Robust Synchronization Primitives for Non-Volatile Main Memory

由于生产力和收入的损失，意外停机和数据丢失每年给加拿大企业造成170亿美元的损失。虽然硬件故障（例如，失去电力或组件故障）是许多停机事件的根本原因，但由于随之而来的软件恢复而造成的经济损失是广泛的。这是因为现代数据驱动的软件系统大量使用主存来缓存关键数据集，并且必须从较慢的辅助存储恢复这些数据集。新兴的非易失性主存储器（NVRAM）介质通过将辅助存储器的持久性与主存储器的性能相结合，缓解了这个问题。软件系统必须重新设计以抓住这个独特的机会。提出的研究旨在通过开发基于nvram的可恢复数据结构的底层构建块来加快应用程序恢复。为此，该项目将解决以下技术目标：(1)为各种同步原语设计高效和容错的实现，而不依赖于对环境的简化假设；(2)建立新实现的理论正确性、特性和性能限制；(3)使用实现的原语来改进可被应用程序直接使用的可恢复数据结构的设计。在成功完成这些目标后，该研究计划将生成同步原语和可恢复数据结构的软件原型，这将允许软件开发人员在关键软件应用和服务中释放NVRAM的性能优势。从这项工作中产生的分析和实证结果将通过定义新的算法和理论基础，以及通过阐明微妙的性能权衡和可恢复数据结构的基本限制来推进计算机科学。

Unplanned downtime and data loss cost Canadian enterprises $17B annually due to lost productivity and revenues. While hardware malfunction (e.g., loss of electrical power or component failure) is the root cause of many downtime events, the economic losses due to consequent software recovery are extensive. This is because modern data-driven software systems use main memory heavily to cache critical data sets, and must restore these data sets from slower secondary storage. Emerging non-volatile main memory (NVRAM) media offer relief from this problem by combining the persistence of secondary storage with the performance of main memory. Software systems must be redesigned to seize this unique opportunity.The proposed research aims to speed up application recovery by developing low-level building blocks of NVRAM-based recoverable data structures. To that end, the project will address the following technical objectives: (O1) design efficient and fault-tolerant implementations for a variety of synchronization primitives without relying on simplifying assumptions regarding the environment; (O2) establish theoretical correctness properties and performance limits of the novel implementations; and (O3) use implemented primitives to improve the design of recoverable data structures that can be used directly by applications. Upon successful completion of these objectives, the research program will generate software prototypes of both synchronization primitives and recoverable data structures that will allow software developers to unleash the performance benefits of NVRAM in critical software applications and services. Analytical and empirical results arising from this work will advance computer science by defining novel algorithms and theoretical foundations, as well as by elucidating subtle performance trade-offs and fundamental limitations of recoverable data structures.