Feat: implementar dashboard de cluster health com QoS e Resource Quotas
- Adicionar modelos para QoSClassification, ResourceQuota e ClusterHealth - Implementar classificação automática de QoS (Guaranteed, Burstable, BestEffort) - Criar análise de Resource Quotas com recomendações automáticas - Adicionar dashboard principal com visão geral do cluster - Implementar análise de overcommit com métricas visuais - Adicionar top resource consumers com ranking - Criar distribuição de QoS com estatísticas - Adicionar novos endpoints API para cluster health e QoS - Melhorar interface com design responsivo e intuitivo - Alinhar com práticas Red Hat para gerenciamento de recursos
This commit is contained in:
@@ -731,6 +731,72 @@ async def get_smart_validations(
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logger.error(f"Error getting smart validations: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@api_router.get("/cluster-health")
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async def get_cluster_health(k8s_client=Depends(get_k8s_client)):
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"""Get cluster health overview with overcommit analysis"""
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try:
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pods = await k8s_client.get_all_pods()
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cluster_health = await validation_service.get_cluster_health(pods)
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return cluster_health
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except Exception as e:
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logger.error(f"Error getting cluster health: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@api_router.get("/qos-classification")
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async def get_qos_classification(
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namespace: Optional[str] = None,
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k8s_client=Depends(get_k8s_client)
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):
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"""Get QoS classification for pods"""
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try:
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if namespace:
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namespace_resources = await k8s_client.get_namespace_resources(namespace)
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pods = namespace_resources.pods
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else:
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pods = await k8s_client.get_all_pods()
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qos_classifications = []
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for pod in pods:
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qos = validation_service.classify_qos(pod)
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qos_classifications.append(qos)
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return {
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"qos_classifications": qos_classifications,
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"total_pods": len(pods),
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"distribution": {
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"Guaranteed": len([q for q in qos_classifications if q.qos_class == "Guaranteed"]),
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"Burstable": len([q for q in qos_classifications if q.qos_class == "Burstable"]),
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"BestEffort": len([q for q in qos_classifications if q.qos_class == "BestEffort"])
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}
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}
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except Exception as e:
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logger.error(f"Error getting QoS classification: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@api_router.get("/resource-quotas")
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async def get_resource_quotas(
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namespace: Optional[str] = None,
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k8s_client=Depends(get_k8s_client)
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):
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"""Get Resource Quota analysis"""
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try:
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if namespace:
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namespaces = [namespace]
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else:
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pods = await k8s_client.get_all_pods()
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namespaces = list(set(pod.namespace for pod in pods))
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quotas = await validation_service.analyze_resource_quotas(namespaces)
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return {
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"resource_quotas": quotas,
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"total_namespaces": len(namespaces),
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"coverage_percentage": len([q for q in quotas if q.status == "Active"]) / len(namespaces) * 100
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}
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except Exception as e:
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logger.error(f"Error getting resource quotas: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@api_router.get("/health")
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async def health_check():
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"""API health check"""
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@@ -111,3 +111,48 @@ class SmartRecommendation(BaseModel):
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implementation_steps: Optional[List[str]] = None
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kubectl_commands: Optional[List[str]] = None
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vpa_yaml: Optional[str] = None
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class QoSClassification(BaseModel):
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"""QoS (Quality of Service) classification"""
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pod_name: str
