本文整理汇总了Golang中k8s/io/kubernetes/plugin/pkg/scheduler/schedulercache.NodeInfo.AllowedPodNumber方法的典型用法代码示例。如果您正苦于以下问题：Golang NodeInfo.AllowedPodNumber方法的具体用法？Golang NodeInfo.AllowedPodNumber怎么用？Golang NodeInfo.AllowedPodNumber使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类k8s/io/kubernetes/plugin/pkg/scheduler/schedulercache.NodeInfo的用法示例。
在下文中一共展示了NodeInfo.AllowedPodNumber方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Golang代码示例。
示例1: PodFitsResources
func PodFitsResources(pod *api.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (bool, error) {
node := nodeInfo.Node()
if node == nil {
return false, fmt.Errorf("node not found")
}
allowedPodNumber := nodeInfo.AllowedPodNumber()
if len(nodeInfo.Pods())+1 > allowedPodNumber {
return false,
newInsufficientResourceError(podCountResourceName, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber))
}
var podRequest *resourceRequest
predicateMeta, ok := meta.(*predicateMetadata)
if ok {
podRequest = predicateMeta.podRequest
} else {
// We couldn't parse metadata - fallback to computing it.
podRequest = getResourceRequest(pod)
}
if podRequest.milliCPU == 0 && podRequest.memory == 0 && podRequest.nvidiaGPU == 0 {
return true, nil
}
allocatable := node.Status.Allocatable
totalMilliCPU := allocatable.Cpu().MilliValue()
totalMemory := allocatable.Memory().Value()
totalNvidiaGPU := allocatable.NvidiaGPU().Value()
if totalMilliCPU < podRequest.milliCPU+nodeInfo.RequestedResource().MilliCPU {
return false,
newInsufficientResourceError(cpuResourceName, podRequest.milliCPU, nodeInfo.RequestedResource().MilliCPU, totalMilliCPU)
}
if totalMemory < podRequest.memory+nodeInfo.RequestedResource().Memory {
return false,
newInsufficientResourceError(memoryResourceName, podRequest.memory, nodeInfo.RequestedResource().Memory, totalMemory)
}
if totalNvidiaGPU < podRequest.nvidiaGPU+nodeInfo.RequestedResource().NvidiaGPU {
return false,
newInsufficientResourceError(nvidiaGpuResourceName, podRequest.nvidiaGPU, nodeInfo.RequestedResource().NvidiaGPU, totalNvidiaGPU)
}
if glog.V(10) {
// We explicitly don't do glog.V(10).Infof() to avoid computing all the parameters if this is
// not logged. There is visible performance gain from it.
glog.Infof("Schedule Pod %+v on Node %+v is allowed, Node is running only %v out of %v Pods.",
podName(pod), node.Name, len(nodeInfo.Pods()), allowedPodNumber)
}
return true, nil
}示例2: PodFitsResources
func PodFitsResources(pod *api.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (bool, []algorithm.PredicateFailureReason, error) {
node := nodeInfo.Node()
if node == nil {
return false, nil, fmt.Errorf("node not found")
}
var predicateFails []algorithm.PredicateFailureReason
allowedPodNumber := nodeInfo.AllowedPodNumber()
if len(nodeInfo.Pods())+1 > allowedPodNumber {
predicateFails = append(predicateFails, NewInsufficientResourceError(api.ResourcePods, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber)))
}
var podRequest *schedulercache.Resource
if predicateMeta, ok := meta.(*predicateMetadata); ok {
podRequest = predicateMeta.podRequest
} else {
// We couldn't parse metadata - fallback to computing it.
podRequest = GetResourceRequest(pod)
}
if podRequest.MilliCPU == 0 && podRequest.Memory == 0 && podRequest.NvidiaGPU == 0 && len(podRequest.OpaqueIntResources) == 0 {
return len(predicateFails) == 0, predicateFails, nil
}
allocatable := nodeInfo.AllocatableResource()
if allocatable.MilliCPU < podRequest.MilliCPU+nodeInfo.RequestedResource().MilliCPU {
predicateFails = append(predicateFails, NewInsufficientResourceError(api.ResourceCPU, podRequest.MilliCPU, nodeInfo.RequestedResource().MilliCPU, allocatable.MilliCPU))
}
if allocatable.Memory < podRequest.Memory+nodeInfo.RequestedResource().Memory {
predicateFails = append(predicateFails, NewInsufficientResourceError(api.ResourceMemory, podRequest.Memory, nodeInfo.RequestedResource().Memory, allocatable.Memory))
}
if allocatable.NvidiaGPU < podRequest.NvidiaGPU+nodeInfo.RequestedResource().NvidiaGPU {
predicateFails = append(predicateFails, NewInsufficientResourceError(api.ResourceNvidiaGPU, podRequest.NvidiaGPU, nodeInfo.RequestedResource().NvidiaGPU, allocatable.NvidiaGPU))
}
for rName, rQuant := range podRequest.OpaqueIntResources {
if allocatable.OpaqueIntResources[rName] < rQuant+nodeInfo.RequestedResource().OpaqueIntResources[rName] {
predicateFails = append(predicateFails, NewInsufficientResourceError(rName, podRequest.OpaqueIntResources[rName], nodeInfo.RequestedResource().OpaqueIntResources[rName], allocatable.OpaqueIntResources[rName]))
}
}
if glog.V(10) {
// We explicitly don't do glog.V(10).Infof() to avoid computing all the parameters if this is
// not logged. There is visible performance gain from it.
glog.Infof("Schedule Pod %+v on Node %+v is allowed, Node is running only %v out of %v Pods.",
podName(pod), node.Name, len(nodeInfo.Pods()), allowedPodNumber)
}
return len(predicateFails) == 0, predicateFails, nil
}