110 research outputs found
Multicast resource management for next generation mobile communication systems
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Design and performance analysis of a super-scalar video-on-demand system.
Lee Chung Hing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 61-63).Abstracts in English and Chinese.Acknowledgements --- p.iiAbstract --- p.iiiList of Figures --- p.viiChapter 1. --- Introduction --- p.1Chapter 1.1 --- Contributions of This Thesis --- p.3Chapter 1.2 --- Organizations of This Thesis --- p.3Chapter 1.3 --- Publication --- p.4Chapter 2. --- Overview of VoD Systems --- p.5Chapter 2.1 --- True VoD --- p.6Chapter 2.2 --- Near VoD --- p.7Chapter 2.3 --- Related Works --- p.9Chapter 2.3.1 --- Batching --- p.9Chapter 2.3.2 --- Patching --- p.11Chapter 2.3.3 --- Mcache --- p.11Chapter 2.3.4 --- Unified VoD --- p.12Chapter 2.4 --- Discussions --- p.15Chapter 3. --- Super-Scalar Architecture --- p.17Chapter 3.1 --- Transmission Scheduling --- p.20Chapter 3.2 --- Admission Control --- p.21Chapter 3.3 --- Channel Merging --- p.26Chapter 3.4 --- Interactive Control --- p.29Chapter 4. --- Performance Modeling --- p.31Chapter 4.1 --- Waiting Time for Statically-Admitted Clients --- p.32Chapter 4.2 --- Waiting Time for Dynamically-Admitted Clients --- p.33Chapter 4.3 --- Admission Threshold --- p.38Chapter 4.4 --- Channel Partitioning --- p.39Chapter 5. --- Performance Evaluation --- p.40Chapter 5.1 --- Model Validation --- p.40Chapter 5.2 --- Channel Partitioning --- p.42Chapter 5.3 --- Latency Comparisons --- p.44Chapter 5.4 --- Channel Requirement --- p.46Chapter 5.5 --- Performance at Light Loads --- p.47Chapter 5.6 --- Multiplexing Gain --- p.49Chapter 6. --- Implementation and Benchmarking --- p.51Chapter 6.1 --- Implementation Description --- p.51Chapter 6.2 --- Benchmarking --- p.53Chapter 6.2.1 --- Benchmarking Setup --- p.53Chapter 6.2.2 --- Benchmarking Result --- p.55Chapter 7. --- Conclusion --- p.56Appendix --- p.57Bibliography --- p.6
Efficient Techniques for Management and Delivery of Video Data
The rapid advances in electronic imaging, storage, data compression telecommunications, and networking technology have resulted in a vast creation and use of digital videos in many important applications such as digital libraries, distance learning, public information systems, electronic commerce, movie on demand, etc. This brings about the need for management as well as delivery of video data. Organizing and managing video data, however, is much more complex than managing conventional text data due to their semantically rich and unstructured contents. Also, the enormous size of video files requires high communication bandwidth for data delivery. In this dissertation, I present the following techniques for video data management and delivery. Decomposing video into meaningful pieces (i.e., shots) is a very fundamental step to handling the complicated contents of video data. Content-based video parsing techniques are presented and analyzed. In order to reduce the computation cost substantially, a non-sequential approach to shot boundary detection is investigated. Efficient browsing and indexing of video data are essential for video data management. Non-linear browsing and cost-effective indexing schemes for video data based on their contents are described and evaluated. In order to satisfy various user requests, delivering long videos through the limited capacity of bandwidth is challenging work. To reduce the demand on this bandwidth, a hybrid of two effective approaches, periodic broadcast and scheduled multicast, is discussed and simulated.
The current techniques related to the above works are discussed thoroughly to explain their advantages and disadvantages, and to make the new improved schemes. The substantial amount of experiments and simulations as well as the concepts are provided to compare the introduced techniques with the other existing ones. The results indicate that they outperform recent techniques by a significant margin. I conclude the dissertation with a discussing of future research directions
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The Design and Implementation of Low-Latency Prediction Serving Systems
Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints
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