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Collaborative Research (NSF-CNPq): Application Level Adaptation and Control for Retrieval and Delivery of Continuous Media over the Internet

Collaborative Research (NSF-CNPq): Application Level Adaptation and Control for Retrieval and Delivery of Continuous Media over the Internet
协作研究 (NSF-CNPq):通过互联网检索和交付连续媒体的应用程序级适应和控制
批准号:
0233979
负责人:
Leana Golubchik
金额:
$22.18万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2002
资助国家:
美国
项目状态:
已结题
起止时间:
2002-07-01 至 2004-09-30

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中文摘要
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英文摘要
Future networked multimedia information systems will carry a wide variety of applications including digital libraries, video, audio and image services, distance learning and collaboration, networked virtual environments, and entertainment. The main characteristics of multimedia applications that lead to difficulties in end-to-end systems design are that they have very large bandwidth and storage requirements, with vastly different performance and reliability requirements, often coupled with real-time constraints. Along with these characteristics, the highly interreliability requirements, often coupled with real-time constraints. Along with these characteristics, the highly interactive nature of a variety of multimedia applications, resulting in fairly unpredictable workloads, makes the design and evaluation of networked multimedia information systems an exceptionally challenging problem. In this proposal, we outline research on three aspects of this problem: (1) the design and evaluation of server resource allocation algorithms for CM servers in order to retrieve information efficiently and according to the QoS demanded by the application; (2) the development of performance evaluation techniques for evaluating new server designs. (3) the participants of this project bring expertise from a wide variety of areas: databases, distance learning, multi-media, networking, and performance evaluation to bear on problems in these areas. Another important feature of this proposal is that the participants have available four prototypes of state of the art multimedia systems: the Virtual World Data Server (VWDS) developed at UCLA: the prototype of a Video-on-Demand server developed in Brazil as part cooperative research project among Brazilian institutions; and the Multimedia Asynchronous Networked Individualized Courseware (MANIC) and the Internet Multimedia Proxy (IMP) both developed at UMass. These applications will be used to motivate the development of new algorithms for retrieving information and for maintaining the desired quality of service after sending the data over a wide area network. They will also form the basis of the many experimental and analytical studies that will be performed to evaluate these new algorithms. To aid in this evaluation, our group also developed three performance evaluation and modeling tools: a state-of-the-art tool for constructing performance and reliability models (Tangram-II) developed in Brazil jointly with UCLA; a symbolic model checking tool (VERUS): and a tool which facilitates design, development, and subsequent performance evaluation of designs of multimedia storage hierarchies (ViPEr-HiSS) under development at UMD. Thus, the environment of our labs as well the long distance among them will provide a unique testbed for this type of an evaluation due to the drastically different connectivities available to our applications, from gigabit low utilized links to intercontinental congested links. The proposed research represents a fundamentally important step in the design and performance evaluation of next generation information servers and the networked applications that will operate on top of them. As a result of our research we expect to have a better understanding of how storage server resource management policies, channel allocation policies, and network adaptation policies interact to satisfy the required QoS of CM applications, despite the fairly unpredictable network delays.
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CSR: Small: Deconstructing Distributed Deep Learning
  • 批准号:
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  • 项目类别:
    Standard Grant
  • 资助金额:
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  • 财政年份:
    2018
  • 负责人:
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  • 依托单位:
RI: Medium: Collaborative Research: Learning to Su
  • 批准号:
    1833137
  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2017
  • 负责人:
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DC:Small: "Synergizing statistical machine learning and stochastic system modeling with application to real systems".
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    0917340
  • 项目类别:
    Standard Grant
  • 资助金额:
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    2009
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2006
  • 负责人:
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  • 批准年份:
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