A QoE-Aware Resource Distribution Framework Incentivizing Context Sharing and Moderate Competition

We contend that context information of Internet clients can help to efficiently manage a variety of underlying resources for different Internet services and systems. We therefore propose a resource distribution framework that provides quality of experience (QoE) aware service differentiation, which means that starving clients are prioritized in resource allocation to enhance the corresponding end-user's QoE. The framework also actively motivates each Internet client to consistently provide its actual context information and to adopt moderate competition policies, given that all clients are selfish but rational in nature. We analyze the Internet client's behavior by formulating a non-cooperative game and prove that the framework guides all clients (game players) towards a unique Nash equilibrium. Furthermore, we prove that the distribution results computed by the framework maximize a social welfare function. Throughout this paper, we demonstrate the motivation, operation and performance of the framework by presenting a Web system example, which leverages on the advanced context information deduced by a context-aware system.

[1]  Weijia Jia,et al.  Context-Awareness in Mobile Web Services , 2004, ISPA.

[2]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[3]  Roy T. Fielding,et al.  Hypertext Transfer Protocol - HTTP/1.1 , 1997, RFC.

[4]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[5]  Dong In Kim,et al.  Game Theoretic Approaches for Multiple Access in Wireless Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[6]  Toru Ishida,et al.  Situated Web Service: Context-Aware Approach to High-Speed Web Service Communication , 2006, 2006 IEEE International Conference on Web Services (ICWS'06).

[7]  Jiangchuan Liu,et al.  NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing , 2009, IEEE INFOCOM 2009.

[8]  Multimedia Quality of Service and performance – Generic and user-related aspects Estimating end-to-end performance in IP networks for data applications , 2014 .

[9]  M. Weiser The Computer for the Twenty-First Century , 1991 .

[10]  NelsonRichard,et al.  Application flow control in YouTube video streams , 2011 .

[11]  Regina Dunlea,et al.  Simple Object Access Protocol (SOAP) , 2005 .

[12]  Schahram Dustdar,et al.  A survey on context-aware web service systems , 2009, Int. J. Web Inf. Syst..

[13]  David K. Y. Yau,et al.  Incentive and Service Differentiation in P2P Networks: A Game Theoretic Approach , 2006, IEEE/ACM Transactions on Networking.

[14]  Peter Brooks,et al.  User measures of quality of experience: why being objective and quantitative is important , 2010, IEEE Network.

[15]  Harry Chen,et al.  An Intelligent Broker Architecture for Pervasive Context-Aware Systems , 2004 .

[16]  Jean-Yves Le Boudec,et al.  Rate adaptation, Congestion Control and Fairness: A Tutorial , 2000 .

[17]  Roger Riggs,et al.  A Distributed Object Model for the Java System , 1996, Comput. Syst..

[18]  Mehul Motani,et al.  When Ambient Intelligence meets the Internet: User Module framework and its applications , 2012, Comput. Networks.

[19]  Michael Zink,et al.  Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications , 2009, Comput. Networks.

[20]  Tim Berners-Lee,et al.  Hypertext transfer protocol--http/i , 1993 .

[21]  Ting Li,et al.  Context-Aware Environment-Role-Based Access Control Model for Web Services , 2008, 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008).

[22]  Carl A. Waldspurger,et al.  Lottery and stride scheduling: flexible proportional-share resource management , 1995 .

[23]  Markus Fiedler,et al.  Quality of Experience from user and network perspectives , 2010, Ann. des Télécommunications.