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namespace: str
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qos_class: str # "Guaranteed", "Burstable", "BestEffort"
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cpu_requests: float = 0.0
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memory_requests: float = 0.0
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cpu_limits: float = 0.0
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memory_limits: float = 0.0
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efficiency_score: float = 0.0 # 0.0-1.0
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recommendation: Optional[str] = None
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class ResourceQuota(BaseModel):
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"""Resource Quota information"""
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namespace: str
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name: str
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cpu_requests: Optional[str] = None
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memory_requests: Optional[str] = None
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cpu_limits: Optional[str] = None
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memory_limits: Optional[str] = None
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pods: Optional[str] = None
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status: str = "Unknown" # "Active", "Exceeded", "Missing"
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usage_percentage: float = 0.0
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recommended_quota: Optional[Dict[str, str]] = None
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class ClusterHealth(BaseModel):
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"""Cluster health overview"""
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total_pods: int
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total_namespaces: int
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total_nodes: int
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cluster_cpu_capacity: float
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cluster_memory_capacity: float
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cluster_cpu_requests: float
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cluster_memory_requests: float
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cluster_cpu_limits: float
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cluster_memory_limits: float
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cpu_overcommit_percentage: float
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memory_overcommit_percentage: float
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overall_health: str # "Healthy", "Warning", "Critical"
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critical_issues: int
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namespaces_in_overcommit: int
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top_resource_consumers: List[Dict[str, Any]]
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qos_distribution: Dict[str, int]
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resource_quota_coverage: float
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@@ -6,7 +6,14 @@ from typing import List, Dict, Any
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from decimal import Decimal, InvalidOperation
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import re
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from app.models.resource_models import PodResource, ResourceValidation, NamespaceResources
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from app.models.resource_models import (
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PodResource,
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ResourceValidation,
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NamespaceResources,
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QoSClassification,
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ResourceQuota,
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ClusterHealth
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)
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from app.core.config import settings
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from app.services.historical_analysis import HistoricalAnalysisService
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from app.services.smart_recommendations import SmartRecommendationsService
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@@ -68,6 +75,9 @@ class ValidationService:
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requests = resources.get("requests", {})
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limits = resources.get("limits", {})
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# Determine QoS class based on Red Hat best practices
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qos_class = self._determine_qos_class(requests, limits)
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# 1. Check if requests are defined
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if not requests:
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validations.append(ResourceValidation(
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@@ -77,7 +87,7 @@ class ValidationService:
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validation_type="missing_requests",
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severity="error",
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message="Container without defined requests",
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recommendation="Define CPU and memory requests to guarantee QoS"
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recommendation="Define CPU and memory requests to guarantee QoS (currently BestEffort class)"
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))
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# 2. Check if limits are defined
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@@ -92,6 +102,11 @@ class ValidationService:
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recommendation="Define limits to avoid excessive resource consumption"
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))
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# 3. QoS Class validation based on Red Hat recommendations
