Collaborative Research:CNS Core:Small:Towards Efficient Cloud Services

合作研究：CNS核心：小型：迈向高效的云服务

• 批准号: 2050007
• 负责人: Jiajia Li
• 金额: $ 24.98万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050007&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Cloud computing frameworks enable a wide range of services while sharing computation resources and infrastructure costs. To achieve these benefits, cloud computing frameworks rely on layers of abstractions to reduce the complexity of distributed and heterogeneous computational infrastructure. Abstractions hide resource management complexities and improve programmability. However, abstractions make cloud frameworks less observable, resulting in various forms of inefficiencies. This project will address the challenges of practical cloud monitoring techniques to guide cloud application development and system design.This project will explore the inefficiency patterns in cloud computing infrastructures and applications. More specifically, it will provide novel measurement techniques to enable monitoring these inefficiencies across the cloud layers of abstraction. Additionally, the project will develop tools that will provide actionable insights for high-performance cloud frameworks and application development. This project has three thrusts. First, it will measure language-level abstractions for intra-application inefficiencies. Second, it will explore the inefficient communication patterns among microservices for inter-service optimization. Third, it will develop tools to analyze inefficiencies in the entire stack of cloud software layers of abstraction.This project will bridge the knowledge gap between application developers and system designers to provide more efficient cloud environments. It will advance the state-of-the-art cloud monitoring techniques and address the current and future challenges in the cloud computing community. The tools developed from this project will have broad interest from industry, research institutes, and laboratories for efficient code execution and high system throughput. Furthermore, the project will disseminate the obtained knowledge through hands-on training sessions and tutorials. Finally, the project will facilitate curriculum development with a particular focus on involving minority and under-represented students.The project will maintain a website at https://www.probir.info/cloudprof. The website will host all the project outcomes, including the publications, open-source code, toolkits, datasets, documentation, and tutorials. The website will be accessible to the public throughout the project lifetime and beyond.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.

云计算框架在共享计算资源和基础设施成本的同时实现了广泛的服务。为了实现这些好处，云计算框架依赖于抽象层来降低分布式和异构计算基础设施的复杂性。抽象隐藏了资源管理的复杂性并提高了可编程性。然而，抽象使得云框架不太可观察，导致各种形式的效率低下。该项目将解决实际云监控技术的挑战，以指导云应用程序开发和系统设计。该项目将探索云计算基础设施和应用程序中的低效率模式。更具体地说，它将提供新的测量技术，以便能够跨云抽象层监控这些低效率。此外，该项目还将开发工具，为高性能云框架和应用程序开发提供可操作的见解。这个项目有三个重点。首先，它将测量应用程序内部效率低下的语言级抽象。其次，它将探索微服务之间的低效通信模式，以实现服务间的优化。第三，它将开发工具来分析整个云软件抽象层堆栈中的低效性。该项目将弥合应用程序开发人员和系统设计人员之间的知识差距，以提供更高效的云环境。它将推进最先进的云监控技术，并解决云计算社区当前和未来的挑战。从这个项目开发的工具将有广泛的兴趣，从行业，研究机构和实验室的高效代码执行和高系统吞吐量。此外，该项目将通过实践培训和教程传播所获得的知识。最后，该项目将促进课程编制，特别注重让少数民族和代表性不足的学生参与，并将维持一个网站https://www.probir.info/cloudprof。该网站将托管所有项目成果，包括出版物、开源代码、工具包、数据集、文档和教程。该网站将在整个项目生命周期及以后向公众开放。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

Toward Efficient Interactions between Python and Native Libraries

实现 Python 和本机库之间的高效交互

• 发表时间: 2021
• 期刊: The 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
• 作者: Tan, J;Chen, C;Liu, Z;Ren, R;Song, R;Shen, X;Liu, X
• 通讯作者: Liu, X

OJXPerf: featherlight object replica detection for Java programs

OJXPerf：Java 程序的轻量级对象副本检测

• DOI: 10.1145/3510003.3510083
• 发表时间: 2022
• 期刊: ICSE '22: Proceedings of the 44th International Conference on Software Engineering
• 作者: Li, Bolun;Xu, Hao;Zhao, Qidong;Su, Pengfei;Chabbi, Milind;Jiao, Shuyin;Liu, Xu
• 通讯作者: Liu, Xu

DroidPerf: Profiling Memory Objects on Android Devices

DroidPerf：分析 Android 设备上的内存对象

• DOI: 10.1145/3570361.3592503
• 发表时间: 2023
• 期刊: ACM
• 作者: Li, Bolun;Zhao, Qidong;Jiao, Shuyin;Liu, Xu
• 通讯作者: Liu, Xu

DJXPerf: Identifying Memory Inefficiencies via Object-Centric Profiling for Java

• DOI: 10.1145/3579990.3580010
• 发表时间: 2021-04
• 期刊: Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization
• 作者: Bolun Li;Pengfei Su;Milind Chabbi;Shuyin Jiao;Xu Liu