On Tue, Nov 26, 2024 at 06:57:19PM -0800, Yuanchu Xie wrote:

This patch series provides workingset reporting of user pages in lruvecs, of which coldness can be tracked by accessed bits and fd references. However, the concept of workingset applies generically to all types of memory, which could be kernel slab caches, discardable userspace caches (databases), or CXL.mem. Therefore, data sources might come from slab shrinkers, device drivers, or the userspace. Another interesting idea might be hugepage workingset, so that we can measure the proportion of hugepages backing cold memory. However, with architectures like arm, there may be too many hugepage sizes leading to a combinatorial explosion when exporting stats to the userspace. Nonetheless, the kernel should provide a set of workingset interfaces that is generic enough to accommodate the various use cases, and extensible to potential future use cases.

Doesn't DAMON already provide this information?

CCing SJ.

Use cases

Job scheduling On overcommitted hosts, workingset information improves efficiency and reliability by allowing the job scheduler to have better stats on the exact memory requirements of each job. This can manifest in efficiency by landing more jobs on the same host or NUMA node. On the other hand, the job scheduler can also ensure each node has a sufficient amount of memory and does not enter direct reclaim or the kernel OOM path. With workingset information and job priority, the userspace OOM killing or proactive reclaim policy can kick in before the system is under memory pressure. If the job shape is very different from the machine shape, knowing the workingset per-node can also help inform page allocation policies.

Proactive reclaim Workingset information allows the a container manager to proactively reclaim memory while not impacting a job's performance. While PSI may provide a reactive measure of when a proactive reclaim has reclaimed too much, workingset reporting allows the policy to be more accurate and flexible.

I'm not sure about more accurate.

Access frequency is only half the picture. Whether you need to keep memory with a given frequency resident depends on the speed of the backing device.

There is memory compression; there is swap on flash; swap on crappy flash; swapfiles that share IOPS with co-located filesystems. There is zswap+writeback, where avg refault speed can vary dramatically.

You can of course offload much more to a fast zswap backend than to a swapfile on a struggling flashdrive, with comparable app performance.

So I think you'd be hard pressed to achieve a high level of accuracy in the usecases you list without taking the (often highly dynamic) cost of paging / memory transfer into account.

There is a more detailed discussion of this in a paper we wrote on proactive reclaim/offloading - in 2.5 Hardware Heterogeneity:

https://www.cs.cmu.edu/~dskarlat/publications/tmo_asplos22.pdf

Ballooning (similar to proactive reclaim) The last patch of the series extends the virtio-balloon device to report the guest workingset. Balloon policies benefit from workingset to more precisely determine the size of the memory balloon. On end-user devices where memory is scarce and overcommitted, the balloon sizing in multiple VMs running on the same device can be orchestrated with workingset reports from each one. On the server side, workingset reporting allows the balloon controller to inflate the balloon without causing too much file cache to be reclaimed in the guest.

Promotion/Demotion If different mechanisms are used for promition and demotion, workingset information can help connect the two and avoid pages being migrated back and forth. For example, given a promotion hot page threshold defined in reaccess distance of N seconds (promote pages accessed more often than every N seconds). The threshold N should be set so that ~80% (e.g.) of pages on the fast memory node passes the threshold. This calculation can be done with workingset reports. To be directly useful for promotion policies, the workingset report interfaces need to be extended to report hotness and gather hotness information from the devices[1].

[1] https://www.opencompute.org/documents/ocp-cms-hotness-tracking-requirements-...

Sysfs and Cgroup Interfaces

The interfaces are detailed in the patches that introduce them. The main idea here is we break down the workingset per-node per-memcg into time intervals (ms), e.g.

1000 anon=137368 file=24530 20000 anon=34342 file=0 30000 anon=353232 file=333608 40000 anon=407198 file=206052 9223372036854775807 anon=4925624 file=892892

Implementation

The reporting of user pages is based off of MGLRU, and therefore requires CONFIG_LRU_GEN=y. We would benefit from more MGLRU generations for a more fine-grained workingset report, but we can already gather a lot of data with just four generations. The workingset reporting mechanism is gated behind CONFIG_WORKINGSET_REPORT, and the aging thread is behind CONFIG_WORKINGSET_REPORT_AGING.

Benchmarks

Ghait Ouled Amar Ben Cheikh has implemented a simple policy and ran Linux compile and redis benchmarks from openbenchmarking.org. The policy and runner is referred to as WMO (Workload Memory Optimization). The results were based on v3 of the series, but v4 doesn't change the core of the working set reporting and just adds the ballooning counterpart.

The timed Linux kernel compilation benchmark shows improvements in peak memory usage with a policy of "swap out all bytes colder than 10 seconds every 40 seconds". A swapfile is configured on SSD.

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peak memory usage (with WMO): 4982.61328 MiB peak memory usage (control): 9569.1367 MiB peak memory reduction: 47.9%

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Benchmark | Experimental |Control | Experimental_Std_Dev | Control_Std_Dev Timed Linux Kernel Compilation - allmodconfig (sec) | 708.486 (95.91%) | 679.499 (100%) | 0.6% | 0.1%

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Seconds, fewer is better

You can do this with a recent (>2018) upstream kernel and ~100 lines of python [1]. It also works on both LRU implementations.

[1] https://github.com/facebookincubator/senpai

We use this approach in virtually the entire Meta fleet, to offload unneeded memory, estimate available capacity for job scheduling, plan future capacity needs, and provide accurate memory usage feedback to application developers.

It works over a wide variety of CPU and storage configurations with no specific tuning.

The paper I referenced above provides a detailed breakdown of how it all works together.

I would be curious to see a more in-depth comparison to the prior art in this space. At first glance, your proposal seems more complex and less robust/versatile, at least for offloading and capacity gauging.

It does provide more detailed insight into userspace memory behavior, which could be helpful when trying to make sense of applications that sit on a rich layer of libraries and complicated runtimes. But here a comparison to DAMON would be helpful.

25 files changed, 2482 insertions(+), 9 deletions(-) create mode 100644 Documentation/admin-guide/mm/workingset_report.rst create mode 100644 include/linux/workingset_report.h create mode 100644 mm/workingset_report.c create mode 100644 mm/workingset_report_aging.c create mode 100644 tools/testing/selftests/mm/workingset_report.c create mode 100644 tools/testing/selftests/mm/workingset_report.h create mode 100644 tools/testing/selftests/mm/workingset_report_test.c