xTier: Intelligent Tiering for CXL-Enabled Memory

2026-09-22

AI Summary

针对CXL内存的页面放置问题，提出了一种基于内核的学习型分层系统xTier，通过eBPF程序和紧凑量化MLP模型来优化页面评分与分配。

Abstract

CXL-enabled memory expands server memory capacity, but introduces a page-placement problem: the operating system must decide which pages should reside in DRAM and which should reside on slower CXL memory. Existing systems make this tradeoff in one of two ways. Userspace controllers support flexible policies, but expose placement decisions to scheduler jitter and kernel-userspace crossing overhead. Kernel-space systems avoid this latency, but rely on fixed heuristics that must generalize across workloads. We present xTier, a kernel-resident learned memory-tiering system. xTier attaches eBPF programs to PEBS events and uses a compact quantized MLP to score sampled pages inside the kernel at microsecond-scale latency. Rather than reacting to every candidate, xTier converges to a low-churn placement for the current workload phase, reduces sampling cost after convergence, and returns to a higher sampling cadence when the workload shifts. We evaluate xTier on six memory-bound workloads at DRAM:CXL ratios from 1:5 to 1:25. The advantage grows as the DRAM budget tightens. At 1:15 and beyond, xTier is the fastest system in 14 of 18 configurations. Where it is not fastest, it trails the best baseline by 3.9% on average. It reaches this performance while moving 13% fewer pages in geometric mean, and 22% fewer at the tighter ratios. When a workload changes phase, xTier rebuilds its hot set in DRAM faster and more completely than any baseline.

Problem

• CXL-enabled memory
• page-placement problem
• DRAM
• operating system

Innovation

• learned memory-tiering
• compact quantized MLP
• microsecond-scale latency
• low-churn placement
• workload phase