NeTS: Small: Collaborative Research: Enabling Application-Level Performance Predictability in Public Clouds

NeTS：小型：协作研究：在公共云中实现应用程序级性能可预测性

• 批准号: 1617698
• 负责人: Zhenhua Liu
• 金额: $ 21.15万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617698&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Enabling

State-of-the-art resource sharing mechanisms in today's datacenters and compute clouds are agnostic to application-level performance requirements, resulting in unpredictable performance. This is especially true for the network. Unlike, CPU, memory, or disk, cloud operators do not provide any guarantees for the network. Many tenants rely on over-provisioning and static allocation for performance isolation, which results in low utilization and increased cost and environmental impacts. This project aims to build a set of solutions to achieve short- and long-term performance predictability with high resource utilization. The goal is to enable coexisting applications from different tenants to meet a variety of performance objectives including obtaining timely responses and minimizing variance of successive responses, while adhering to organizational hierarchies of individual tenants. The key technical challenges in this project include developing short- and long-term resource allocation algorithms, accurate demand estimation, as well as fast and efficient enforcement, all of which are compounded by the multi-resource and shared nature of the network. Two key techniques guide the proposed work: (i) temporal scheduling ensures predictable performance through short- and long-term performance isolation, and (ii) spatial placement ensures higher utilization through initial placement and periodic migration of tenants' virtual machines. Predictable, efficient data analytics will have significant socio-economic ramifications. It will also enable mission-critical applications, e.g., anomaly detection, fraud protection, autonomous vehicles, and robotics-- that require a highly consistent and reliable level of performance to coexist with the less sensitive ones. Algorithms and software from the project will be incorporated into existing open-source big data stacks for public reuse. By leveraging ongoing relationships with the industry, artifacts from this project will be converted from research into practice in a fast manner. The project has significant educational and outreach components, which include introducing new courses at both graduate and undergraduate levels based on the outcomes of this project as well as arranging cloud computing boot camps aimed at students from high schools and involving women and under-represented minorities.

当今数据中心和计算云中最先进的资源共享机制与应用程序级性能要求无关，从而导致性能不可预测。对于网络来说尤其如此。与CPU、内存或磁盘不同，云运营商不为网络提供任何保证。许多租户依靠超额配置和静态分配来实现性能隔离，这会导致利用率低下，并增加成本和环境影响。该项目旨在构建一套解决方案，以实现短期和长期性能可预测性和高资源利用率。目标是使来自不同租户的共存应用程序能够满足各种性能目标，包括获得及时响应和最小化连续响应的差异，同时遵守各个租户的组织层次结构。该项目的关键技术挑战包括开发短期和长期资源分配算法、准确的需求估计以及快速有效的执行，所有这些都因网络的多资源和共享性质而变得更加复杂。两项关键技术指导了拟议的工作：(i) 时间调度通过短期和长期性能隔离确保可预测的性能，(ii) 空间布局通过租户虚拟机的初始布局和定期迁移确保更高的利用率。可预测、高效的数据分析将产生重大的社会经济影响。它还将支持关键任务应用程序，例如异常检测、欺诈防护、自动驾驶汽车和机器人，这些应用程序需要高度一致和可靠的性能水平才能与不太敏感的应用程序共存。该项目的算法和软件将被纳入现有的开源大数据堆栈中以供公众重用。通过利用与行业的​​持续关系，该项目的成果将快速从研究转化为实践。该项目具有重要的教育和推广部分，其中包括根据该项目的成果在研究生和本科生层面引入新课程，以及针对高中生并让女性和代表性不足的少数族裔参与的云计算训练营。

项目成果

AlloX: Allocation across Computing Resources for Hybrid CPU/GPU clusters

AlloX：混合CPU/GPU集群的计算资源分配

• DOI: 10.1145/3305218.3305251
• 发表时间: 2019
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Le, Tan N.;Sun, Xiao;Chowdhury, Mosharaf;Liu, Zhenhua
• 通讯作者: Liu, Zhenhua

Greening Reliability of Virtual Network Functions via Online Optimization

• DOI: 10.1109/iwqos49365.2020.9212998
• 发表时间: 2020-06
• 期刊: 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)
• 作者: Xiaojun Shang;Yu Liu;Yingling Mao;Zhenhua Liu;Yuanyuan Yang

Online optimization in the Non-Stationary Cloud: Change Point Detection for Resource Provisioning (Invited Paper)

非平稳云中的在线优化：资源配置的变化点检测（特邀论文）

• DOI: 10.1109/ciss.2019.8692890
• 发表时间: 2019
• 期刊: 2019 53rd Annual Conference on Information Sciences and Systems (CISS
• 作者: Maghakian, Jessica;Comden, Joshua;Liu, Zhenhua
• 通讯作者: Liu, Zhenhua

AlloX: compute allocation in hybrid clusters

• DOI: 10.1145/3342195.3387547
• 发表时间: 2020-04
• 期刊: Proceedings of the Fifteenth European Conference on Computer Systems
• 作者: T. Le;Xiao Sun;Mosharaf Chowdhury;Zhenhua Liu

Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-adapting Scheme

通过自适应方案降低边缘和云端服务功能链备份成本

• DOI: 10.1109/infocom41043.2020.9155394
• 发表时间: 2020
• 期刊: IEEE INFOCOM 2020-IEEE Conference on Computer Communications
• 作者: Shang, Xiaojun;Huang, Yaodong;Liu, Zhenhua;Yang, Yuanyuan
• 通讯作者: Yang, Yuanyuan