SHF: Small: Optimizing Consolidation Efficiency of Emerging Virtualized Cloud Applications on Contemporary Server Architecture

SHF：小型：优化当代服务器架构上新兴虚拟化云应用程序的整合效率

• 批准号: 1527535
• 负责人: Tao Li
• 金额: $ 46万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527535&HistoricalAwards=false
• 关键词: SHF; Small; Optimizing; Consolidation; Efficiency

Optimizing Virtual Machine (VM) consolidation performance has been one of the most critical tasks faced by the cloud provider community. Nevertheless, when scaling virtual platforms to handle the proliferation of cloud applications, challenges arise due to constraints on performance degradation, especially in those environments where VM consolidation density continues to grow. Worse, the consolidated workloads are shifting from conventional single-task computation-oriented applications to large-scale and complex workloads. They generate many diverse interactions and communication patterns and some of these applications have a large irregular memory footprint and high memory consumption, which presents a significant challenge to optimizing the efficiency of virtual machine consolidation. Currently, there emerge some new techniques for contemporary server architecture. Server manufacturers are replacing traditional Uniform Memory Access (UMA) machines with Non-Uniform Memory Access (NUMA) ones due to the latter's higher memory bandwidth and better system scalability. On the other hand, the throughput-oriented graphics processing units (GPUs) are being increasingly deployed to cloud data center servers to meet computation demands. Modern hypervisors begin to virtualize GPU resources and deliver efficient and reliable performance to hosted virtual machines. To embrace the opportunities and address the associated challenges, this research project will develop techniques to improve the consolidation efficiency of emerging virtualized cloud applications as the advancement of the underlying server architecture continues. The project objectives include: (1) System-wide consolidation performance profiling and optimization for NUMA architecture; (2) Graphic-as-a-Service (GaaS) workload consolidation overhead characterization and minimization; and (3) Collective workload consolidation optimizations in terms of both NUMA and GPU server configurations. This project, which synergistically integrates emerging server architecture features and virtual machine hypervisor resource management, will open the door for a new class of efficient scale-out computing platforms for cloud and big data computing. It will contribute to enabling computing systems to stay on track with its historic scaling and hence benefit numerous real-life applications. This project will also contribute to society through engaging under-represented groups, and research infrastructure dissemination for education and training.

优化虚拟机（VM）合并性能一直是云提供商社区所面临的最关键任务之一。然而，当缩放虚拟平台处理云应用程序的扩散时，由于对性能降解的限制而引起的挑战，尤其是在VM合并密度不断增长的环境中。更糟糕的是，合并的工作负载正在从常规的单任务计算应用程序转移到大规模和复杂的工作负载。它们产生了许多不同的互动和沟通模式，其中一些应用具有较大的不规则内存足迹和高内存消耗，这给优化虚拟机巩固的效率带来了重大挑战。目前，出现了一些针对当代服务器体系结构的新技术。服务器制造商正在替换传统的统一存储器访问（UMA）机器，该计算机由于后者的较高内存带宽和更好的系统可扩展性而用非统一存储器访问（NUMA）替换。另一方面，面向吞吐量的图形处理单元（GPU）越来越多地部署到云数据中心服务器以满足计算需求。现代的管理程序开始虚拟化GPU资源，并为托管虚拟机提供高效可靠的性能。为了拥抱机会并应对相关的挑战，该研究项目将开发技术，以提高新兴虚拟化云应用程序的整合效率，因为基础服务器架构的进步仍在继续。项目目标包括：（1）对NUMA架构的全系统整合性能分析和优化； （2）图形-AS-A-Service（GAAS）工作负载固结开销表征和最小化； （3）根据NUMA和GPU服务器配置，集体工作负载合并优化。该项目协同整合了新兴服务器体系结构功能和虚拟机管理机构资源管理，将为云和大数据计算的新型有效的扩展计算平台打开大门。它将有助于使计算系统以其历史性的规模保持步入正轨，从而使许多现实生活中的应用受益。该项目还将通过参与代表性不足的群体以及研究基础设施进行教育和培训来为社会做出贡献。