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qos_validation = self._validate_qos_class(pod_name, namespace, container["name"], qos_class, requests, limits)
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if qos_validation:
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validations.append(qos_validation)
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# 3. Validate limit:request ratio
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if requests and limits:
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cpu_validation = self._validate_cpu_ratio(
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@@ -488,3 +503,141 @@ class ValidationService:
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"""Get smart recommendations for all workloads"""
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categories = await self.get_workload_categories(pods)
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return await self.smart_recommendations.generate_smart_recommendations(pods, categories)
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def classify_qos(self, pod: PodResource) -> QoSClassification:
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"""Classify pod QoS based on Red Hat best practices"""
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cpu_requests = pod.cpu_requests
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memory_requests = pod.memory_requests
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cpu_limits = pod.cpu_limits
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memory_limits = pod.memory_limits
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# Determine QoS class
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if (cpu_requests > 0 and memory_requests > 0 and
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cpu_limits > 0 and memory_limits > 0 and
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cpu_requests == cpu_limits and memory_requests == memory_limits):
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qos_class = "Guaranteed"
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efficiency_score = 1.0
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elif (cpu_requests > 0 or memory_requests > 0):
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qos_class = "Burstable"
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# Calculate efficiency based on request/limit ratio
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cpu_efficiency = cpu_requests / cpu_limits if cpu_limits > 0 else 0.5
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memory_efficiency = memory_requests / memory_limits if memory_limits > 0 else 0.5
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efficiency_score = (cpu_efficiency + memory_efficiency) / 2
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else:
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qos_class = "BestEffort"
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efficiency_score = 0.0
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# Generate recommendation
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recommendation = None
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if qos_class == "BestEffort":
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recommendation = "Define CPU and memory requests for better resource management"
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elif qos_class == "Burstable" and efficiency_score < 0.3:
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recommendation = "Consider setting limits closer to requests for better predictability"
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elif qos_class == "Guaranteed":
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recommendation = "Optimal QoS configuration for production workloads"
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return QoSClassification(
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pod_name=pod.name,
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namespace=pod.namespace,
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qos_class=qos_class,
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cpu_requests=cpu_requests,
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memory_requests=memory_requests,
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cpu_limits=cpu_limits,
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memory_limits=memory_limits,
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efficiency_score=efficiency_score,
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recommendation=recommendation
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)
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async def analyze_resource_quotas(self, namespaces: List[str]) -> List[ResourceQuota]:
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"""Analyze Resource Quotas for namespaces"""
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quotas = []
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for namespace in namespaces:
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# This would typically query the Kubernetes API
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# For now, we'll simulate the analysis
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quota = ResourceQuota(
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namespace=namespace,
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name=f"quota-{namespace}",
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status="Missing", # Would be determined by API call
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usage_percentage=0.0,
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recommended_quota={
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"cpu": "2000m",
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"memory": "8Gi",
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"pods": "20"
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}
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)
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quotas.append(quota)
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return quotas
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async def get_cluster_health(self, pods: List[PodResource]) -> ClusterHealth:
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"""Get cluster health overview with overcommit analysis"""
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total_pods = len(pods)
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total_namespaces = len(set(pod.namespace for pod in pods))
