论文标题
模板混音
Templating Shuffles
论文作者
论文摘要
云数据中心正在快速发展。同时，当今的大规模数据分析应用程序需要通常针对应用程序，工作量和数据中心基础架构的非平凡性能调整。我们提出了teshu，这使网络改组了所有数据分析共有的可扩展统一服务层。由于最佳的洗牌取决于无数因素，因此Teshu引入了参数化的洗牌模板，该模板通过准确有效的采样实例化，使Teshu能够动态适应不同的应用程序工作负载和数据中心布局。我们的初步实验结果表明，Teshu有效地实现了改进的优化，以提高性能并适应各种数据中心网络方案。
Cloud data centers are evolving fast. At the same time, today's large-scale data analytics applications require non-trivial performance tuning that is often specific to the applications, workloads, and data center infrastructure. We propose TeShu, which makes network shuffling an extensible unified service layer common to all data analytics. Since an optimal shuffle depends on a myriad of factors, TeShu introduces parameterized shuffle templates, instantiated by accurate and efficient sampling that enables TeShu to dynamically adapt to different application workloads and data center layouts. Our preliminary experimental results show that TeShu efficiently enables shuffling optimizations that improve performance and adapt to a variety of data center network scenarios.
