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CNS Core: Small: Robust Performance Guarantee of Containerized Microservices in the Cloud

CNS核心：小：云中容器化微服务的稳健性能保障

基本信息

批准号：
1911012
负责人：
Palden Lama
金额：
$ 24.39万
依托单位：
University of Texas at San Antonio
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
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911012&HistoricalAwards=false
关键词：
CNS Core Small Robust Performance

项目摘要

Large-scale web services are increasingly being built with many small modular components (microservices), which can be deployed, updated and scaled seamlessly. These microservices are packaged to run in a lightweight isolated execution environment (containers) and deployed on compute resources rented from cloud providers. However, the complex interactions and the contention of shared hardware resources in cloud datacenters pose significant challenges in managing web service performance. This project will develop novel performance models and resource management solutions that can enable cloud platforms to provide robust performance guarantee for large-scale web services.This project will leverage multi-layered data collected from container-level resource usage metrics and virtual machine-level hardware performance counter metrics for accurate performance modeling. It will (1) develop probabilistic machine learning-based performance models that can quickly adapt to changing system dynamics and directly provide confidence bounds in the predictions even when the data is noisy and sparse. (2) It will directly incorporate predictive uncertainty obtained from the proposed models into the design and development of a robust resource scaling system and interference-aware container scheduling algorithm to manage the end-to-end tail latency of microservice request workflows in a resource efficient manner. The project will improve the performance, cost-efficiency, and scalability of a broad range of cloud-native applications. This can spur innovation in large-scale application development through increased adoption of cloud computing and cloud-native design principles. To this end, the project will develop open-source solutions for platforms such as Kubernetes. It will advance interdisciplinary education by developing a new course, which will contribute to a Cloud Computing Certificate jointly created by the University of Texas at San Antonio Colleges of Science, Engineering and Business.Data produced as a result of this project, including algorithms, models, software solutions, publications, and courseware, will be made publicly available at the project repository: http://www.cs.utsa.edu/~plama/microservices.html. The project data will be maintained and made available during the execution of the project and minimum three years after the project's ending date. Data will be stored on the host institution's dedicated web servers.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.

大规模web服务越来越多地由许多小型模块化组件（微服务）构建，这些组件可以无缝地部署、更新和扩展。这些微服务被打包在轻量级的隔离执行环境（容器）中运行，并部署在从云提供商租用的计算资源上。然而，云数据中心中复杂的交互和共享硬件资源的争用在管理web服务性能方面提出了重大挑战。该项目将开发新的性能模型和资源管理解决方案，使云平台能够为大规模web服务提供强大的性能保证。该项目将利用从容器级资源使用指标和虚拟机级硬件性能计数器指标收集的多层数据来进行准确的性能建模。它将(1)开发基于概率机器学习的性能模型，该模型可以快速适应不断变化的系统动力学，并且即使在数据嘈杂和稀疏的情况下也可以直接提供预测的置信范围。(2)将模型得到的预测不确定性直接纳入稳健的资源扩展系统和干扰感知容器调度算法的设计和开发中，以资源高效的方式管理微服务请求工作流的端到端尾部延迟。该项目将提高大量云原生应用程序的性能、成本效率和可扩展性。这可以通过更多地采用云计算和云原生设计原则来刺激大规模应用程序开发中的创新。为此，该项目将为Kubernetes等平台开发开源解决方案。它将通过开发一门新课程来推进跨学科教育，该课程将为德克萨斯大学圣安东尼奥科学、工程和商业学院联合创建的云计算证书做出贡献。作为这个项目的结果产生的数据，包括算法、模型、软件解决方案、出版物和课件，将在项目存储库中公开提供：http://www.cs.utsa.edu/~plama/microservices.html。项目数据将在项目执行期间和项目结束后至少三年内予以保存和提供。数据将存储在托管机构的专用web服务器上。该奖项反映了美国国家科学基金会的法定使命，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（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SLO-aware Virtual Rebalancing for Edge Stream Processing

用于边缘流处理的 SLO 感知虚拟重新平衡

DOI：
10.1109/ic2e52221.2021.00027
发表时间：
2021
期刊：
IEEE International Conference on Cloud Engineering
影响因子：
0
作者：
Kang, Peng;Lama, Palden;Khan, Samee U.
通讯作者：
Khan, Samee U.

Some New Observations on SLO-aware Edge Stream Processing

关于 SLO 感知边缘流处理的一些新观察

DOI：
10.1109/cloudsummit57601.2023.00011
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Shahid, Amna;Kang, Peng;Lama, Palden;Khan, Samee U.
通讯作者：
Khan, Samee U.

KneeScale: Efficient Resource Scaling for Serverless Computing at the Edge

DOI：
10.1109/ccgrid54584.2022.00027
发表时间：
2022-05
期刊：
2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
影响因子：
0
作者：
Xue Li;Peng Kang;Jordan Molone;W. Wang;P. Lama
通讯作者：
Xue Li;Peng Kang;Jordan Molone;W. Wang;P. Lama

Robust Resource Scaling of Containerized Microservices with Probabilistic Machine learning

通过概率机器学习实现容器化微服务的强大资源扩展