NeTS: Medium: Collaborative Research: An App-Centric Transport Architecture for the Internet
NeTS: Medium: Collaborative Research: An App-Centric Transport Architecture for the Internet
批准号:
1407470
负责人:
Hari Balakrishnan
金额:
$80.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-01 至 2020-08-31
中文摘要
传输控制协议及其套接字接口被当今大多数网络应用程序所使用-作为一个软件程序,它可能是世界上最流行的一个。三十多年过去了,这个节目现在显示出年龄的迹象。Web、移动的和嵌入式应用程序以意想不到的方式使用它(具有不同目标的不同长度的许多交互流);它不能正确地支持具有多个网络的移动的设备;它不允许应用快速地适应变化的网络条件;它不能在快速数据中心网络中很好地工作;以应用为中心的传输(ACT)就是为了解决这些问题而提出的。与今天的传输架构不同,ACT在做出传输决策时考虑了应用目标。 ACT使用计算机生成的传输控制程序,该程序采用工作量和目标的模型来自动合成协议。 这个程序(雷米)既可以作为一种实现协议的方法,也可以作为一种设计工具来回答有关拥塞控制的基本问题。ACT探索了对传输的集中控制和路由选择,并使用机器学习分类器在移动的设备上选择最佳网络。它旨在解决端点传输和网络中间盒之间的争斗,通过查看中间盒插入作为多路径传输的推广。项目参与者正在与Facebook和谷歌就其中一些想法进行合作。该教育计划引入了协议设计竞赛,学生可以在跟踪数据上运行和测量他们的协议,并在排行榜上比较他们的协议。
英文摘要
The Transmission Control Protocol and its socket interface are used by most network applications today---viewed as a software program, it is likely the most popular one in the world. Over thirty years old, this program is now showing signs of age. Web, mobile, and embedded applications use it in unanticipated ways (many interacting flows of different lengths with different objectives); it does not properly support mobile devices with multiple networks; it does not allow an application to adapt quickly to changing network conditions; it does not work well in fast datacenter networks; and it has a fruitless, adversarial relationship with in-network "middleboxes".App-Centric Transport (ACT) aims to solve these problems. Unlike today's transport architecture, ACT takes application objectives into account in making its transmission decisions. ACT uses a computer-generated transmission control program, which takes models of the workload and objectives to automatically synthesize protocols. This program (Remy) serves both as a way to implement protocols and as a design tool to answer fundamental questions about congestion control. ACT explores centralized control over transmission and path selection for datacenters, and uses machine learning classifiers to select the best network on mobile devices. It aims to resolve tussles between endpoint transport and in-network middleboxes by viewing middlebox interposition as a generalization of multi-path transport. Project participants are collaborating with Facebook and Google on some of these ideas. The education plan introduces protocol design contests where students can run and measure their protocols over trace data and compare their protocols on a leaderboard.
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会议论文
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