations. The algorithms are chosen with respect to different complexity and implementation level in order to cover the design space in a systematic way. The study of the algorithms focuses on the application‐layer performance for the most used applications today, namely, web browsing and video streaming with constant bit rate as well as hypertext transfer protocol progressive streaming with variable bit rate. Application quality trade‐offs are analysed in particular for a high‐resource utilization at a bottleneck link. The results confirm that application‐aware resource management outperforms best‐effort resource management in terms of quality of experience. Moreover, our study provides guidelines for the selection and configuration of the evaluated algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Modelling and performance analysis of application‐aware resource management

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

QoE Management for Future Networks

Schatz

Schwarzmann

Zinner

et al. 2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE-driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

show abstract

Quantitative comparison of application–network interaction: a case study of adaptive video streaming

2017

Self Cite

View full text Add to dashboard Cite

Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (1) monitoring of the key influence factors and QoE indicators, and (2) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g., video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes applicationnetwork interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.

show abstract

Dynamic bandwidth allocation for multiple network connections

Cited by 7 publications

References 17 publications

Modelling and performance analysis of application‐aware resource management

Modelling and performance analysis of application‐aware resource management

QoE Management for Future Networks

Quantitative comparison of application–network interaction: a case study of adaptive video streaming

Contact Info

Product

Resources

About