CSR: Small: Big Memory: Exploring Memory Management Mechanisms and Policies for Rack-Scale Computers

CSR：小：大内存：探索机架级计算机的内存管理机制和策略

• 批准号: 1619124
• 负责人: Scott Rixner
• 金额: $ 50万
• 依托单位: William Marsh Rice University
• 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=1619124&HistoricalAwards=false
• 关键词: CSR; Small; Big; Memory; Exploring

Large-memory applications, including data analytics, in-memory objectcaches, and in-memory databases, are a major consumer of data centerresources. Moreover, these applications can effectively use morememory than a single computer can provide. Traditional approaches toscaling these applications involve parallelism across multiplecomputers. However, that may not use resources cost-effectively, asthe ratio of processing, networking, and memory is determined by theconfiguration of the underlying computers, not the application. Thisproject explores disaggregated servers, or "rack-scale computers".These servers consist of pools of processors, memory, and I/O deviceswithin a rack that can be flexibly allocated to virtual machines basedon the needs of their resident applications.This project focuses on one aspect of disaggregated servers: managinga large pool of memory within the rack. Specifically, using this poolto dynamically add memory to and remove memory from virtual machines.This will enable applications to handle bursty load without the needto spin up/down entire virtual machines. In order to effectivelymanage memory across virtual machines, the hypervisor will need abetter understanding of how memory is being used, by both the guestoperating systems and applications. Therefore, we are exploring theinterfaces, mechanisms, and policies within and between thehypervisor, guest operating system, and application that will enablethe use of large disaggregated memories. This research is one step towards rethinking what a computer is and how to construct one that better serves data science and analytics applications that can benefit from large amounts of memory.

大内存应用程序，包括数据分析、内存对象缓存和内存数据库，是数据中心资源的主要消耗者。此外，这些应用程序可以有效地使用比单台计算机所能提供的更多的内存。扩展这些应用程序的传统方法涉及跨多台计算机的并行。然而，这可能不能有效地使用资源，因为处理、联网和内存的比例是由底层计算机的配置决定的，而不是应用程序。本项目探讨的是分散的服务器，或“机架式计算机”。这些服务器由机架中的处理器、内存和I/O设备池组成，可以根据其驻留应用程序的需求灵活地分配给虚拟机。本项目关注分散服务器的一个方面：管理机架中的大内存池。具体地说，使用此池可以动态地向虚拟机添加内存和从中删除内存。这将使应用程序能够处理突发负载，而无需启动/关闭整个虚拟机。为了有效地管理跨虚拟机的内存，管理程序需要更好地了解来宾操作系统和应用程序如何使用内存。因此，我们正在探索管理程序、来宾操作系统和应用程序内部和之间的接口、机制和策略，以实现大容量分散内存的使用。这项研究是朝着重新思考计算机是什么，以及如何构建一台能够更好地服务于数据科学和分析应用的计算机，从而受益于大量内存迈出的一步。