Frameworks: Collaborative Research: ChronoLog: A High-Performance Storage Infrastructure for Activity and Log Workloads
框架:协作研究:ChronoLog:用于活动和日志工作负载的高性能存储基础架构
基本信息
- 批准号:2104008
- 负责人:
- 金额:$ 130.86万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-06-01 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Modern computing applications generate massive amounts of data at unprecedented rates. Beyond simply storing data, one increasingly common requirement is to store activity data, also known as log data, which describe things that happen rather than things that are. Activity data are generated by computing systems, scientific instruments, electrical devices, etc. as well as by humans. The fast growing of activity data stresses current data management systems beyond their capability and becomes a known killer performance bottleneck of high-performance computing systems. This project develops ChronoLog, a novel system for organizing and storing activity data effectively and efficiently. ChronoLog leverages modern storage hardware and provides user-focused plugins and easy-to-use interface for productivity. It will benefit a diverse range of communities in various ways, such as enabling better fraud detection in financial transactions, faster and more accurate weather predictions and simulations, reduced time-to-insight for medical and bioengineering data, autonomous computing (e.g., driving), and more secure web and mobile services. ChronoLog uses physical time to provide a synchronization-free data distribution and the total ordering on a log. It first leverages multiple storage tiers, such as storage-class memories (e.g., 3D XPoint) and new flash storage (e.g., NVMe SSDs), to transparently scale the log via log auto-tiering. It then adopts a tunable parallel access model, which offers multiple-writers-multiple-readers (MWMR) semantics and highly concurrent I/O, to fully utilize the multi-tiered storage environment. ChronoLog's innovative design supports high-performance data access via I/O isolation between tails and historical operations, efficient resource utilization with newly developed elastic storage capabilities, and scalability using a novel 3D log distribution. It facilitates data processing pipelining by acting as an authoritative source of strong consistency and with the help of fast append and commit semantics. It can be used as an arbitrator offering a plethora of features such as transactional isolation and atomicity, a consensus engine for consistent replication and indexing services, and a scalable data integration and warehousing solution. ChronoLog and its plugins establish a robust, flexible, and high-performance storage ecosystem that promotes the development of scalable applications and services for high performance computing systems. The project includes a diverse group of collaborators who share a common need for a fundamentally new approach to distributed logging to address their use cases. These close partnerships will strengthen the bonds between academic and applied science, ultimately leading to new applications and driving discovery in domains as diverse as geoscience, cosmology, and astrophysics. Forming these collaborations and integrating students and junior IT professionals will create a well-trained workforce in cyberinfrastructure.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.
现代计算应用程序以前所未有的速度生成大量数据。除了简单地存储数据之外,一个越来越常见的需求是存储活动数据,也称为日志数据,它描述发生的事情而不是正在发生的事情。活动数据由计算系统、科学仪器、电气设备等以及人类生成。活动数据的快速增长使现有的数据管理系统不堪重负,成为高性能计算系统的致命性能瓶颈。该项目开发了ChronoLog,一个有效和高效地组织和存储活动数据的新系统。ChronoLog利用现代存储硬件,并提供以用户为中心的插件和易于使用的界面,以提高生产力。它将以各种方式使各种社区受益,例如在金融交易中实现更好的欺诈检测,更快,更准确的天气预测和模拟,减少医疗和生物工程数据的洞察时间,自主计算(例如,驾驶)以及更安全的Web和移动的服务。ChronoLog使用物理时间来提供无同步的数据分布和日志上的总排序。它首先利用多个存储层,例如存储级存储器(例如,3D XPoint)和新的闪存(例如,NVMe SSD),通过日志自动分层透明地扩展日志。然后,它采用了一个可调的并行访问模型,它提供了多写多读(MWMR)语义和高度并发的I/O,充分利用多层存储环境。ChronoLog的创新设计通过尾部和历史操作之间的I/O隔离支持高性能数据访问,通过新开发的弹性存储功能实现高效资源利用,并通过新颖的3D日志分布实现可扩展性。它通过充当强一致性的权威来源并借助快速追加和提交语义来促进数据处理流水线。它可以用作仲裁器,提供大量功能,如事务隔离和原子性,一致复制和索引服务的共识引擎,以及可扩展的数据集成和仓库解决方案。ChronoLog及其插件建立了一个强大、灵活和高性能的存储生态系统,促进了高性能计算系统的可扩展应用程序和服务的开发。该项目包括一组不同的合作者,他们共同需要一种全新的分布式日志记录方法来解决他们的用例。这些密切的伙伴关系将加强学术和应用科学之间的联系,最终导致新的应用,并推动地球科学,宇宙学和天体物理学等领域的发现。形成这些合作并整合学生和初级IT专业人员将在网络基础设施方面创造一支训练有素的劳动力队伍。该奖项反映了NSF的法定使命,并通过使用基金会的知识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(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kyle Chard其他文献