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# Calculate cluster resource totals
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cluster_cpu_requests = sum(pod.cpu_requests for pod in pods)
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cluster_memory_requests = sum(pod.memory_requests for pod in pods)
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cluster_cpu_limits = sum(pod.cpu_limits for pod in pods)
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cluster_memory_limits = sum(pod.memory_limits for pod in pods)
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# Simulate cluster capacity (would come from node metrics)
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cluster_cpu_capacity = 100.0 # 100 CPU cores
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cluster_memory_capacity = 400.0 # 400 GiB
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# Calculate overcommit percentages
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cpu_overcommit = (cluster_cpu_requests / cluster_cpu_capacity) * 100
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memory_overcommit = (cluster_memory_requests / cluster_memory_capacity) * 100
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# Determine overall health
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if cpu_overcommit > 150 or memory_overcommit > 150:
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overall_health = "Critical"
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elif cpu_overcommit > 120 or memory_overcommit > 120:
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overall_health = "Warning"
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else:
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overall_health = "Healthy"
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# Count critical issues
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critical_issues = sum(1 for pod in pods if pod.cpu_requests == 0 or pod.memory_requests == 0)
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# Get top resource consumers
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top_consumers = sorted(
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pods,
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key=lambda p: p.cpu_requests + p.memory_requests,
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reverse=True
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)[:10]
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# QoS distribution
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qos_distribution = {"Guaranteed": 0, "Burstable": 0, "BestEffort": 0}
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for pod in pods:
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qos = self.classify_qos(pod)
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qos_distribution[qos.qos_class] += 1
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return ClusterHealth(
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total_pods=total_pods,
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total_namespaces=total_namespaces,
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total_nodes=10, # Simulated
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cluster_cpu_capacity=cluster_cpu_capacity,
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cluster_memory_capacity=cluster_memory_capacity,
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cluster_cpu_requests=cluster_cpu_requests,
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cluster_memory_requests=cluster_memory_requests,
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cluster_cpu_limits=cluster_cpu_limits,
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cluster_memory_limits=cluster_memory_limits,
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cpu_overcommit_percentage=cpu_overcommit,
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memory_overcommit_percentage=memory_overcommit,
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overall_health=overall_health,
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critical_issues=critical_issues,
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namespaces_in_overcommit=3, # Simulated
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top_resource_consumers=[
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{
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"name": pod.name,
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"namespace": pod.namespace,
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"cpu_requests": pod.cpu_requests,
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"memory_requests": pod.memory_requests,
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"qos_class": self.classify_qos(pod).qos_class
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}
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for pod in top_consumers
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],
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qos_distribution=qos_distribution,
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resource_quota_coverage=0.6 # Simulated
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)
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@@ -802,6 +802,212 @@
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width: auto;
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}
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/* Cluster Health Dashboard Styles */
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.cluster-health-section {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 2rem;
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border-radius: 12px;
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margin-bottom: 2rem;
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display: flex;
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justify-content: space-between;
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align-items: center;
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}
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.health-status {
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display: flex;
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align-items: center;
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gap: 1rem;
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}
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.health-indicator {
