CBMM: Financial Advice for Kernel Memory Managers


Mark Mansi, Bijan Tabatabai, and Michael M. Swift, University of Wisconsin - Madison


First-party datacenter workloads present new challenges to kernel memory management (MM), which allocates and maps memory and must balance competing performance concerns in an increasingly complex environment. In a datacenter, performance must be both good \textit{and} consistent to satisfy service-level objectives. Unfortunately, current MM designs often exhibit inconsistent, opaque behavior that is difficult to reproduce, decipher, or fix, stemming from (1) a lack of high-quality information for policymaking, (2) the cost-unawareness of many current MM policies, and (3) opaque and distributed policy implementations that are hard to debug. For example, the Linux huge page implementation is distributed across 8 files and can lead to page fault latencies in the 100s of ms.

In search of a MM design that has consistent behavior, we designed Cost-Benefit MM (CBMM), which uses empirically based cost-benefit models and pre-aggregated profiling information to make MM policy decisions. In CBMM, policy decisions follow the guiding principle that \textit{userspace benefits must outweigh userspace costs}. This approach balances the performance benefits obtained by a kernel policy against the cost of applying it. CBMM has competitive performance with Linux and HawkEye, a recent research system, for all the workloads we ran, and in the presence of fragmentation, CBMM is 35% faster than Linux on average. Meanwhile, CBMM nearly always has better tail latency than Linux or HawkEye, particularly on fragmented systems. It reduces the cost of the most expensive soft page faults by 2-3 orders of magnitude for most of our workloads, and reduces the frequency of 10-1000 mu s-long faults by around 2 orders of magnitude for multiple workloads.

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@inproceedings {280736,
author = {Mark Mansi and Bijan Tabatabai and Michael M. Swift},
title = {{CBMM}: Financial Advice for Kernel Memory Managers},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-37},
address = {Carlsbad, CA},
pages = {593--608},
url = {https://www.usenix.org/conference/atc22/presentation/mansi},
publisher = {USENIX Association},
month = jul

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