Container-aware heap sizing for OpenJDK

[Kirk Pepperdine]

Hi Jonathan,

Very interesting experiment. This sizing issue is something that is
befuddling a significant portion of those responsible for
deploying containerized Java applications. Lio nicely points out that the
old goal of "play nice" when configuring memory is in conflict with the new
goal of "be greedy". Thus a re-visiting of memory sizing ergonomics is
something that I certainly welcome. The cloud providers have been
interested in better (for some weakly definition of better) memory resizing
dynamics for quite some time so also a hot button topic.

I'm not sure how much I have to add over what others have commented on but,
I don't believe we need an inter-process communication, at least not in the
first instance nor do we need a watcher thread (again, at least not in the
first instance). The one thing that I see here, if I'm reading this
correctly, is that there is a focus on total heap size. For generational
collectors, like G1, young and tenured play two different roles and thus
require different tuning strategies. Tuning young is about controlling the
promotion of transients into tenured. The two big things that drive
transients into tenured are undersized survivor space and frequency
collections (accelerated aging). Thus young sizing should be heavily
influenced by allocation rates. This is considerably different than tenured
where the driving metric is live set size (LSS). Thus tenured should be
LSS + some working space. From this, it follows that max heap will be the
sum of the parts. From your description here, you're using CPU (GC
overheads) to help you resize. Do you mind elaborating on how this works?

Another side note is that you mention sizing is trial and error where you
start small and then make bigger as needed. Might I suggest that a quicker
way is to start large and then resize to smaller. The reason for doing this
is because small clips the signals you need to look at to know how big
things need to be. Starting big should give you a cleaner, unclipped signal
to work with.

Kind regards,
Kirk


On Tue, Sep 13, 2022 at 12:17 PM Jonathan Joo <jonathanjoo at google.com>
wrote:

> Hello hotspot-dev and hotspot-gc-dev,
>
> My name is Jonathan, and I'm working on the Java Platform Team at Google.
> Here, we are working on a project to address Java container memory issues,
> as we noticed that a significant number of Java servers hit container OOM
> issues due to people incorrectly tuning their heap size with respect to the
> container size. Because our containers have other RAM consumers which
> fluctuate over time, it is often difficult to determine a priori what is an
> appropriate Xmx to set for a particular server.
>
> We set about trying to solve this by dynamically adjusting the Java
> heap/gc behavior based on the container usage information that we pass into
> the JVM. We have seen promising results so far, reducing container OOMs by
> a significant amount, and oftentimes also reducing average heap usage (with
> the tradeoff of more CPU time spent doing GC).
>
> Below (under the dotted line) is a more detailed explanation of our
> initial approach. Does this sound like something that may be useful for the
> general OpenJDK community? If so, would some of you be open to further
> discussion? I would also like to better understand what container
> environments look like outside of Google, to see how we could modify our
> approach for the more general case.
>
> Thank you!
>
>
> Jonathan
> ------------------------------------------------------------------------
> Introduction:
>
> Adaptable Heap Sizing (AHS) is a project internal to Google that is meant
> to simplify configuration and improve the stability of applications in
> container environments. The key is that in a containerized environment, we
> have access to container usage and limit information. This can be used as a
> signal to modify Java heap behavior, helping prevent container OOMs.
> Problem:
>
>    -
>
>    Containers at Google must be properly sized to not only the JVM heap,
>    but other memory consumers as well. These consumers include non-heap Java
>    (e.g. native code allocations), and simultaneously running non-Java
>    processes.
>    -
>
>    Common antipattern we see here at Google:
>    -
>
>       We have an application running into container OOMs.
>       -
>
>       An engineer raises both container memory limit and Xmx by the same
>       amount, since there appears to be insufficient memory.
>       -
>
>       The application has reduced container OOMs, but is still prone to
>       them, since G1 continues to use most of Xmx.
>       -
>
>    This results in many jobs being configured with much more RAM than
>    they need, but still running into container OOM issues.
>
> Hypothesis:
>
>    -
>
>    For preventing container OOM: Why can't heap expansions be bounded by
>    the remaining free space in the container?
>    -
>
>    For preventing the `unnecessarily high Xmx` antipattern: Why can't
>    target heap size be set based on GC CPU overhead?
>    -
>
>    From our work on Adaptable Heap Sizing, it appears they can!
>
> Design:
>
>    -
>
>    We add two manageable flags in the JVM
>    -
>
>       Current maximum heap expansion size
>       -
>
>       Current target heap size
>       -
>
>    A separate thread runs alongside the JVM, querying:
>    -
>
>       Container memory usage/limits
>       -
>
>       GC CPU overhead metrics from the JVM.
>       -
>
>    This thread then uses this information to calculate new values for the
>    two new JVM flags, and continually updates them at runtime.
>    -
>
>    The `Current maximum heap expansion size` informs the JVM what is the
>    maximum amount we can expand the heap by, while staying within container
>    limits. This is a hard limit, and trying to expand more than this amount
>    results in behavior equivalent to hitting the Xmx limit.
>    -
>
>    The `Current target heap size` is a soft target value, which is used to
>    resize the heap (when possible) so as to bring GC CPU overhead toward its
>    target value.
>
>
> Results:
>
>    -
>
>    At Google, we have found that this design works incredibly well in our
>    initial rollout, even for large and complex workloads.
>    -
>
>    After deploying this to dozens of applications:
>    -
>
>       Significant memory savings for previously misconfigured jobs (many
>       of which reduced their heap usage by 50% or more)
>       -
>
>       Significantly reduced occurrences of container OOM (100% reduction
>       in vast majority of cases)
>       -
>
>       No correctness issues
>       -
>
>       No latency regressions*
>       -
>
>       We plan to deploy AHS across a much wider subset of applications by
>       EOY '22.
>
>
> *Caveats:
>
>    - Enabling this feature might require tuning of the newly introduced
>    default GC CPU overhead target to avoid regressions.
>    -
>
>    Time spent doing GC for an application may increase significantly
>    (though generally we've seen in practice that even if this is the case,
>    end-to-end latency does not increase a noticeable amount)
>    -
>
>    Enabling AHS results in frequent heap resizings, but we have not seen
>    evidence of any negative effects as a result of these more frequent heap
>    resizings.
>    -
>
>    AHS is not necessarily a replacement for proper JVM tuning, but should
>    generally work better than an untuned or improperly tuned configuration.
>    -
>
>    AHS is not intended for every possible workload, and there could be
>    pathological cases where AHS results in worse behavior.
>
>

-- 
Kind regards,
Kirk Pepperdine

http://www.kodewerk.com
http://www.javaperformancetuning.com
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