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Posted on • Originally published at kubernetes.io on

Blog: Kubernetes Memory Manager moves to beta

Authors: Artyom Lukianov (Red Hat), Cezary Zukowski (Samsung)

The blog post explains some of the internals of the Memory manager, a beta feature of Kubernetes 1.22. In Kubernetes, the Memory Manager is akubelet subcomponent. The memory manage provides guaranteed memory (and hugepages) allocation for pods in the Guaranteed QoS class.

This blog post covers:

  1. Why do you need it?
  2. The internal details of how the MemoryManager works
  3. Current limitations of the MemoryManager
  4. Future work for the MemoryManager

Why do you need it?

Some Kubernetes workloads run on nodes withnon-uniform memory access (NUMA). Suppose you have NUMA nodes in your cluster. In that case, you'll know about the potential for extra latency when compute resources need to access memory on the different NUMA locality.

To get the best performance and latency for your workload, container CPUs, peripheral devices, and memory should all be aligned to the same NUMA locality. Before Kubernetes v1.22, the kubelet already provided a set of managers to align CPUs and PCI devices, but you did not have a way to align memory. The Linux kernel was able to make best-effort attempts to allocate memory for tasks from the same NUMA node where the container is executing are placed, but without any guarantee about that placement.

How does it work?

The memory manager is doing two main things:

  • provides the topology hint to the Topology Manager
  • allocates the memory for containers and updates the state

The overall sequence of the Memory Manager under the Kubelet

MemoryManagerDiagram

During the Admission phase:

  1. When first handling a new pod, the kubelet calls the TopologyManager's Admit() method.
  2. The Topology Manager is calling GetTopologyHints() for every hint provider including the Memory Manager.
  3. The Memory Manager calculates all possible NUMA nodes combinations for every container inside the pod and returns hints to the Topology Manager.
  4. The Topology Manager calls to Allocate() for every hint provider including the Memory Manager.
  5. The Memory Manager allocates the memory under the state according to the hint that the Topology Manager chose.

During Pod creation:

  1. The kubelet calls PreCreateContainer().
  2. For each container, the Memory Manager looks the NUMA nodes where it allocated the memory for the container and then returns that information to the kubelet.
  3. The kubelet creates the container, via CRI, using a container specification that incorporates information from the Memory Manager information.

Let's talk about the configuration

By default, the Memory Manager runs with the None policy, meaning it will just relax and not do anything. To make use of the Memory Manager, you should set two command line options for the kubelet:

  • --memory-manager-policy=Static
  • --reserved-memory="<numaNodeID>:<resourceName>=<quantity>"

The value for --memory-manager-policy is straightforward: Static. Deciding what to specify for --reserved-memory takes more thought. To configure it correctly, you should follow two main rules:

  • The amount of reserved memory for the memory resource must be greater than zero.
  • The amount of reserved memory for the resource type must be equal to NodeAllocatable(kube-reserved + system-reserved + eviction-hard) for the resource. You can read more about memory reservations in Reserve Compute Resources for System Daemons.

Reserved memory

Current limitations

The 1.22 release and promotion to beta brings along enhancements and fixes, but the Memory Manager still has several limitations.

Single vs Cross NUMA node allocation

The NUMA node can not have both single and cross NUMA node allocations. When the container memory is pinned to two or more NUMA nodes, we can not know from which NUMA node the container will consume the memory.

Single vs Cross NUMA allocation

  1. The container1 started on the NUMA node 0 and requests 5Gi of the memory but currently is consuming only 3Gi of the memory.
  2. For container2 the memory request is 10Gi, and no single NUMA node can satisfy it.
  3. The container2 consumes 3.5Gi of the memory from the NUMA node 0, but once the container1 will require more memory, it will not have it, and the kernel will kill one of the containers with the OOM error.

To prevent such issues, the Memory Manager will fail the admission of the container2 until the machine has two NUMA nodes without a single NUMA node allocation.

Works only for Guaranteed pods

The Memory Manager can not guarantee memory allocation for Burstable pods, also when the Burstable pod has specified equal memory limit and request.

Let's assume you have two Burstable pods: pod1 has containers with equal memory request and limits, and pod2 has containers only with a memory request set. You want to guarantee memory allocation for the pod1. To the Linux kernel, processes in either pod have the same OOM score, once the kernel finds that it does not have enough memory, it can kill processes that belong to pod pod1.

Memory fragmentation

The sequence of Pods and containers that start and stop can fragment the memory on NUMA nodes. The alpha implementation of the Memory Manager does not have any mechanism to balance pods and defragment memory back.

Future work for the Memory Manager

We do not want to stop with the current state of the Memory Manager and are looking to make improvements, including in the following areas.

Make the Memory Manager allocation algorithm smarter

The current algorithm ignores distances between NUMA nodes during the calculation of the allocation. If same-node placement isn't available, we can still provide better performance compared to the current implementation, by changing the Memory Manager to prefer the closest NUMA nodes for cross-node allocation.

Reduce the number of admission errors

The default Kubernetes scheduler is not aware of the node's NUMA topology, and it can be a reason for many admission errors during the pod start. We're hoping to add a KEP (Kubernetes Enhancement Proposal) to cover improvements in this area. Follow Topology aware scheduler plugin in kube-scheduler to see how this idea progresses.

Conclusion

With the promotion of the Memory Manager to beta in 1.22, we encourage everyone to give it a try and look forward to any feedback you may have. While there are still several limitations, we have a set of enhancements planned to address them and look forward to providing you with many new features in upcoming releases. If you have ideas for additional enhancements or a desire for certain features, please let us know. The team is always open to suggestions to enhance and improve the Memory Manager. We hope you have found this blog informative and helpful! Let us know if you have any questions or comments.

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