论文标题
池塘：云平台的基于CXL的内存池化系统
Pond: CXL-Based Memory Pooling Systems for Cloud Platforms
论文作者
论文摘要
公共云提供商寻求满足严格的性能要求和低硬件成本。性能和成本的关键驱动力是主要内存。记忆池有望改善DRAM利用率，从而降低成本。但是，在云性能要求下，合并具有挑战性。本文提出了池塘，这是第一个符合云性能目标并大大降低DRAM成本的内存集合系统。池塘建立在Compute Express Link（CXL）标准上，用于负载/存储对池存储器的访问和两个关键见解。首先，我们对云生产痕迹的分析表明，在8-16个插座上汇集足以实现大部分好处。这样可以实现具有低访问延迟的天池设计。其次，可以创建机器学习模型，以准确预测分配给虚拟机（VM）的本地和池存储器多少，以类似于相同的NUMA节点内存性能。我们使用158次工作量的评估表明，池塘将DRAM成本降低了7％，其性能在同一NUMA节点VM分配的1-5％以内。
Public cloud providers seek to meet stringent performance requirements and low hardware cost. A key driver of performance and cost is main memory. Memory pooling promises to improve DRAM utilization and thereby reduce costs. However, pooling is challenging under cloud performance requirements. This paper proposes Pond, the first memory pooling system that both meets cloud performance goals and significantly reduces DRAM cost. Pond builds on the Compute Express Link (CXL) standard for load/store access to pool memory and two key insights. First, our analysis of cloud production traces shows that pooling across 8-16 sockets is enough to achieve most of the benefits. This enables a small-pool design with low access latency. Second, it is possible to create machine learning models that can accurately predict how much local and pool memory to allocate to a virtual machine (VM) to resemble same-NUMA-node memory performance. Our evaluation with 158 workloads shows that Pond reduces DRAM costs by 7% with performance within 1-5% of same-NUMA-node VM allocations.
