流媒体 (stream ing m edia)是一种新兴的网络传输技术 ,在互联网上实时顺序地传输和播放视 /音频等多媒体内容的连续时基数据流 ,流媒体技术包括流媒体数据采集、视 /音频编解码、存储、传输、播放等领域。本文分别从流媒体的技术原理...流媒体 (stream ing m edia)是一种新兴的网络传输技术 ,在互联网上实时顺序地传输和播放视 /音频等多媒体内容的连续时基数据流 ,流媒体技术包括流媒体数据采集、视 /音频编解码、存储、传输、播放等领域。本文分别从流媒体的技术原理、传输方式、传输协议等方面对流媒体做出阐述。展开更多
Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but...Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but newer generations of cloud applications, such as web search, recommendations, and social networking, typically employ a tree-based Partition-Aggregate structure, which may incur bursts of traffic. As a result, flows in these applications have stringent latency requirements, i.e., flow deadlines need to be met in order to achieve a satisfactory user experience. To meet these flow deadlines, research efforts in the recent literature have attempted to redesign flow and congestion control protocols that are specific to datacenter networks. In this paper, we focus on the new array of deadline-sensitive flow control protocols, thoroughly investigate their underlying design principles, analyze the evolution of their designs, and evaluate the tradeoffs involved in their design choices.展开更多
This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission...This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission only for packets of the base layer to disburden the congested network. The bandwidth estimation allows for adjusting the transmission rate quickly to the current available bandwidth of the network. Compared to binomial congestion control, the proposed system allows for shorter start-up times and data rate adaptation. The paper describes the components of this streaming system and the results of experiments showing that the proposed approach works effectively for streaming video.展开更多
文摘Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but newer generations of cloud applications, such as web search, recommendations, and social networking, typically employ a tree-based Partition-Aggregate structure, which may incur bursts of traffic. As a result, flows in these applications have stringent latency requirements, i.e., flow deadlines need to be met in order to achieve a satisfactory user experience. To meet these flow deadlines, research efforts in the recent literature have attempted to redesign flow and congestion control protocols that are specific to datacenter networks. In this paper, we focus on the new array of deadline-sensitive flow control protocols, thoroughly investigate their underlying design principles, analyze the evolution of their designs, and evaluate the tradeoffs involved in their design choices.
文摘This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission only for packets of the base layer to disburden the congested network. The bandwidth estimation allows for adjusting the transmission rate quickly to the current available bandwidth of the network. Compared to binomial congestion control, the proposed system allows for shorter start-up times and data rate adaptation. The paper describes the components of this streaming system and the results of experiments showing that the proposed approach works effectively for streaming video.
