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SPX: Collaborative Research: NG4S: A Next-generation Geo-distributed Scalable Stateful Stream Processing System

SPX: Collaborative Research: NG4S: A Next-generation Geo-distributed Scalable Stateful Stream Processing System
SPX:合作研究:NG4S:下一代地理分布式可扩展状态流处理系统
批准号:
1919181
负责人:
Abdullah Muzahid
金额:
$26.19万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2023-09-30

项目摘要

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中文摘要
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英文摘要
Our society increasingly relies on applications that process streaming data across geo-distributed sites, such as making business decisions from marketing data, identifying spam campaigns in social network streams, and analyzing genome datasets in different labs and countries to track the sources of potential epidemics. State-of-art solutions for these needs are centered around stateless stream processing. This project advances stream processing to enable next-generation streaming applications to store and update state along with computation, therefore processing live data streams in a timely fashion from massive and geo-distributed datasets. Existing systems are mainly designed for stateless stream processing in intra-datacenter settings and do not scale well for running stream applications that contain large distributed states. This project breaks the traditional abstractions of a centralized architecture and hashtable-based stateless operators, redefining them with a new decentralized architecture and new memory-efficient stateful operators, which enables novel approaches to improve overall system performance and scalability. This project builds a next-generation geo-distributed scalable stateful stream processing system that will significantly improve the scalability of stream processing systems. This work includes three primary research directions. (1) At the architecture level, a new decentralized 'many masters/many workers' architecture will be proposed, which provides each master with maximum independence. (2) At the operator level, a new in-memory data structure will be designed and implemented to store application state and minimize the memory overhead so as to handle 'big data' requirements. (3) A new shard-based parallel recovery mechanism will be proposed to handle failures and stragglers in a scalable way. All three parts of the project will be prototyped and implemented on a widely adopted stream processing system (Apache Storm).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.
期刊论文(3)
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科研奖励(0)
会议论文
DOI: 10.1109/ipdps47924.2020.00116
发表时间: 2020-05
期刊: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子: --
作者: [Pinchao Liu;Hailu Xu;D. D. Silva-D.;Qingyang Wang;Sarker Tanzir Ahmed;Liting Hu]
通讯作者: Pinchao Liu;Hailu Xu;D. D. Silva-D.;Qingyang Wang;Sarker Tanzir Ahmed;Liting Hu
DOI: 10.1145/3423211.3425681
发表时间: 2020-12
期刊: Proceedings of the 21st International Middleware Conference
影响因子: --
作者: [Hailu Xu;Pinchao Liu;Susana Cruz-Diaz;D. D. Silva-D.;Liting Hu]
通讯作者: Hailu Xu;Pinchao Liu;Susana Cruz-Diaz;D. D. Silva-D.;Liting Hu
DART: A Scalable and Adaptive Edge Stream Processing Engine
DART:可扩展的自适应边缘流处理引擎
DOI: --
发表时间: 2021
期刊: 2021 USENIX Annual Technical Conference (USENIX ATC 21
影响因子: --
作者: [Liu, Pinchao, Silva, Dilma Da, Hu, Liting.]
通讯作者: Hu, Liting.
SHF: Small: Software and Hardware Support for Robust Deep Learning
CAREER: A Dynamic Program Monitoring Framework Using Neural Network Hardware
CAREER: A Dynamic Program Monitoring Framework Using Neural Network Hardware
  • 批准号:
    1652655
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $44.99万
  • 财政年份:
    2017
  • 负责人:
    Abdullah Muzahid
  • 依托单位:
SHF: Small: Novel Techniques for Handling Memory Model Bugs
  • 批准号:
    1319983
  • 项目类别:
    Standard Grant
  • 资助金额:
    $24.93万
  • 财政年份:
    2013
  • 负责人:
    Abdullah Muzahid
  • 依托单位:
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