论文标题
画布：在远程内存上多应用的隔离和自适应交换
Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote Memory
论文作者
论文摘要
数据中心应用程序的远程存储技术最近获得了广泛的广泛性。现有的远程内存技术仅关注单个应用程序设置的效率。但是，当多个应用程序在远程记忆系统上共同运行时，可能会发生重大干扰，即使授予每个应用程序相同数量的物理资源，也会导致意外的放缓。这种放缓源于应用程序交换数据路径中的大量共享。画布是一个重新设计的交换系统，可完全隔离远程记忆应用程序的交换路径。帆布允许每个应用程序具有其专用交换分区，交换缓存，预摘要和RDMA带宽。交换隔离基于每个应用程序自己的访问模式和需求，为自适应优化技术奠定了基础。我们开发了三种这样的技术：（1）自适应交换输入分配，（2）语义意识到预取，以及（3）二维RDMA调度。对一组广泛的应用程序进行了彻底的评估，表明画布可最大程度地减少性能变化并大大降低性能降解。
Remote memory techniques for datacenter applications have recently gained a great deal of popularity. Existing remote memory techniques focus on the efficiency of a single application setting only. However, when multiple applications co-run on a remote-memory system, significant interference could occur, resulting in unexpected slowdowns even if the same amounts of physical resources are granted to each application. This slowdown stems from massive sharing in applications' swap data paths. Canvas is a redesigned swap system that fully isolates swap paths for remote-memory applications. Canvas allows each application to possess its dedicated swap partition, swap cache, prefetcher, and RDMA bandwidth. Swap isolation lays a foundation for adaptive optimization techniques based on each application's own access patterns and needs. We develop three such techniques: (1) adaptive swap entry allocation, (2) semantics-aware prefetching, and (3) two-dimensional RDMA scheduling. A thorough evaluation with a set of widely-deployed applications demonstrates that Canvas minimizes performance variation and dramatically reduces performance degradation.
