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Production-Grade Container Scheduling and Management
99c57828ce
For the 'single-numa' and 'restricted' TopologyManager policies, pods are only admitted if all of their containers have perfect alignment across the set of resources they are requesting. The best-effort policy, on the other hand, will prefer allocations that have perfect alignment, but fall back to a non-preferred alignment if perfect alignment can't be achieved. The existing algorithm of how to choose the best hint from the set of "non-preferred" hints is fairly naive and often results in choosing a sub-optimal hint. It works fine in cases where all resources would end up coming from a single NUMA node (even if its not the same NUMA nodes), but breaks down as soon as multiple NUMA nodes are required for the "best" alignment. We will never be able to achieve perfect alignment with these non-preferred hints, but we should try and do something more intelligent than simply choosing the hint with the narrowest mask. In an ideal world, we would have the TopologyManager return a set of "resources-relative" hints (as opposed to a common hint for all resources as is done today). Each resource-relative hint would indicate how many other resources could be aligned to it on a given NUMA node, and a hint provider would use this information to allocate its resources in the most aligned way possible. There are likely some edge cases to consider here, but such an algorithm would allow us to do partial-perfect-alignment of "some" resources, even if all resources could not be perfectly aligned. Unfortunately, supporting something like this would require a major redesign to how the TopologyManager interacts with its hint providers (as well as how those hint providers make decisions based on the hints they get back). That said, we can still do better than the naive algorithm we have today, and this patch provides a mechanism to do so. We start by looking at the set of hints passed into the TopologyManager for each resource and generate a list of the minimum number of NUMA nodes required to satisfy an allocation for a given resource. Each entry in this list then contains the 'minNUMAAffinity.Count()' for a given resources. Once we have this list, we find the *maximum* 'minNUMAAffinity.Count()' from the list and mark that as the 'bestNonPreferredAffinityCount' that we would like to have associated with whatever "bestHint" we ultimately generate. The intuition being that we would like to (at the very least) get alignment for those resources that *require* multiple NUMA nodes to satisfy their allocation. If we can't quite get there, then we should try to come as close to it as possible. Once we have this 'bestNonPreferredAffinityCount', the algorithm proceeds as follows: If the mergedHint and bestHint are both non-preferred, then try and find a hint whose affinity count is as close to (but not higher than) the bestNonPreferredAffinityCount as possible. To do this we need to consider the following cases and react accordingly: 1. bestHint.NUMANodeAffinity.Count() > bestNonPreferredAffinityCount 2. bestHint.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount 3. bestHint.NUMANodeAffinity.Count() < bestNonPreferredAffinityCount For case (1), the current bestHint is larger than the bestNonPreferredAffinityCount, so updating to any narrower mergeHint is preferred over staying where we are. For case (2), the current bestHint is equal to the bestNonPreferredAffinityCount, so we would like to stick with what we have *unless* the current mergedHint is also equal to bestNonPreferredAffinityCount and it is narrower. For case (3), the current bestHint is less than bestNonPreferredAffinityCount, so we would like to creep back up to bestNonPreferredAffinityCount as close as we can. There are three cases to consider here: 3a. mergedHint.NUMANodeAffinity.Count() > bestNonPreferredAffinityCount 3b. mergedHint.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount 3c. mergedHint.NUMANodeAffinity.Count() < bestNonPreferredAffinityCount For case (3a), we just want to stick with the current bestHint because choosing a new hint that is greater than bestNonPreferredAffinityCount would be counter-productive. For case (3b), we want to immediately update bestHint to the current mergedHint, making it now equal to bestNonPreferredAffinityCount. For case (3c), we know that *both* the current bestHint and the current mergedHint are less than bestNonPreferredAffinityCount, so we want to choose one that brings us back up as close to bestNonPreferredAffinityCount as possible. There are three cases to consider here: 3ca. mergedHint.NUMANodeAffinity.Count() > bestHint.NUMANodeAffinity.Count() 3cb. mergedHint.NUMANodeAffinity.Count() < bestHint.NUMANodeAffinity.Count() 3cc. mergedHint.NUMANodeAffinity.Count() == bestHint.NUMANodeAffinity.Count() For case (3ca), we want to immediately update bestHint to mergedHint because that will bring us closer to the (higher) value of bestNonPreferredAffinityCount. For case (3cb), we want to stick with the current bestHint because choosing the current mergedHint would strictly move us further away from the bestNonPreferredAffinityCount. Finally, for case (3cc), we know that the current bestHint and the current mergedHint are equal, so we simply choose the narrower of the 2. This patch implements this algorithm for the case where we must choose from a set of non-preferred hints and provides a set of unit-tests to verify its correctness. Signed-off-by: Kevin Klues <kklues@nvidia.com> |
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Kubernetes (K8s)
Kubernetes, also known as K8s, is an open source system for managing containerized applications across multiple hosts. It provides basic mechanisms for deployment, maintenance, and scaling of applications.
Kubernetes builds upon a decade and a half of experience at Google running production workloads at scale using a system called Borg, combined with best-of-breed ideas and practices from the community.
Kubernetes is hosted by the Cloud Native Computing Foundation (CNCF). If your company wants to help shape the evolution of technologies that are container-packaged, dynamically scheduled, and microservices-oriented, consider joining the CNCF. For details about who's involved and how Kubernetes plays a role, read the CNCF announcement.
To start using K8s
See our documentation on kubernetes.io.
Try our interactive tutorial.
Take a free course on Scalable Microservices with Kubernetes.
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The community repository hosts all information about building Kubernetes from source, how to contribute code and documentation, who to contact about what, etc.
If you want to build Kubernetes right away there are two options:
You have a working Go environment.
mkdir -p $GOPATH/src/k8s.io
cd $GOPATH/src/k8s.io
git clone https://github.com/kubernetes/kubernetes
cd kubernetes
make
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git clone https://github.com/kubernetes/kubernetes
cd kubernetes
make quick-release
For the full story, head over to the developer's documentation.
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