http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
application to content delivery network architecture
1 online resource (xix, 156 pages) : illustrations.
Based on a convergence of network technologies, the Next Generation Network (NGN) is being deployed to carry high quality video and voice data. In fact, the convergence of network technologies has been driven by the converging needs of end-users. The perceived end-to-end quality is one of the main goals required by users that must be guaranteed by the network operators and the Internet Service Providers, through manufacturer equipment. This is referred to as the notion of Quality of Experience (QoE) and is becoming commonly used to represent user perception. The QoE is not a technical met.
Cover; Title page; Contents; LIST OF FIGURES; PREFACE; INTRODUCTION; CHAPTER 1. NETWORK CONTROL BASED ON SMART COMMUNICATION PARADIGM; 1.1. Motivation; 1.2. General framework; 1.3. Main innovations; 1.3.1. User perception metrics and affective computing; 1.3.2. Knowledge dissemination; 1.3.3. Bio-inspired approaches and control theory; 1.4. Conclusion; CHAPTER 2. QUALITY OF EXPERIENCE; 2.1. Motivation; 2.2. QoE concept; 2.3. Importance of QoE; 2.4. QoE metrics; 2.5. QoE measurement methods; 2.6. QoS/QoE relationship; 2.7. Impact of networking on QoE.
2.7.1. Layered classification of impacts on QoE2.7.2. Impact of user mobility on QoE; 2.7.3. Impact of network resource utilization and management on QoE; 2.7.4. Impact of billing and pricing; 2.8. Conclusion; CHAPTER 3. CONTENT DISTRIBUTION NETWORK; 3.1. Motivation; 3.2. Routing layer; 3.2.1. Routing in telecommunication network; 3.2.2. Classical routing algorithms; 3.2.3. QoS-based routing; 3.3. Meta-routing layer; 3.3.1. Server placement; 3.3.2. Cache organization; 3.3.3. Server selection; 3.4. Conclusion; CHAPTER 4. USER-DRIVEN ROUTING ALGORITHM APPLICATION FOR CDN FLOW; 4.1. Introduction.
4.2. Reinforcement learning and Q-routing4.2.1. Mathematical model of reinforcement learning; 4.2.2. Value functions; 4.3. Q-learning; 4.4. Q-routing; 4.5. Related works and motivation; 4.6. QQAR routing algorithm; 4.6.1. Formal parametric model; 4.6.2. QQAR algorithm; 4.6.3. Learning process; 4.6.4. Simple use case-based example of QQAR; 4.6.5. Selection process; 4.7. Experimental results; 4.7.1. Simulation setup; 4.7.2. Experimental setup; 4.7.3. Average MOS score; 4.7.4. Convergence time; 4.7.5. Capacity of convergence and fault tolerance; 4.7.6. Control overheads.
4.7.7. Packet delivery ratio4.8. Conclusion; CHAPTER 5. USER-DRIVEN SERVER SELECTION ALGORITHM FOR CDN ARCHITECTURE; 5.1. Introduction; 5.2. Multi-armed bandit formalization; 5.2.1. MAB paradigm; 5.2.2. Applications of MAB; 5.2.3. Algorithms for MAB; 5.3. Server selection schemes; 5.4. Our proposal for QoE-based server selection method; 5.4.1. Proposed server selection scheme; 5.4.2. Proposed UCB1-based server selection algorithm; 5.5. Experimental results; 5.5.1. Simulation results; 5.5.2. Real platform results; 5.6. Acknowledgment; 5.7. Conclusion; CONCLUSION; BIBLIOGRAPHY; INDEX.
Abdelhamid Mellouk, Said Hoceini, Hai Anh Tran.
Includes bibliographical references and index.
London : ISTE ; Hoboken, N.J. : Wiley, 2013
Focus nanoscience and nanotechnology series
F4FB388A-C3F4-472C-9A5A-B0B5F3A3652B OverDrive, Inc.