CSR: Small: Learning and Management in Tiered Memory Systems

分层内存系统对于需要管理大量数据同时控制成本的公司和组织来说非常重要。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，并且还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总体成本。本课题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（ML）的力量，以及它作为一种更通用的、适应性更强的解决方案在改进许多方面的局限性和开销，如扫描、迁移、分配、参数调优和任务调度，以优化性能、QoS和资源利用率。这项研究将允许为快速变化的工作负载设计更好的分层存储系统。将机器学习应用于分层存储的综合方法将产生新的指导方针，并对依赖于处理大量数据的许多领域产生重大影响。该项目将通过计算机科学项目与本科生和研究生分享研究结果，并为未被充分代表的群体和第一代大学生提供就业机会。该项目将向工业界传播拟议的技术，并通过新的工业合作促进技术转让。开发的基础设施将通过一个基于网络的门户提供给研究界。本研究通过解决不断发展的大规模内存密集型应用程序所带来的主要挑战，为系统- ml协同设计空间做出了经验贡献。具体来说，它提出了以下方面的知识状态：(1)如何设计和开发基于机器学习的动态内存分层系统，以确保正确的数据在正确的时间处于正确的分层中；(2)如何在每个机器学习设计决策中使用专家领域知识，从而使最终模型的效率和开销在实际系统中易于管理和有用？(3)如何设计允许用户修改分层存储系统的内部资源和参数的交互框架；（四）如何让新手能因应工作负荷及数据处理需求，配置分层记忆体系统，以取得较高的效能及资源利用率？(5)如何推导机器学习模型来预测未来的工作负载模式，并相应地提前配置分层存储系统以获得更好的性能？因此，为了设计可持续的基于ML的分层存储系统，通过使用不同的ML模型进行各种设计决策来探索性能，QoS和资源利用率之间的权衡是非常重要的。该奖项反映了美国国家科学基金会的法定使命，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。

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.