NeTS: Medium: Streaming Data Analytics over Programmable Datacenter Networks

NeTS：媒介：通过可编程数据中心网络进行流数据分析

基本信息

批准号：
1801884
负责人：
Ang Chen
金额：
$ 120万
依托单位：
William Marsh Rice University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-15 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1801884&HistoricalAwards=false
关键词：
NeTS Medium Streaming Data Analytics

项目摘要

Today's datacenters play many important roles, not the least of which is to support big data analytics. Streaming data, in particular, is a prevalent workload that comes from web clickstreams and applications like financial transactions. Streaming data analytics presents unique challenges, as the data arrives continuously at very high speeds, which makes deriving real-time insight about it difficult. However, datacenter networks are becoming more programmable, meaning that more of the common components that a data stream might pass through now have the flexibility to do real-time processing. In addition, new technologies are enabling the datacenter network topology to be dynamically configured to connect the components and servers needed to process a certain stream more efficiently. This project seeks to build a framework and tools that exploit this emerging programmability to enable real time data processing. It has the potential to drastically improve the scalability, performance and energy efficiency of data analytics, and to deliver critical insight and response in real time. This project will develop a new data analytics framework designed to transform the way of performing streaming data analytics by leveraging datacenter programmability. The set of programmable elements has expanded to include not only servers, but network interface components, field-programmable gate arrays, application-specific integrated circuits, and network topology. The vision of the project is to jointly optimize all components to achieve a "sweetspot" in the design space for each application. The researchers aim to develop the scientific foundations and practical techniques to realize this vision through the following activities: identifying key abstractions that programmable datacenter networks can provide to support application-level data analytics, designing a practical streaming data analytics framework to leverage these abstractions, and developing scheduling algorithms to multiplex the programmable resources across different applications. Finally, the investigators will explore efficient and reusable implementations of the framework, which will be applied to real-world workloads as case studies. The project includes collaboration with industry practitioners to enable research ideas and system prototypes to be smoothly transitioned into practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当今的数据中心发挥着许多重要作用，其中最重要的是支持大数据分析。流数据尤其是来自网络点击流和金融交易等应用程序的普遍工作负载。流数据分析带来了独特的挑战，因为数据以非常高的速度持续到达，这使得获得实时洞察变得困难。然而，数据中心网络变得越来越可编程，这意味着数据流可能经过的更多通用组件现在可以灵活地进行实时处理。此外，新技术使数据中心网络拓扑能够动态配置，以连接更有效地处理特定流所需的组件和服务器。该项目旨在构建一个框架和工具，利用这种新兴的可编程性来实现实时数据处理。它有可能大幅提高数据分析的可扩展性、性能和能源效率，并实时提供关键的洞察和响应。该项目将开发一个新的数据分析框架，旨在通过利用数据中心可编程性来改变执行流数据分析的方式。可编程元件集已扩展到不仅包括服务器，还包括网络接口组件、现场可编程门阵列、专用集成电路和网络拓扑。该项目的愿景是共同优化所有组件，以在每个应用的设计空间中实现“最佳点”。研究人员的目标是通过以下活动开发科学基础和实用技术来实现这一愿景：识别可编程数据中心网络可以提供的支持应用程序级数据分析的关键抽象，设计实用的流数据分析框架来利用这些抽象，以及开发调度算法以在不同应用程序之间复用可编程资源。最后，研究人员将探索该框架的高效且可重用的实现，并将其作为案例研究应用于现实世界的工作负载。该项目包括与行业从业者的合作，使研究想法和系统原型顺利转化为实践。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Adaptive Weighted Traffic Splitting in Programmable Data Planes

可编程数据平面中的自适应加权流量分割

DOI：
10.1145/3373360.3380841
发表时间：
2020-01
期刊：
ACM SOSR
影响因子：
0
作者：
Hsu, Kuo;Tammana, Praveen;Beckett, Ryan;Chen, Ang;Rexford, Jennifer;Walker, David
通讯作者：
Walker, David

Check before You Change: Preventing Correlated Failures in Service Updates

更改前检查：防止服务更新中出现相关故障

DOI：
发表时间：
2020-01
期刊：
USENIX NSDI
影响因子：
0
作者：
Zhai, Ennan;Chen, Ang;Piskac, Ruzica;Balakrishnan, Mahesh;Tian, Bingchuan;Song, Bo;Zhang, Haoliang
通讯作者：
Zhang, Haoliang

A Feasibility Study on Time-aware Monitoring with Commodity Switches

商品开关时间感知监控的可行性研究

DOI：
10.1145/3405669.3405821
发表时间：
2020-01
期刊：
ACM SIGCOMM 2020 Workshop on Secure Programmable Net- work Infrastructure (SPIN 2020
影响因子：
0
作者：
Qiu, Yiming;Hsu, Kuo;Xing, Jiarong;Chen, Ang
通讯作者：
Chen, Ang

A Feasibility Study on Time-aware Monitoring with Commodity Switches

商品开关时间感知监控的可行性研究

DOI：
发表时间：
2020-01
期刊：
ACM SIGCOMM 2020 Workshop on Secure Programmable Network Infrastructure (SPIN 2020
影响因子：
0
作者：
Xing, Jiarong;Chen, Ang;Ng, T.S. Eugene
通讯作者：
Ng, T.S. Eugene

Green, Yellow, Yield: End-Host Traffic Scheduling for Distributed Deep Learning with TensorLights

绿色、黄色、屈服：使用 TensorLights 进行分布式深度学习的终端主机流量调度