# Node Resource Managers

Node Resource ManagersIn order to support latency-critical and high-throughput workloads, Kubernetes offers a suite of Resource Managers. The managers aim to co-ordinate and optimise the alignment of node's resources for pods configured with a specific requirement for CPUs, devices, and memory (hugepages) resources.Hardware topology alignment policiesTopology Manager is a kubelet component that aims to coordinate the set of components that are responsible for these optimizations. The overall resource management process is governed using the policy you specify. To learn more, read Control Topology Management Policies on a Node.Policies for assigning CPUs to PodsFEATURE STATE: Kubernetes v1.26 [stable] (enabled by default: true)Once a Pod is bound to a Node, the kubelet on that node may need to either multiplex the existing hardware (for example, sharing CPUs across multiple Pods) or allocate hardware by dedicating some resource (for example, assigning one of more CPUs for a Pod's exclusive use).By default, the kubelet uses CFS quota to enforce pod CPU limits. When the node runs many CPU-bound pods, the workload can move to different CPU cores depending on whether the pod is throttled and which CPU cores are available at scheduling time. Many workloads are not sensitive to this migration and thus work fine without any intervention.However, in workloads where CPU cache affinity and scheduling latency significantly affect workload performance, the kubelet allows alternative CPU management policies to determine some placement preferences on the node. This is implemented using the CPU Manager and its policy. There are two available policies:none: the none policy explicitly enables the existing default CPU affinity scheme, providing no affinity beyond what the OS scheduler does automatically. Limits on CPU usage for Guaranteed pods and Burstable pods are enforced using CFS quota.static: the static policy allows containers in Guaranteed pods with integer CPU requests access to exclusive CPUs on the node. This exclusivity is enforced using the cpuset cgroup controller.Note:System services such as the container runtime and the kubelet itself can continue to run on these exclusive CPUs. The exclusivity only extends to other pods.CPU Manager doesn't support offlining and onlining of CPUs at runtime.Static policyThe static policy enables finer-grained CPU management and exclusive CPU assignment. This policy manages a shared pool of CPUs that initially contains all CPUs in the node. The amount of exclusively allocatable CPUs is equal to the total number of CPUs in the node minus any CPU reservations set by the kubelet configuration. CPUs reserved by these options are taken, in integer quantity, from the initial shared pool in ascending order by physical core ID. This shared pool is the set of CPUs on which any containers in BestEffort and Burstable pods run. Containers in Guaranteed pods with fractional CPU requests also run on CPUs in the shared pool. Only containers that are part of a Guaranteed pod and have integer CPU requests are assigned exclusive CPUs.Note:The kubelet requires a CPU reservation greater than zero when the static policy is enabled. This is because a zero CPU reservation would allow the shared pool to become empty.As Guaranteed pods whose containers fit the requirements for being statically assigned are scheduled to the node, CPUs are removed from the shared pool and placed in the cpuset for the container. CFS quota is not used to bound the CPU usage of these containers as their usage is bound by the scheduling domain itself. In others words, the number of CPUs in the container cpuset is equal to the integer CPU limit specified in the pod spec. This static assignment increases CPU affinity and decreases context switches due to throttling for the CPU-bound workload.Consider the containers in the following pod specs:spec: containers: - name: nginx image: nginx The pod above runs in the BestEffort QoS class because no resource requests or limits are specified. It runs in the shared pool.spec: containers: - name: nginx image: nginx resources: limits: memory: "200Mi" requests: memory: "100Mi" The pod above runs in the Burstable QoS class because resource requests do not equal limits and the cpu quantity is not specified. It runs in the shared pool.spec: containers: - name: nginx image: nginx resources: limits: memory: "200Mi" cpu: "2" requests: memory: "100Mi" cpu: "1" The pod above runs in the Burstable QoS class because resource requests do not equal limits. It runs in the shared pool.spec: containers: - name: nginx image: nginx resources: limits: memory: "200Mi" cpu: "2" requests: memory: "200Mi" cpu: "2" The pod above runs in the Guaranteed QoS class because requests are equal to limits. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.spec: containers: - name: nginx image: nginx resources: limits: memory: "200Mi" cpu: "1.5" requests: memory: "200Mi" cpu: "1.5" The pod above runs in the Guaranteed QoS class because requests are equal to limits. But the container's resource limit for the CPU resource is a fraction. It runs in the shared pool.spec: containers: - name: nginx image: nginx resources: limits: memory: "200Mi" cpu: "2" The pod above runs in the Guaranteed QoS class because only limits are specified and requests are set equal to limits when not explicitly specified. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.Static policy optionsHere are the available policy options for the static CPU management policy, listed in alphabetical order:align-by-socket (alpha, hidden by default)Align CPUs by physical package / socket boundary, rather than logical NUMA boundaries (available since Kubernetes v1.25)distribute-cpus-across-cores (alpha, hidden by default)Allocate virtual cores, sometimes called hardware threads, across different physical cores (available since Kubernetes v1.31)distribute-cpus-across-numa (beta, visible by default)Spread CPUs across different NUMA domains, aiming for an even balance between the selected domains (available since Kubernetes v1.23)full-pcpus-only (GA, visible by default)Always allocate full physical cores (available since Kubernetes v1.22, GA since Kubernetes v1.33)strict-cpu-reservation (beta, visible by default)Prevent all the pods regardless of their Quality of Service class to run on reserved CPUs (available since Kubernetes v1.32)prefer-align-cpus-by-uncorecache (beta, visible by default)Align CPUs by uncore (Last-Level) cache boundary on a best-effort way (available since Kubernetes v1.32)You can toggle groups of options on and off based upon their maturity level using the