유휴 예약 권장사항을 사용하는 데는 비용이 들지 않습니다. 리소스 사용량 감소를 위한 권장사항을 사용하면 비용을 절약할 수 있습니다. 표시되는 비용 절감 예상액은 VM 예약을 실제 사용량에 맞게 조정할 경우 월별로 절감할 수 있는 금액입니다. 예를 들어 VM 8대를 예약했지만 항상 1대만 사용하는 경우 예약을 VM 1대로 축소하여 비용을 절감할 수 있습니다.
제한사항
다음 예약에는 유휴 및 사용률이 낮은 예약 권장사항을 사용할 수 없습니다.
약정 사용 할인(CUD)에 연결된 주문형 예약
TPU가 있는 가상 머신(VM) 인스턴스의 주문형 예약
유휴 상태 및 사용률이 낮은 예약 감지 작동 방식
Compute Engine의 예약 권장사항은 이전 사용량 측정항목을 기반으로 합니다. 기본적으로 이전 관찰 기간은 지난 7일입니다. 기본 관찰 기간을 변경하여 수신되는 권장사항을 맞춤설정할 수 있습니다.
알고리즘은 권장사항을 생성하기 위해 비용이 발생하지만 이전 7일 동안 활성 Compute Engine 리소스와 연결되지 않은 예약을 고려합니다.
권장사항 실행 빈도
예약이 생성되고 최소 7일 동안 리소스를 사용하지 않으면 Compute Engine에서 이에 대한 권장사항을 생성하기 시작합니다.
새 권장사항은 하루에 한 번 이상 생성됩니다.
권장사항 맞춤설정
Compute Engine을 사용하면 권장사항 알고리즘에 사용되는 구성을 변경하여 프로젝트에 대해 수신되는 권장사항을 맞춤설정할 수 있습니다.
특히 기본 관찰 기간을 변경하여 워크로드, 애플리케이션, 인프라 요구에 적합한 권장사항을 수신할 수 있습니다.
이 섹션에서는 이 구성에 설정할 수 있는 값에 대해 설명합니다.
이러한 값을 변경하면 수신되는 권장사항에 영향을 줍니다.
관찰 기간
idle_reservation_lookback_period 또는 under_utilized_reservation_lookback_period의 값을 수정하여 추천을 계산할 관찰 기간을 설정하고 프로젝트의 새 구성을 업로드합니다. 관찰 기간은 7~30일 사이의 값으로 설정할 수 있습니다. 예를 들면 다음과 같습니다.
관찰 기간이 이전 7일이면 "P7D"를 사용합니다.
관찰 기간이 이전 30일이면 "P30D"를 사용합니다.
기본적으로 관찰 기간은 7일입니다.
워크로드의 단기 변경사항을 기반으로 한 권장사항의 경우 더 짧은 관찰 기간을 사용합니다.
워크로드의 단기 변동의 영향을 받지 않는 권장사항의 경우 더 긴 관찰 기간을 사용합니다.
마찬가지로 under_utilized_reservation_utilization_threshold의 값을 수정하여 사용량 기준점을 설정하고 프로젝트의 새 구성을 업로드하여 사용량이 부족한 예약 추천을 트리거합니다. 예를 들면 다음과 같습니다.