Walking the cost-accuracy tightrope: balancing trade-offs in data-intensive genomics
走成本准确性钢丝:平衡数据密集型基因组学的权衡
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
K. Leung;M. Kimball;Jason Pitt;A. Woodard;Kyle Chard - 通讯作者:
Kyle Chard
GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS
GreenFaaS:利用 FaaS 最大限度提高 HPC 工作负载的能源效率
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Alok V. Kamatar;Valerie Hayot;Y. Babuji;André Bauer;Gourav Rattihalli;Ninad Hogade;D. Milojicic;Kyle Chard;Ian Foster - 通讯作者:
Ian Foster
Enabling real-time multi-messenger astrophysics discoveries with deep learning
利用深度学习实现实时多信使天体物理学发现
- DOI:
10.1038/s42254-019-0097-4 - 发表时间:
2019-10-03 - 期刊:
- 影响因子:39.500
- 作者:
E. A. Huerta;Gabrielle Allen;Igor Andreoni;Javier M. Antelis;Etienne Bachelet;G. Bruce Berriman;Federica B. Bianco;Rahul Biswas;Matias Carrasco Kind;Kyle Chard;Minsik Cho;Philip S. Cowperthwaite;Zachariah B. Etienne;Maya Fishbach;Francisco Forster;Daniel George;Tom Gibbs;Matthew Graham;William Gropp;Robert Gruendl;Anushri Gupta;Roland Haas;Sarah Habib;Elise Jennings;Margaret W. G. Johnson;Erik Katsavounidis;Daniel S. Katz;Asad Khan;Volodymyr Kindratenko;William T. C. Kramer;Xin Liu;Ashish Mahabal;Zsuzsa Marka;Kenton McHenry;J. M. Miller;Claudia Moreno;M. S. Neubauer;Steve Oberlin;Alexander R. Olivas;Donald Petravick;Adam Rebei;Shawn Rosofsky;Milton Ruiz;Aaron Saxton;Bernard F. Schutz;Alex Schwing;Ed Seidel;Stuart L. Shapiro;Hongyu Shen;Yue Shen;Leo P. Singer;Brigitta M. Sipocz;Lunan Sun;John Towns;Antonios Tsokaros;Wei Wei;Jack Wells;Timothy J. Williams;Jinjun Xiong;Zhizhen Zhao - 通讯作者:
Zhizhen Zhao
Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis
揭示时间性能偏差:在微服务性能分析中利用集群
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
André Bauer;Timo Dittus;Martin Straesser;Alok V. Kamatar;Matt Baughman;Lukas Beierlieb;Marius Hadry;Daniel Grillmeyer;Yannik Lubas;Samuel Kounev;Ian Foster;Kyle Chard - 通讯作者:
Kyle Chard
A terminology for scientific workflow systems
科学工作流系统的术语
- DOI:
10.1016/j.future.2025.107974 - 发表时间:
2026-01-01 - 期刊:
- 影响因子:6.100
- 作者:
Frédéric Suter;Tainã Coleman;İlkay Altintaş;Rosa M. Badia;Bartosz Balis;Kyle Chard;Iacopo Colonnelli;Ewa Deelman;Paolo Di Tommaso;Thomas Fahringer;Carole Goble;Shantenu Jha;Daniel S. Katz;Johannes Köster;Ulf Leser;Kshitij Mehta;Hilary Oliver;J.-Luc Peterson;Giovanni Pizzi;Loïc Pottier;Rafael Ferreira da Silva - 通讯作者:
Rafael Ferreira da Silva
Kyle Chard的其他文献
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{{ truncateString('Kyle Chard', 18)}}的其他基金
Collaborative Research: Frameworks: Diamond: Democratizing Large Neural Network Model Training for Science
合作研究:框架:钻石:科学大型神经网络模型训练的民主化
- 批准号:
2311769 - 财政年份:2023
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
Collaborative Research: REU Site: BigDataX: From theory to practice in Big Data computing at eXtreme scales
合作研究:REU 网站:BigDataX:极限规模大数据计算从理论到实践
- 批准号:
2150501 - 财政年份:2022
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
Collaborative Research: Sustainability: A Community-Centered Approach for Supporting and Sustaining Parsl
合作研究:可持续性:以社区为中心的支持和维持 Parsl 的方法
- 批准号:
2209919 - 财政年份:2022
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
Collaborative Research: OAC Core: Enabling Extremely Fine-grained Parallelism on Modern Many-core Architectures
合作研究:OAC Core:在现代多核架构上实现极其细粒度的并行性
- 批准号:
2107283 - 财政年份:2021
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
CCRI: Planning: Collaborative Research: Infrastructure for Enabling Systematic Development and Research of Scientific Workflow Management Systems
CCRI:规划:协作研究:支持科学工作流程管理系统系统开发和研究的基础设施
- 批准号:
2016682 - 财政年份:2020
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
CSR: Small: Cost-Aware Cloud Profiling, Prediction, and Provisioning as a Service
CSR:小:具有成本意识的云分析、预测和配置即服务
- 批准号:
1816611 - 财政年份:2018
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
REU Site: Collaborative Research: BigDataX: From theory to practice in Big Data computing at eXtreme scales
REU 网站:协作研究:BigDataX:极限规模大数据计算从理论到实践
- 批准号:
1757970 - 财政年份:2018
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
Collaborative Research: SI2-SSI: Swift/E: Integrating Parallel Scripted Workflow into the Scientific Software Ecosystem
协作研究:SI2-SSI:Swift/E:将并行脚本工作流程集成到科学软件生态系统中
- 批准号:
1550588 - 财政年份:2016
- 资助金额:
$ 130.86万 - 项目类别:
Standard Grant
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