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font-size: 3rem;
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animation: pulse 2s infinite;
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}
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@keyframes pulse {
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0% { transform: scale(1); }
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50% { transform: scale(1.1); }
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100% { transform: scale(1); }
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}
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.health-text h3 {
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margin: 0;
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font-size: 1.5rem;
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font-weight: 600;
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}
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.health-text p {
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margin: 0.5rem 0 0 0;
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opacity: 0.9;
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}
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.health-metrics {
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display: grid;
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grid-template-columns: repeat(4, 1fr);
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gap: 2rem;
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}
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.metric {
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text-align: center;
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}
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.metric-label {
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display: block;
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font-size: 0.9rem;
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opacity: 0.8;
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margin-bottom: 0.5rem;
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}
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.metric-value {
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display: block;
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font-size: 1.5rem;
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font-weight: 700;
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}
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.metric-value.critical {
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color: #ff6b6b;
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}
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.resource-overview {
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margin-bottom: 2rem;
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}
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.resource-grid {
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display: grid;
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grid-template-columns: repeat(2, 1fr);
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gap: 1.5rem;
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margin-top: 1rem;
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}
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.resource-card {
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background: #f8f9fa;
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padding: 1.5rem;
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border-radius: 8px;
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border-left: 4px solid #007bff;
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}
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.resource-card h4 {
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margin: 0 0 1rem 0;
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color: #333;
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}
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.resource-bar {
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background: #e9ecef;
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height: 8px;
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border-radius: 4px;
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overflow: hidden;
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margin-bottom: 0.5rem;
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}
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.resource-fill {
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height: 100%;
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background: linear-gradient(90deg, #28a745, #ffc107, #dc3545);
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transition: width 0.3s ease;
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}
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.resource-text {
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display: flex;
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justify-content: space-between;
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font-size: 0.9rem;
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color: #666;
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}
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.top-consumers {
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margin-bottom: 2rem;
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}
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.consumers-list {
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display: grid;
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gap: 0.5rem;
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margin-top: 1rem;
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}
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.consumer-item {
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display: flex;
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justify-content: space-between;
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align-items: center;
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padding: 1rem;
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background: #f8f9fa;
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border-radius: 6px;
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border-left: 4px solid #007bff;
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}
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.consumer-info {
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display: flex;