[[["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: 2025-07-26(UTC)"],[[["\u003cp\u003eCompute Engine automatically generates reservation recommendations, based on the previous seven days of usage, to identify idle or underutilized on-demand reservations.\u003c/p\u003e\n"],["\u003cp\u003eThese recommendations help users optimize resource usage, potentially leading to cost savings by allowing the modification or deletion of unnecessary reservations.\u003c/p\u003e\n"],["\u003cp\u003eUsers can customize the recommendation algorithm by adjusting the observation period (from 7 to 30 days) and setting utilization thresholds, ensuring that recommendations align with their specific workloads and needs.\u003c/p\u003e\n"],["\u003cp\u003eThere are no costs associated with using these reservation recommendations, and they can be viewed and applied directly through the Compute Engine interface.\u003c/p\u003e\n"],["\u003cp\u003eThe generated recommendations are not applicable to reservations attached to committed use discounts (CUDs) or virtual machine (VM) instances with TPUs.\u003c/p\u003e\n"]]],[],null,["*** ** * ** ***\n\n|\n| **Preview**\n|\n|\n| This product or feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA products and features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nThis page explains how Compute Engine generates reservation recommendations\nand the parameters to configure them.\n\nCompute Engine provides reservation recommendations to help you identify\nidle or underutilized on-demand reservations for the previous seven days so that\nyou can [modify](/compute/docs/instances/reservations-modify) or\n[delete the reservations](/compute/docs/instances/reservations-delete).\n\nCompute Engine generates recommendations automatically based on system\nmetrics gathered by the Cloud Monitoring service. You can configure\nreservations recommendations to receive more or fewer recommendations.\n\n- To identify these recommendations and take action, see [View and apply idle reservation recommendations](/compute/docs/instances/view-and-apply-idle-reservation-recommendations) or [View and apply underutilized reservation recommendations](/compute/docs/instances/view-and-apply-underutilized-reservation-recommendations).\n- To configure these recommendations, see [Configure idle reservation recommendations](/compute/docs/instances/configure-idle-reservation-recommendations) or [Configure underutilized reservation recommendations](/compute/docs/instances/configure-underutilized-reservation-recommendations).\n- For an overview of Compute Engine reservations, see [About reservations](/compute/docs/instances/reservations-overview).\n\nPricing\n\nThere are no costs associated with using idle reservation recommendations. Using\nrecommendations to reduce your resource usage can result in cost savings. The\ndisplayed cost savings estimate is your potential monthly savings if you adjust\nyour VM reservation to match your actual usage. For example, if you\nreserved 8 VMs but consistently use only 1, you see the cost savings of\ndownsizing your reservation to 1 VM.\n\nLimitations\n\nIdle and underutilized reservation recommendations are not available for the\nfollowing reservations:\n\n- On-demand reservations that are attached to committed use discounts (CUDs)\n- On-demand reservations for virtual machine (VM) instances with TPUs\n\nHow detection of idle and underutilized reservations works\n\nReservation recommendations for Compute Engine are based on\nhistorical usage metrics. By default, the historical observation period is\nthe previous 7 days. By changing the default observation period, you can\ncustomize the recommendations that you receive.\n\nTo generate recommendations, the algorithm considers reservations that accrue\ncosts, but aren't associated with an active Compute Engine resource\nfor the previous 7 days.\n\nFrequency of recommendations\n\nAfter a reservation is created and you haven't consumed any resources for at\nleast 7 days, Compute Engine begins generating recommendations for it.\nNew recommendations are generated once per day.\n\nCustomize recommendations\n\nCompute Engine lets you customize the recommendations you receive for\nyour project by changing the configuration used by the recommendation algorithm.\nIn particular, by changing the default observation period, you can receive\nrecommendations that better fit your workloads, applications,\nand infrastructure needs.\n\nTo learn how to modify the configuration for your project,\nsee the following:\n\n- [Configure idle reservation recommendations](/compute/docs/instances/configure-idle-reservation-recommendations)\n- [Configure underutilized reservation recommendations](/compute/docs/instances/configure-underutilized-reservation-recommendations).\n\nChoose the right configuration\n\nThis section describes the values that you can set for the configuration.\nChanging these values affects the recommendations that you receive.\n\nThe observation period\n\nSet the observation period duration to calculate recommendations by modifying\nthe value for `idle_reservation_lookback_period` or\n`under_utilized_reservation_lookback_period` and upload the new\nconfiguration for your project. You can set the observation period\nto a value between 7 days and 30 days, for example:\n\n- For an observation period of the previous 7 days, use `\"P7D\"`.\n- For an observation period of the previous 30 days, use `\"P30D\"`.\n\nBy default, the observation period is 7 days.\n\n- For recommendations based on short-term changes in your workload, use a shorter observation period.\n- For recommendations that are not affected by short-term fluctuations in your workload, use a longer observation period.\n\nSimilarly, set the usage threshold that triggers an underutilized reservation\nrecommendation by modifying the value for\n`under_utilized_reservation_utilization_threshold` and upload the new\nconfiguration for your project, for example:\n\n- For a threshold of 80%, `\"0.8\"`.\n\nWhat's next\n\n- Learn how to [view and apply idle reservation recommendations](/compute/docs/instances/view-and-apply-idle-reservation-recommendations) or [view and apply underutilized reservation recommendations](/compute/docs/instances/view-and-apply-underutilized-reservation-recommendations).\n- Learn how to [configure idle reservation recommendations](/compute/docs/instances/configure-idle-reservation-recommendations) or [configure underutilized reservation recommendations](/compute/docs/instances/configure-underutilized-reservation-recommendations)."]]