CSR: Small: Learning and Management in Tiered Memory Systems

CSR：小：分层内存系统中的学习和管理

• 批准号: 2323100
• 负责人: Janki Bhimani
• 金额: $ 51.47万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323100&HistoricalAwards=false
• 关键词: CSR; Small; Learning; Management; Tiered; CSR; Small; Learning; Management; Tiered

Tiered memory systems are important for companies and organizations that need to manage vast amounts of data while keeping costs under control. They help to improve performance and efficiency by reducing the time it takes to access data, and also reduce the overall cost of computing systems by providing a more cost-effective solution for storing large amounts of data. The research objective of this proposed project is to design new tiered memory management techniques for in-memory databases and analytic frameworks. We will explore the power of machine learning (ML) as well as its limits and overheads as a more general, adaptable solution in improving many aspects such as scanning, migration, allocation, parameter tuning, and task scheduling to optimize the performance, QoS, and resource utilization. This research will allow designing better tiered memory systems for rapidly changing workloads. The proposed comprehensive approach to applying ML to tiered memories will generate new guidelines and leave a significant impact on many areas that are dependent on processing a large amount of data. This project will share the findings with undergraduate and graduate students through computer science programs and open up career opportunities to students from underrepresented groups and first-generation college students. This project will disseminate the proposed techniques into the industry and foster technology transfer through new industrial collaborations. The developed infrastructure will be available to the research community through a web-based portal.This research makes empirical contributions to the system-ML co-design space by tackling major challenges posed by evolving large-scale memory-intensive applications. Specifically, it advances the state of knowledge regarding, (1) how to design and develop a machine learning-based dynamic memory tiering system that is designed to ensure that the right data is in the right tier at the right time?; (2) how to use expert domain knowledge for each ML design decision such that the efficiency and the overhead of the final model are manageable and useful in real systems?; (3) how to design interactive frameworks that allow the user to modify the internal resources and parameters of the tiered memory system?; (4) how to enable novices to configure tiered memory systems with respect to their workloads and data processing requirements to obtain high performance and resource utilization?; and (5) how to derive ML models to predict the future workload patterns and accordingly configure the tiered memory systems in advance for better performance?; and Thus, to design sustainable ML-based tiered memory systems, exploring the trade-off between performance, QoS, and resource utilization by using different ML models for various design decisions is very important.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.

分层存储系统对于需要管理大量数据的同时控制成本的公司和组织很重要。 它们通过减少访问数据所需的时间来帮助提高性能和效率，并通过为存储大量数据提供更具成本效益的解决方案来降低计算系统的整体成本。该建议的项目的研究目标是为内存数据库和分析框架设计新的分层内存管理技术。 我们将探索机器学习的力量（ML）及其限制和开销，以改善许多方面，例如扫描，迁移，分配，参数调整以及任务计划，以优化性能，QoS和资源利用。这项研究将允许设计更好的分层存储系统，以快速变化的工作负载。提出的将ML应用于分层记忆的综合方法将产生新的准则，并对许多取决于处理大量数据的领域产生重大影响。该项目将通过计算机科学课程与本科生和研究生分享研究结果，并为来自代表性不足的团体和第一代大学生的学生提供职业机会。该项目将通过新的工业合作将所提出的技术传播到行业，并促进技术转移。开发的基础架构将通过基于网络的门户网站向研究社区提供。这项研究通过解决不断发展的大规模记忆密集型应用程序所带来的重大挑战，从而为系统ML共同设计空间做出了经验贡献。具体而言，它提高了有关（1）如何设计和开发基于机器学习的动态内存分层系统的知识状态，该系统旨在确保正确的数据在正确的时间在正确的层次？ （2）如何为每个ML设计决策使用专家领域知识，以使最终模型的效率和开销在实际系统中可以管理且有用？ （3）如何设计允许用户修改分层内存系统的内部资源和参数的交互式框架？ （4）如何使新手相对于其工作负载和数据处理要求配置分层的内存系统，以获得高性能和资源利用？ （5）如何得出ML模型来预测未来的工作负载模式，并因此提前配置分层的内存系统以提高性能？因此，为了设计基于ML的可持续分层内存系统，通过使用不同的ML模型进行各种设计决策来探索绩效，QoS和资源利用之间的权衡。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来支持的。