Golang NodeInfo.AllocatableResource方法代碼示例

// Calculates host priority based on the amount of unused resources.
// 'node' has information about the resources on the node.
// 'pods' is a list of pods currently scheduled on the node.
// TODO: Use Node() from nodeInfo instead of passing it.
func calculateUnusedPriority(pod *api.Pod, podRequests *schedulercache.Resource, node *api.Node, nodeInfo *schedulercache.NodeInfo) schedulerapi.HostPriority {
	allocatableResources := nodeInfo.AllocatableResource()
	totalResources := *podRequests
	totalResources.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
	totalResources.Memory += nodeInfo.NonZeroRequest().Memory

	cpuScore := calculateUnusedScore(totalResources.MilliCPU, allocatableResources.MilliCPU, node.Name)
	memoryScore := calculateUnusedScore(totalResources.Memory, allocatableResources.Memory, node.Name)
	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.V(10).Infof(
			"%v -> %v: Least Requested Priority, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d CPU %d memory",
			pod.Name, node.Name,
			allocatableResources.MilliCPU, allocatableResources.Memory,
			totalResources.MilliCPU, totalResources.Memory,
			cpuScore, memoryScore,
		)
	}

	return schedulerapi.HostPriority{
		Host:  node.Name,
		Score: int((cpuScore + memoryScore) / 2),
	}
}

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
}

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 *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 {
		return true, nil
	}

	allocatable := nodeInfo.AllocatableResource()
	if allocatable.MilliCPU < podRequest.MilliCPU+nodeInfo.RequestedResource().MilliCPU {
		return false,
			newInsufficientResourceError(cpuResourceName, podRequest.MilliCPU, nodeInfo.RequestedResource().MilliCPU, allocatable.MilliCPU)
	}
	if allocatable.Memory < podRequest.Memory+nodeInfo.RequestedResource().Memory {
		return false,
			newInsufficientResourceError(memoryResourceName, podRequest.Memory, nodeInfo.RequestedResource().Memory, allocatable.Memory)
	}
	if allocatable.NvidiaGPU < podRequest.NvidiaGPU+nodeInfo.RequestedResource().NvidiaGPU {
		return false,
			newInsufficientResourceError(nvidiaGpuResourceName, podRequest.NvidiaGPU, nodeInfo.RequestedResource().NvidiaGPU, allocatable.NvidiaGPU)
	}
	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
}

func calculateBalancedResourceAllocation(pod *api.Pod, podRequests *schedulercache.Resource, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
	node := nodeInfo.Node()
	if node == nil {
		return schedulerapi.HostPriority{}, fmt.Errorf("node not found")
	}

	allocatableResources := nodeInfo.AllocatableResource()
	totalResources := *podRequests
	totalResources.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
	totalResources.Memory += nodeInfo.NonZeroRequest().Memory

	cpuFraction := fractionOfCapacity(totalResources.MilliCPU, allocatableResources.MilliCPU)
	memoryFraction := fractionOfCapacity(totalResources.Memory, allocatableResources.Memory)
	score := int(0)
	if cpuFraction >= 1 || memoryFraction >= 1 {
		// if requested >= capacity, the corresponding host should never be preferred.
		score = 0
	} else {
		// Upper and lower boundary of difference between cpuFraction and memoryFraction are -1 and 1
		// respectively. Multilying the absolute value of the difference by 10 scales the value to
		// 0-10 with 0 representing well balanced allocation and 10 poorly balanced. Subtracting it from
		// 10 leads to the score which also scales from 0 to 10 while 10 representing well balanced.
		diff := math.Abs(cpuFraction - memoryFraction)
		score = int(10 - diff*10)
	}
	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.V(10).Infof(
			"%v -> %v: Balanced Resource Allocation, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d",
			pod.Name, node.Name,
			allocatableResources.MilliCPU, allocatableResources.Memory,
			totalResources.MilliCPU, totalResources.Memory,
			score,
		)
	}

	return schedulerapi.HostPriority{
		Host:  node.Name,
		Score: score,
	}, nil
}