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User-Level Physical Memory Management

用户级物理内存管理

基本信息

批准号：
9212087
负责人：
Stuart Sechrest
金额：
$ 9万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1992
资助国家：
美国
起止时间：
1992-09-15 至 1995-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9212087&HistoricalAwards=false
关键词：
User Level Physical Memory Management

项目摘要

A machine's physical memory is a resource for which many subsystems, including databases, persistent object storage managers, and multimedia file systems, are potential competitors. As memory size grows, optimizing the diverse uses of physical memory will require diverse management policies. These policies must, however, coexist, and the claims on memory for different uses must be balanced. This research investigates a system architecture for balancing the requirements of memory competitors, while allowing a greater variety of management policies to be implemented. The system architecture permits pageout daemons situated outside the kernel to control pools of physical memory. This allows subsystems such as databases much greater control over memory management policy. A balance manager within the kernel controls the size of these pools. A simulator is used to explore the interactions of management policies, while a prototype system, supported by an extended version of Mach 3.0, allows the experimental validation of the simulation results. Successful completion of this project will provide a framework for managing large physical memories in future machines. Using the balancing strategies identified in this research, subsystems for memory- intensive application will be more readily developed and installed on new machines without ad hoc performance tuning.

机器的物理内存是一种资源，包括数据库、持久对象存储管理器和多媒体文件系统在内的许多子系统都是该资源的潜在竞争对手。随着内存大小的增长，优化物理内存的不同使用将需要不同的管理策略。然而，这些策略必须共存，并且必须平衡不同用途对内存的要求。这项研究调查了一种系统架构，用于平衡内存竞争对手的需求，同时允许实施更多种类的管理策略。系统架构允许位于内核外部的pageout守护进程控制物理内存池。这允许数据库等子系统更好地控制内存管理策略。内核中的平衡管理器控制这些池的大小。模拟器用于探索管理策略的交互作用，而原型系统由扩展版本的Mach 3.0支持，允许对模拟结果进行实验验证。该项目的成功完成将为管理未来机器中的大容量物理内存提供一个框架。使用这项研究中确定的平衡策略，内存密集型应用程序的子系统将更容易开发并安装在新机器上，而无需特别的性能调整。