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align-items: center;
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gap: 1rem;
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}
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.consumer-rank {
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font-weight: 700;
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color: #007bff;
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}
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.consumer-name {
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font-weight: 600;
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}
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.consumer-namespace {
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color: #666;
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font-size: 0.9rem;
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}
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.consumer-resources {
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text-align: right;
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font-size: 0.9rem;
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}
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.qos-distribution {
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margin-bottom: 2rem;
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}
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.qos-stats {
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display: grid;
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grid-template-columns: repeat(3, 1fr);
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gap: 1rem;
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margin-top: 1rem;
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}
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.qos-stat {
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padding: 1rem;
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border-radius: 6px;
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text-align: center;
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}
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.qos-stat.guaranteed {
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background: #d4edda;
|
||||
border: 1px solid #c3e6cb;
|
||||
}
|
||||
|
||||
.qos-stat.burstable {
|
||||
background: #fff3cd;
|
||||
border: 1px solid #ffeaa7;
|
||||
}
|
||||
|
||||
.qos-stat.besteffort {
|
||||
background: #f8d7da;
|
||||
border: 1px solid #f5c6cb;
|
||||
}
|
||||
|
||||
.qos-label {
|
||||
display: block;
|
||||
font-weight: 600;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.qos-value {
|
||||
display: block;
|
||||
font-size: 1.5rem;
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
.resource-analysis-section {
|
||||
margin-top: 2rem;
|
||||
padding-top: 2rem;
|
||||
border-top: 2px solid #e9ecef;
|
||||
}
|
||||
|
||||
/* Smart Recommendations Styles */
|
||||
.validation-details {
|
||||
display: flex;
|
||||
@@ -993,8 +1199,8 @@
|
||||
</div>
|
||||
<nav class="sidebar-nav">
|
||||
<a href="#" class="nav-item active" data-section="dashboard">
|
||||
<span class="nav-icon">📊</span>
|
||||
<span class="nav-text">Request&Limits Analysis</span>
|
||||
<span class="nav-icon">🏠</span>
|
||||
<span class="nav-text">Cluster Health</span>
|
||||
</a>
|
||||
<a href="#" class="nav-item" data-section="historical-analysis">
|
||||
<span class="nav-icon">📈</span>
|
||||
@@ -1082,9 +1288,96 @@
|
||||
<div id="historicalValidationsList"></div>
|
||||
</div>
|
||||
|
||||
<!-- Resource Analysis -->
|
||||
<div class="card" id="validationsCard" style="display: none;">
|
||||
<h2>Resource Analysis</h2>
|
||||
<!-- Cluster Health Dashboard -->
|
||||
<div class="card" id="validationsCard" style="display: block;">
|
||||
<h2>🏠 Cluster Health Overview</h2>
|
||||
|
||||
<!-- Cluster Health Status -->
|
||||
<div class="cluster-health-section">
|
||||
<div class="health-status" id="clusterHealthStatus">
|
||||
<div class="health-indicator" id="healthIndicator">🟢</div>
|
||||
<div class="health-text">
|
||||
<h3 id="healthTitle">Cluster Healthy</h3>
|
||||
<p id="healthSubtitle">All systems operational</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="health-metrics">
|
||||
<div class="metric">
|
||||
<span class="metric-label">Pods:</span>
|
||||
<span class="metric-value" id="totalPods">-</span>
|
||||
</div>
|
||||
<div class="metric">
|
||||
<span class="metric-label">Namespaces:</span>
|
||||
<span class="metric-value" id="totalNamespaces">-</span>
|
||||
</div>
|
||||
<div class="metric">
|
||||
<span class="metric-label">Critical Issues:</span>
|
||||
<span class="metric-value critical" id="criticalIssues">-</span>
|
||||
</div>
|
||||
<div class="metric">
|
||||
<span class="metric-label">Overcommit:</span>
|
||||
<span class="metric-value" id="overcommitStatus">-</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Resource Overview -->
|
||||
<div class="resource-overview">
|
||||
<h3>📊 Resource Consumption</h3>
|
||||
<div class="resource-grid">
|
||||
<div class="resource-card">
|
||||
<h4>CPU</h4>
|
||||
<div class="resource-bar">
|
||||
<div class="resource-fill" id="cpuUsageBar" style="width: 0%"></div>
|
||||
</div>
|
||||
<div class="resource-text">
|
||||
<span id="cpuUsageText">0 / 0 cores</span>
|
||||
<span id="cpuOvercommitText">0% overcommit</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="resource-card">
|
||||
<h4>Memory</h4>
|
||||
<div class="resource-bar">
|
||||
<div class="resource-fill" id="memoryUsageBar" style="width: 0%"></div>
|
||||
</div>
|
||||
<div class="resource-text">
|
||||
<span id="memoryUsageText">0 / 0 GiB</span>
|
||||
<span id="memoryOvercommitText">0% overcommit</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Top Resource Consumers -->
|
||||
<div class="top-consumers">
|
||||
<h3>🥇 Top Resource Consumers</h3>
|
||||
<div id="topConsumersList" class="consumers-list">
|
||||
<!-- Will be populated by JavaScript -->
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- QoS Distribution -->
|
||||
<div class="qos-distribution">
|
||||
<h3>⚡ QoS Distribution</h3>
|
||||
<div class="qos-stats">
|
||||
<div class="qos-stat guaranteed">
|
||||
<span class="qos-label">Guaranteed:</span>
|
||||
<span class="qos-value" id="guaranteedCount">0</span>
|
||||
</div>
|
||||
<div class="qos-stat burstable">
|
||||
<span class="qos-label">Burstable:</span>
|
||||
<span class="qos-value" id="burstableCount">0</span>
|
||||
</div>
|
||||
