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.

云计算中大规模应用的海量数据和高计算强度要求大数据中心有成千上万台机器。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也越来越大。不幸的是，随着技术扩展速度的放缓，半导体行业在处理大型数据集的同时提供更好的性能和降低功耗方面面临着重大挑战。 为了为在严格的延迟约束下操作大量数据的云应用程序提供更好的性能和更低的成本，服务提供商正在转向内存框架来存储工作数据。 通过探索新兴内存技术的作用，该研究项目有可能提高云计算的性能。在这项研究中开发的想法将弥合架构，系统和软件工程社区之间的差距，并将使系统支持和自动化工具，以适应应用程序在持久云。该项目最终将实现一个整体的“持久云系统”，这样云应用程序就可以透明地适应，而不需要大量的程序员的努力。这项工作的目标是在整个系统堆栈中以整体的方式实现一个持久云系统，这样持久云应用程序就可以在系统中适应，而不需要大量的程序员。努力为了设计一个持久云系统，本文的工作是通过三个主要的研究方向提供从应用到硬件的全栈支持，这三个主要的研究方向是设计一个全栈持久云系统需要解决的问题：（i）持久存储系统的轻量级存储层支持，（ii）基于应用特性和NVM中的权衡的数据监控和放置，和（iii）应用级的自动化持久性支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。