CSR: Small: Collaborative Research: Enhancing Cloud Performance With On-Demand Isolation

CSR：小型：协作研究：通过按需隔离增强云性能

• 批准号: 1421585
• 负责人: John Lange
• 金额: $ 24.99万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421585&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Enhancing

The modern trend in computing systems is towards system architectures containing a large numbers of heterogeneous computational and I/O resources. Unfortunately, while the increase in scale allows increased workload consolidation, wherein a single system runs multiple independent applications in parallel, it does come at the cost of introducing increased interference across the different application workloads. Workload interference is the result of the behavior of one application impacting the performance of another, even if both applications are running on different hardware resources. This can be due to contention on shared hardware resources (such as last level caches, memory controllers, or I/O devices) or even software resources managed by the operating system. Cross workload interference is especially problematic for large scale shared infrastructures such as cloud hosting services, which rely on co-hosting large numbers of widely disparate workloads inside a single datacenter environment. Preventing interference effects is critical for cloud computing to fully deliver on its promise as a universal computing substrate. This project addresses the problem of cross workload interference, by providing a holistic system that both detects the impact of interference on applications and mitigates its effects by providing dynamic isolation capabilities in the underlying system software. This approach relies on the ability to dynamically partition the underlying hardware resources such that isolation is achieved at the hardware layer, while also allowing the partitioning of system software to avoid contention on more abstract resources present in the system software itself. To achieve these goals this work implements a "Virtual Platform" abstraction representing an individual and isolatable system domain assigned to a particular task or workload and consisting of one or more virtual machine instances. The virtual platform itself is assigned an allocation of hardware resources consisting of independent "isolatable units." These units are created through the decomposition of local hardware resources into the finest grained subdivision of resources that can be both individually allocated and effectively isolated from the rest of the system. While providing partitioned hardware resources to a virtual platform provides hardware level isolation, it does not address interference generated by the system software. Avoiding software level interference is achieved by partitioning the system software itself through Multi-Stack Virtualization. Multi-stack virtualization allows multiple independent system software layers to co-exist on the same local system by restricting their managed resources to the set allocated to a virtual platform. Taken together this system provides full isolation capabilities at both the hardware and software layers.

计算系统的现代趋势是朝着包含大量异构计算和 I/O 资源的系统架构发展。不幸的是，虽然规模的增加允许增加工作负载整合（其中单个系统并行运行多个独立应用程序），但它的代价是在不同应用程序工作负载之间引入更多的干扰。工作负载干扰是一个应用程序的行为影响另一个应用程序性能的结果，即使两个应用程序运行在不同的硬件资源上。这可能是由于共享硬件资源（例如最后一级缓存、内存控制器或 I/O 设备）甚至操作系统管理的软件资源的争用造成的。对于云托管服务等大规模共享基础设施来说，交叉工作负载干扰尤其成问题，这些基础设施依赖于在单个数据中心环境内共同托管大量不同的工作负载。防止干扰效应对于云计算充分兑现其作为通用计算基础的承诺至关重要。该项目通过提供一个整体系统来解决交叉工作负载干扰的问题，该系统既可以检测干扰对应用程序的影响，又可以通过在底层系统软件中提供动态隔离功能来减轻其影响。这种方法依赖于动态分区底层硬件资源的能力，以便在硬件层实现隔离，同时还允许系统软件分区以避免对系统软件本身中存在的更抽象资源的争用。为了实现这些目标，这项工作实现了“虚拟平台”抽象，表示分配给特定任务或工作负载并由一个或多个虚拟机实例组成的单独且可隔离的系统域。虚拟平台本身被分配了由独立的“可隔离单元”组成的硬件资源分配。这些单元是通过将本地硬件资源分解为最细粒度的资源细分而创建的，这些资源既可以单独分配，又可以与系统的其余部分有效隔离。虽然向虚拟平台提供分区的硬件资源提供了硬件级别的隔离，但它并不能解决系统软件产生的干扰。通过多堆栈虚拟化对系统软件本身进行分区，从而避免软件级别的干扰。多堆栈虚拟化通过将其托管资源限制为分配给虚拟平台的集合，允许多个独立的系统软件层在同一本地系统上共存。总的来说，该系统在硬件和软件层都提供了完全的隔离功能。