following feature gates:CPUManagerPolicyBetaOptions (default enabled). Disable to hide beta-level options.CPUManagerPolicyAlphaOptions (default disabled). Enable to show alpha-level options.You will still have to enable each option using the cpuManagerPolicyOptions field in the kubelet configuration file.For more detail about the individual options you can configure, read on.full-pcpus-onlyIf the full-pcpus-only policy option is specified, the static policy will always allocate full physical cores. By default, without this option, the static policy allocates CPUs using a topology-aware best-fit allocation. On SMT enabled systems, the policy can allocate individual virtual cores, which correspond to hardware threads. This can lead to different containers sharing the same physical cores; this behaviour in turn contributes to the noisy neighbours problem. With the option enabled, the pod will be admitted by the kubelet only if the CPU request of all its containers can be fulfilled by allocating full physical cores. If the pod does not pass the admission, it will be put in Failed state with the message SMTAlignmentError.distribute-cpus-across-numaIf the distribute-cpus-across-numapolicy option is specified, the static policy will evenly distribute CPUs across NUMA nodes in cases where more than one NUMA node is required to satisfy the allocation. By default, the CPUManager will pack CPUs onto one NUMA node until it is filled, with any remaining CPUs simply spilling over to the next NUMA node. This can cause undesired bottlenecks in parallel code relying on barriers (and similar synchronization primitives), as this type of code tends to run only as fast as its slowest worker (which is slowed down by the fact that fewer CPUs are available on at least one NUMA node). By distributing CPUs evenly across NUMA nodes, application developers can more easily ensure that no single worker suffers from NUMA effects more than any other, improving the overall performance of these types of applications.align-by-socketIf the align-by-socket policy option is specified, CPUs will be considered aligned at the socket boundary when deciding how to allocate CPUs to a container. By default, the CPUManager aligns CPU allocations at the NUMA boundary, which could result in performance degradation if CPUs need to be pulled from more than one NUMA node to satisfy the allocation. Although it tries to ensure that all CPUs are allocated from the minimum number of NUMA nodes, there is no guarantee that those NUMA nodes will be on the same socket. By directing the CPUManager to explicitly align CPUs at the socket boundary rather than the NUMA boundary, we are able to avoid such issues. Note, this policy option is not compatible with TopologyManager single-numa-node policy and does not apply to hardware where the number of sockets is greater than number of NUMA nodes.distribute-cpus-across-coresIf the distribute-cpus-across-cores policy option is specified, the static policy will attempt to allocate virtual cores (hardware threads) across different physical cores. By default, the CPUManager tends to pack CPUs onto as few physical cores as possible, which can lead to contention among CPUs on the same physical core and result in performance bottlenecks. By enabling the distribute-cpus-across-cores policy, the static policy ensures that CPUs are distributed across as many physical cores as possible, reducing the contention on the same physical core and thereby improving overall performance. However, it is important to note that this strategy might be less effective when the system is heavily loaded. Under such conditions, the benefit of reducing contention diminishes. Conversely, default behavior can help in reducing inter-core communication overhead, potentially providing better performance under high load conditions.strict-cpu-reservationThe reservedSystemCPUs parameter in KubeletConfiguration, or the deprecated kubelet command line option --reserved-cpus, defines an explicit CPU set for OS system daemons and kubernetes system daemons. More details of this parameter can be found on the Explicitly Reserved CPU List page. By default, this isolation is implemented only for guaranteed pods with integer CPU requests not for burstable and best-effort pods (and guaranteed pods with fractional CPU requests). Admission is only comparing the CPU requests against the allocatable CPUs. Since the CPU limit is higher than the request, the default behaviour allows burstable and best-effort pods to use up the capacity of reservedSystemCPUs and cause host OS services to starve in real life deployments. If the strict-cpu-reservation policy option is enabled, the static policy will not allow any workload to use the CPU cores specified in reservedSystemCPUs.prefer-align-cpus-by-uncorecacheIf the prefer-align-cpus-by-uncorecache policy is specified, the static policy will allocate CPU resources for individual containers such that all CPUs assigned to a container share the same uncore cache block (also known as the Last-Level Cache or LLC). By default, the CPUManager will tightly pack CPU assignments which can result in containers being assigned CPUs from multiple uncore caches. This option enables the CPUManager to allocate CPUs in a way that maximizes the efficient use of the uncore cache. Allocation is performed on a best-effort basis, aiming to affine as many CPUs as possible within the same uncore cache. If the container's CPU requirement exceeds the CPU capacity of a single uncore cache, the CPUManager minimizes the number of uncore caches used in order to maintain optimal uncore cache alignment. Specific workloads can benefit in performance from the reduction of inter-cache latency and noisy neighbors at the cache level. If the CPUManager cannot align optimally while the node has sufficient resources, the container will still be admitted using the default packed behavior.Memory Management PoliciesFEATURE STATE: Kubernetes v1.32 [stable] (enabled by default: true)The Kubernetes Memory Manager enables the feature of guaranteed memory (and hugepages) allocation for pods in the Guaranteed QoS class.The Memory Manager employs hint generation protocol to yield the most suitable NUMA affinity for a pod. The Memory Manager feeds the central manager (Topology Manager) with these affinity hints. Based on both the hints and Topology Manager policy, the pod is rejected or admitted to the node.Moreover, the Memory Manager ensures that the memory which a pod requests is allocated from a minimum number of NUMA nodes.Other resource managersThe configuration of individual managers is elaborated in dedicated documents:Device Manager