SPX: Integrating Persistent Memory in the Cloud

SPX：在云中集成持久内存

• 批准号: 1822965
• 负责人: Samira Khan
• 金额: $ 96.95万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822965&HistoricalAwards=false
• 关键词: SPX; Integrating; Persistent; Memory; Cloud

The massive volume of data and high computing intensity of large-scale applications in the cloud require thousands of machines in big data centers. In addition, there is an increasing demand for faster, energy-efficient, and scalable performance from new data-intensive applications. Unfortunately, as the technology scaling slows down, the semiconductor industry has been facing a major challenge in providing better performance and reducing the power consumption while processing large datasets. To provide better performance and lower costs for cloud applications that manipulate massive data with tight latency constraints, service providers are moving towards in-memory frameworks to store the working data. By exploring the roles of emerging memory technologies, this research project has the potential to improve cloud computing performance. The ideas developed in this research will bridge the gap between architecture, systems, and software engineering community and will enable system support and automated tools for adapting applications in the persistent cloud. The project will eventually enable a holistic "persistent cloud system" such that the cloud applications can be adapted transparently without significant programmers' effort.The goal of this work is to enable a persistent cloud system in a holistic manner across the system stack such that the persistent cloud applications can be adapted in the systems without significant programmers? effort. In order to design a persistent cloud system, this work is to provide full stack support from the applications to hardware through three major research directions that need to be addressed to design a full-stack persistent cloud system, (i) lightweight storage layer support for persistent memory systems, (ii) data monitoring and placement based on application characteristics and trade-offs in NVM, and (iii) automated persistency support at the application-level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

云中大规模应用的大量数据和高计算强度需要大数据中心中的数千台机器。此外，新的数据密集型应用程序对更快，节能和可扩展性能的需求不断增长。不幸的是，随着技术扩展的减慢，半导体行业在提供更好的性能和减少功耗的同时，在处理大型数据集时面临着一个重大挑战。 为了为云应用程序提供更好的性能和较低的成本，以操纵大量数据具有严格的延迟限制，服务提供商正朝着内存框架迈向存储工作数据。 通过探索新兴记忆技术的作用，该研究项目有可能提高云计算性能。这项研究中提出的想法将弥合体系结构，系统和软件工程社区之间的差距，并将启用系统支持和自动化工具，以适应持久云中的应用程序。该项目最终将实现整体的“持续云系统”，以便可以在没有重要的程序员努力的情况下透明地对云应用程序进行调整。这项工作的目的是在整个系统堆栈中以整体方式启用持续的云系统，以便没有重要的程序员可以在没有重要的程序员中适应持久的云应用程序？努力。为了设计持久的云系统，这项工作是通过三个主要的研究方向提供了从应用程序到硬件的全面支持，需要解决这些方向，以设计全堆栈持久的云系统，（i）对持久存储系统的轻量级存储层支持，（ii）基于NVM和（III）的应用程序的数据监视和位置，以及（ii）持续的数据，以及（IIII）的启动，以及III III次启用。法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。