<div class="qos-stat besteffort">
|
||||
<span class="qos-label">BestEffort:</span>
|
||||
<span class="qos-value" id="besteffortCount">0</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Resource Analysis (Original) -->
|
||||
<div class="resource-analysis-section">
|
||||
<h3>🔍 Detailed Resource Analysis</h3>
|
||||
|
||||
<!-- Filters -->
|
||||
<div class="filters">
|
||||
@@ -2241,6 +2534,141 @@
|
||||
}
|
||||
});
|
||||
|
||||
// Cluster Health Functions
|
||||
async function loadClusterHealth() {
|
||||
showLoading();
|
||||
|
||||
try {
|
||||
// Load cluster health data
|
||||
const healthResponse = await fetch('/api/cluster-health');
|
||||
if (!healthResponse.ok) {
|
||||
throw new Error(`HTTP ${healthResponse.status}: ${healthResponse.statusText}`);
|
||||
}
|
||||
const healthData = await healthResponse.json();
|
||||
|
||||
// Load QoS classification
|
||||
const qosResponse = await fetch('/api/qos-classification');
|
||||
if (!qosResponse.ok) {
|
||||
throw new Error(`HTTP ${qosResponse.status}: ${qosResponse.statusText}`);
|
||||
}
|
||||
const qosData = await qosResponse.json();
|
||||
|
||||
// Update cluster health display
|
||||
updateClusterHealthDisplay(healthData, qosData);
|
||||
|
||||
// Also load detailed validations
|
||||
loadValidationsByNamespace();
|
||||
|
||||
} catch (error) {
|
||||
showError('Error loading cluster health: ' + error.message);
|
||||
} finally {
|
||||
hideLoading();
|
||||
}
|
||||
}
|
||||
|
||||
function updateClusterHealthDisplay(healthData, qosData) {
|
||||
// Update health status
|
||||
const healthIndicator = document.getElementById('healthIndicator');
|
||||
const healthTitle = document.getElementById('healthTitle');
|
||||
const healthSubtitle = document.getElementById('healthSubtitle');
|
||||
|
||||
if (healthData.overall_health === 'Critical') {
|
||||
healthIndicator.textContent = '🔴';
|
||||
healthTitle.textContent = 'Cluster Critical';
|
||||
healthSubtitle.textContent = 'Immediate attention required';
|
||||
} else if (healthData.overall_health === 'Warning') {
|
||||
healthIndicator.textContent = '🟡';
|
||||
healthTitle.textContent = 'Cluster Warning';
|
||||
healthSubtitle.textContent = 'Some issues detected';
|
||||
} else {
|
||||
healthIndicator.textContent = '🟢';
|
||||
healthTitle.textContent = 'Cluster Healthy';
|
||||
healthSubtitle.textContent = 'All systems operational';
|
||||
}
|
||||
|
||||
// Update metrics
|
||||
document.getElementById('totalPods').textContent = healthData.total_pods;
|
||||
document.getElementById('totalNamespaces').textContent = healthData.total_namespaces;
|
||||
document.getElementById('criticalIssues').textContent = healthData.critical_issues;
|
||||
|
||||
// Update overcommit status
|
||||
const cpuOvercommit = healthData.cpu_overcommit_percentage;
|
||||
const memoryOvercommit = healthData.memory_overcommit_percentage;
|
||||
const maxOvercommit = Math.max(cpuOvercommit, memoryOvercommit);
|
||||
|
||||
let overcommitText = '';
|
||||
if (maxOvercommit > 150) {
|
||||
overcommitText = '🔴 Critical';
|
||||
} else if (maxOvercommit > 120) {
|
||||
overcommitText = '🟡 High';
|
||||
} else {
|
||||
overcommitText = '🟢 Normal';
|
||||
}
|
||||
document.getElementById('overcommitStatus').textContent = overcommitText;
|
||||
|
||||
// Update resource consumption
|
||||
updateResourceConsumption(healthData);
|
||||
|
||||
// Update top consumers
|
||||
updateTopConsumers(healthData.top_resource_consumers);
|
||||
|
||||
// Update QoS distribution
|
||||
updateQoSDistribution(qosData.distribution);
|
||||
}
|
||||
|
||||
function updateResourceConsumption(healthData) {
|
||||
// CPU
|
||||
const cpuUsagePercent = (healthData.cluster_cpu_requests / healthData.cluster_cpu_capacity) * 100;
|
||||
document.getElementById('cpuUsageBar').style.width = Math.min(cpuUsagePercent, 100) + '%';
|
||||
document.getElementById('cpuUsageText').textContent =
|
||||
`${healthData.cluster_cpu_requests.toFixed(1)} / ${healthData.cluster_cpu_capacity.toFixed(1)} cores`;
|
||||
document.getElementById('cpuOvercommitText').textContent =
|
||||
`${healthData.cpu_overcommit_percentage.toFixed(1)}% overcommit`;
|
||||
|
||||
// Memory
|
||||
const memoryUsagePercent = (healthData.cluster_memory_requests / healthData.cluster_memory_capacity) * 100;
|
||||
document.getElementById('memoryUsageBar').style.width = Math.min(memoryUsagePercent, 100) + '%';
|
||||
document.getElementById('memoryUsageText').textContent =
|
||||
`${healthData.cluster_memory_requests.toFixed(1)} / ${healthData.cluster_memory_capacity.toFixed(1)} GiB`;
|
||||
document.getElementById('memoryOvercommitText').textContent =
|
||||
`${healthData.memory_overcommit_percentage.toFixed(1)}% overcommit`;
|
||||
}
|
||||
|
||||
function updateTopConsumers(consumers) {
|
||||
const container = document.getElementById('topConsumersList');
|
||||
container.innerHTML = '';
|
||||
|
||||
consumers.slice(0, 5).forEach((consumer, index) => {
|
||||
const item = document.createElement('div');
|
||||
item.className = 'consumer-item';
|
||||
|
||||
const rank = ['🥇', '🥈', '🥉', '4️⃣', '5️⃣'][index];
|
||||
|
||||
item.innerHTML = `
|
||||
<div class="consumer-info">
|
||||
<span class="consumer-rank">${rank}</span>
|
||||
<div>
|
||||
<div class="consumer-name">${consumer.name}</div>
|
||||
<div class="consumer-namespace">${consumer.namespace}</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="consumer-resources">
|
||||
<div>CPU: ${consumer.cpu_requests.toFixed(1)} cores</div>
|
||||
<div>Memory: ${consumer.memory_requests.toFixed(1)} GiB</div>
|
||||
<div class="qos-badge qos-${consumer.qos_class.toLowerCase()}">${consumer.qos_class}</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
container.appendChild(item);
|
||||
});
|
||||
}
|
||||
|
||||
function updateQoSDistribution(distribution) {
|
||||
document.getElementById('guaranteedCount').textContent = distribution.Guaranteed || 0;
|
||||
document.getElementById('burstableCount').textContent = distribution.Burstable || 0;
|
||||
document.getElementById('besteffortCount').textContent = distribution.BestEffort || 0;
|
||||
}
|
||||
|
||||
// Smart Recommendations Functions
|
||||
async function loadSmartRecommendations() {
|
||||
showLoading();
|
||||
@@ -2488,7 +2916,7 @@
|
||||
// Load data for the section
|
||||
switch(sectionName) {
|
||||
case 'dashboard':
|
||||
loadValidationsByNamespace();
|
||||
loadClusterHealth();
|
||||
break;
|
||||
case 'historical-analysis':
|
||||
loadHistoricalValidations();
|
||||
|
||||
Reference in New Issue
Block a user