Serverless Data Management Primitives for Software-defined Composable Systems

数据管理的科学和商业领域在过去二十年中经历了重大的推动。“数据是新石油”引发了数据管理系统各个领域的研究和创新，从理论到系统相关工作，再到数据密集型应用。除了新颖的数据管理架构，研究社区还探索了现代硬件的有效使用，如GPU， FPGA， RDMA等。然而，在这个项目中，我们计划采取下一步，并针对即将到来的硬件技术，目前在可组合系统的背景下讨论。可组合系统是软件定义的计算机，直接“软连接”了一组真实的硬件组件，从而完全模糊了传统的Scale-Out或Scale-Up架构的界限。由于这样的“逻辑”系统可以动态地重新组合，每个算法可以通过立即添加/删除计算单元，更改内存和存储分配以及修改内存和数据背板的拓扑来定义“最佳”组合。传统上，软件努力优化现有（硬连接）硬件的使用，而现在的目标是确定软件和（软连接）硬件配置的最佳组合。这种软件定义的基础设施的特点需要从根本上重新思考许多现有的算法和数据结构。能够在应用程序运行时重新组合底层“计算机”的许多方面，为广泛未开发的研究机会开辟了广阔的领域。反映Reinhart Koselleck建议的性质，我们的目标是研究这种“下一代”可组合系统的硬件环境对数据管理原语的设计和体系结构的影响，以有效地运行大规模且事务上正确的查询工作负载。到目前为止，我们还没有发现任何专门针对可组合系统的数据管理研究项目。我们努力产生国际可见的结果，并期望在ACM SIGMOD， VLDB (PVLDB)， IEEE ICDE等国际会议以及ACM TODS， VLDB Journal和IEEE数据工程公告等期刊上发表和演示。

The scientific as well as the commercial domain of data management has experienced a significant push within the last two decades. "Data as the new oil" has sparked research and innovation in all areas of data management systems, from theory via system-related work to data-intensive applications. In addition to novel data management architectures, the research community also explored the efficient use of modern hardware like GPU, FPGA, RDMA, etc.Within this project however, we plan to take the next step and target an upcoming hardware technology currently discussed in the context of Composable Systems. Composable Systems are software-defined computers directly "soft-wiring" a collection of real hardware components and thus completely blurring the boundaries of traditional Scale-Out or Scale-Up architectures. Since such a "Logical" system may be dynamically re-composed, each algorithm may define the "optimal" composition by instantly adding/removing computing units, changing memory and storage assignments, and modifying the topology of the memory and data backplane. While software is traditionally striving to optimally exploit the use of existing (hard-wired) hardware, the goal is now to identify the optimal combination of a software and (soft-wired) hardware configuration. The characteristics of such software-defined infrastructures require a fundamental re-thinking of many existing algorithms and data structures. Being able to re-compose many aspects of an underlying "Computer" during runtime (!) of an application opens up a large field of widely unexplored research opportunities. Reflecting the nature of a Reinhart Koselleck proposal, we aim at investigating the impact of such "next-next"-generation hardware environments of Composable Systems on the design and architecture of data management primitives to efficiently run large-scale but also transactionally correct query workloads. As of now, we are not aware of any dedicated data management research project with this particular focus on Composable Systems. We strive to produce internationally visible results and expect publications and presentations at international conferences like ACM SIGMOD, VLDB (PVLDB), IEEE ICDE as well as within journals like ACM TODS, VLDB Journal, and IEEE Data Engineering Bulletin.