Resource Allocation for High-Volume Streaming Data in Data Centers

数据中心大容量流数据的资源分配

• 批准号: DP200100005
• 负责人: Prof Zahir Tari
• 金额: $ 26.33万
• 依托单位: RMIT University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-10-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200100005
• 关键词: Resource; Allocation; Volume; Streaming; Data

Almost all chip vendors are producing new hardware accelerators by combining several units into a single main-board, and therefore making the execution of parallel and distributed run-time primitives not efficient/scalable. This project aims to develop innovative ways to building incremental and iterative computations over massive data sets in a cluster of heterogeneous systems. This will provide a significant reduction of performance bottlenecks when running heavily distributed data-driven applications. Expected outcomes will include resource management algorithms that optimise performance at large scale. The project will benefit many areas, including running stateful iterative stream-based data-analysis applications in data centres.

几乎所有的芯片供应商都在生产新的硬件加速器，将几个单元组合到一个主板上，因此使并行和分布式运行时原语的执行效率低下/可扩展性差。该项目旨在开发创新的方法，在异构系统集群中的海量数据集上构建增量和迭代计算。当运行高度分布式的数据驱动应用程序时，这将大大减少性能瓶颈。预期成果将包括大规模优化性能的资源管理算法。该项目将使许多领域受益，包括在数据中心运行有状态的迭代流数据分析应用程序。