CAREER: Optimizing Cloud-Native Databases for Storage Disaggregation

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。数据库是管理不断增长的数据量的关键软件系统。传统数据库在用户控制的硬件中运行，而现代数据库系统由于硬件和软件管理的复杂性和成本降低而迁移到云。与传统的本地数据库相比，这种云原生数据库具有独特的体系结构特性，例如高可伸缩性、弹性和可用性。这些新的体系结构特性为性能优化带来了新的挑战和机遇。该项目将研究新的算法和系统设计，以更好地利用云中的独特架构，并提高性能、成本效益和可靠性。该项目将促进对云原生数据库的理解，并为整个社会带来重大价值。该项目将建立已开发技术的开源原型，以支持未来的研究。该计划亦会使数据库课程现代化，包括云端数据库的实践练习，以解决该领域人才严重短缺的问题，并向公众推广STEM教育。现代云原生数据库采用独特的存储分解架构，将计算和存储作为两个独立的服务层解耦，然后通过数据中心网络连接起来。分解使存储层可以独立于计算进行扩展，从而实现灵活的资源分配和降低成本，但由于网络的高延迟和低带宽，导致连接两层的网络成为新的性能瓶颈。该项目旨在通过将某些数据库功能（如过滤、聚合、原子比较和交换等）下推到存储层来解决网络瓶颈，以减少计算和存储之间的交互量，同时仍然充分利用分解的好处。该项目重新审视了数据库设计的传统智慧，并专注于优化最容易受到分解瓶颈影响的构建块，包括查询执行、查询优化、分布式事务和实时数据分析。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Databases are critical software systems that manage the ever-growing volumes of data. While conventional databases run in user-controlled hardware, modern database systems are migrating to the cloud due to reduced complexity and cost in hardware and software management. Such cloud-native databases have unique architectural features compared to conventional on-premises databases, such as high scalability, elasticity, and availability. These new architectural features bring new challenges and opportunities for performance optimizations. This project will investigate new algorithms and system designs that better leverage the unique architectures in the cloud, and improve performance, cost-effectiveness, and reliability. The project will advance the understanding of cloud-native databases and add significant value to society at large. The project will build open-source prototypes of developed techniques to enable future research. The project will also modernize database courses to include hands-on exercises on cloud-native databases to resolve the severe shortage of talent in the field and promote STEM education to the public. Modern cloud-native databases adopt a unique storage-disaggregation architecture, where the computation and storage are decoupled as two separate layers of services and then connected through the data center network. Disaggregation enables the storage layer to scale independently from computation, which allows for flexible resource allocation and cost reduction, but causes the network connecting the two layers to be a new performance bottleneck due to its high latency and low bandwidth. This project aims to resolve the network bottleneck by pushing certain database functions (such as filtering, aggregation, atomic compare and swap, etc.) down into the storage layer to reduce the amount of interaction between computation and storage, while still fully leveraging the benefits of disaggregation. The project revisits the conventional wisdom of database design and focuses on optimizing the building blocks that are the most susceptible to the disaggregation bottleneck, including query execution, query optimization, distributed transaction, and real-time data analytics.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.