통계와 추천을 사용하여 유휴 상태의 Google Kubernetes Engine(GKE) Standard 클러스터를 식별할 수 있습니다. 식별된 유휴 클러스터가 사용되지 않는 것을 확인한 후 이를 삭제하여 비용을 절약할 수 있습니다. 가능한 경우 클러스터를 삭제할 경우 예상되는 월간 절감액이 추천에 포함됩니다. 자세한 내용은 유휴 클러스터의 비용 추정치 이해를 참조하세요.
GKE는 과소 프로비저닝된 클러스터, 초과 프로비저닝된 클러스터, 유휴 클러스터와 같은 비용 최적화 시나리오에 관한 통계와 추천을 제공하며, 클러스터 확장, 축소 또는 삭제에 관한 추천도 제공합니다. 이 페이지에서는 유휴 클러스터를 식별하는 방법을 설명합니다. 과소 프로비저닝 및 초과 프로비저닝된 GKE 클러스터 식별도 참조하세요.
GKE는 워크로드가 요청하는 리소스에 대해서만 비용을 지불하기 때문에 최소한의 운영 비용이 발생하는 Autopilot 클러스터에 대한 통계를 제공하지 않습니다. 자세한 내용은 Autopilot 가격 책정을 참조하세요.
GKE는 클러스터를 모니터링하고 Google Cloud에서 리소스 사용에 대한 통계와 추천을 생성하는 추천자를 제공하는 서비스인 Active Assist를 통해 사용량을 최적화하는 방법에 대한 안내를 제공합니다.
유휴 클러스터를 식별하려면 Google Cloud 콘솔, Google Cloud CLI 또는 Recommender API를 사용하여 통계 및 추천을 확인합니다. 다음 섹션의 표에 표시된 통계 하위유형과 CLUSTER_IDLE 추천 하위유형을 사용합니다. 콘솔에서 이러한 통계는 클러스터 페이지의 비용 최적화 탭에 표시됩니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-01(UTC)"],[],[],null,["# Identify idle GKE clusters\n\n[Standard](/kubernetes-engine/docs/concepts/choose-cluster-mode)\n\n*** ** * ** ***\n\nYou can identify idle Google Kubernetes Engine (GKE) Standard clusters\nusing insights and recommendations. After you verify that the identified idle\nclusters are unused, you can delete them to save costs. If possible, the\nrecommendation includes projected monthly savings for deleting a cluster. For\nmore information, see [Understand cost estimation for idle\nclusters](#cost-estimate).\n\nGKE provides insights and recommendations for cost optimization\nscenarios such as underprovisioned clusters, overprovisioned clusters, and idle\nclusters, with corresponding recommendations to scale up, scale down, or delete\nthe clusters. This page explains how to identify idle clusters. See also,\n[Identify underprovisioned and overprovisioned GKE\nclusters](/kubernetes-engine/docs/how-to/optimize-cluster-utilization).\n\nGKE doesn't provide insights for Autopilot\nclusters, which incur minimal operational costs as you only pay for the\nresources that your workloads request. For more information, see\n[Autopilot\nPricing](/kubernetes-engine/docs/concepts/autopilot-overview#pricing).\n\nGKE monitors your clusters and delivers guidance to optimize your\nusage through [Active Assist](/recommender/docs/whatis-activeassist), a\nservice that provides recommenders that generate insights and recommendations\nfor using resources on Google Cloud.\n\nFor more information about how to manage insights and recommendations, see\n[Optimize your usage of GKE with insights and recommendations](/kubernetes-engine/docs/how-to/optimize-with-recommenders).\n\nIdentify idle clusters\n----------------------\n\nTo identify idle clusters, [view insights and\nrecommendations](/kubernetes-engine/docs/how-to/optimize-with-recommenders#view-insights-recs)\nusing the Google Cloud console, the Google Cloud CLI, or the Recommender API. Use\nthe insight subtypes shown in [the table in the following\nsection](#how-idle-clusters) and the `CLUSTER_IDLE` recommendation subtype. In\nthe console, these insights appear in the **Cost Optimization**\ntab on the **Clusters** page.\n\nAfter you identify idle clusters, see the [considerations when deleting idle\nclusters](#idle-cluster-considerations).\n\nHow GKE identifies idle clusters\n--------------------------------\n\nGKE uses utilization signals to determine whether you receive an\ninsight and recommendation.\n\nThe following table describes the signals that GKE uses and the\nthreshold for each signal. Each signal triggers an independent insight. If a\ncluster has multiple insights, GKE displays a single\nrecommendation.\n\nGKE doesn't send recommendations for clusters that were created\nless than 30 days ago.\n\nUnderstand cost estimation for idle clusters\n--------------------------------------------\n\nIf possible, GKE includes with the recommendation an estimated\nmonthly cost of the idle cluster, projecting how much money you'd save each\nmonth if you deleted the cluster. This estimate is derived from the cluster\ncosts over the past 30 days.\n\nAny estimated savings are projections based on previous spending, and are not a\nguarantee of future cost or savings.\n\nTo see these estimates, ensure that you have the required\n`billing.accounts.getSpendingInformation` permission to get spending\ninformation. For details, see [Cloud Billing\naccess](/billing/docs/how-to/billing-access#billing-access).\n\nTo get more information about the cost of all of your GKE\nclusters, including a more granular breakdown based on namespaces and workloads,\nsee [Get key spending insights for your GKE resource allocation\nand cluster costs](/kubernetes-engine/docs/how-to/cost-allocations).\n\nFor more information about the costs of running a GKE cluster,\nsee [GKE pricing](/kubernetes-engine/pricing).\n\nConsiderations when deleting idle clusters\n------------------------------------------\n\nBefore you delete a cluster that GKE determines is idle,\nconsider the following possibilities:\n\n- Does anyone use the cluster? For example, a cluster might be intentionally idle if its purpose is to maintain failover capacity.\n- Should the cluster be scaled down instead of deleted? For example, a cluster running a useful workload might have low utilization and be identified as idle because more resources were provisioned than necessary.\n\nImplement the recommendation to delete idle clusters\n----------------------------------------------------\n\nIf you've received an insight and recommendation that you have an idle cluster\nthat can be deleted and have ruled out the [considerations](#idle-cluster-considerations)\nfor keeping the cluster running, follow the instructions in the recommendation\nand delete the cluster.\n\nWhat's next\n-----------\n\n- [Optimize your usage of GKE with insights and recommendations](/kubernetes-engine/docs/how-to/optimize-with-recommenders).\n- [Best practices for running cost-optimized Kubernetes applications on GKE](/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke)."]]
