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description= Learn about reservations of Compute Engine VM instances.;
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server databases to a google cloud storage bucket back up sql server databases using instant snapshots cloning a microsoft sql server database on compute engine load test sql server using hammerdb redis deployment options for redis on google cloud containers containers on compute engine deploy containers on vms and managed instance groups configure options to run your container transition from the container startup agent prepare for the shutdown of the container startup agent prevent the creation of vms that use the container metadata migrate containers that were deployed on vms during vm creation openshift workloads openshift on google cloud overview plan for openshift on google cloud overview of cluster services for openshift automatic configuration validation for openshift clusters built in integrations for openshift best practices for high availability with openshift disaster recovery for openshift on google cloud disaster recovery strategies for active passive and active inactive setups with openshift microsoft windows windows workloads best practices for windows server vms setting up active directory best practices for running active directory on google cloud deploy microsoft sharepoint server on compute engine deploying microsoft exchange server 2016 on compute engine windows server perform an in place upgrade of windows server run windows server failover clustering ibm spectrum symphony integrate ibm spectrum symphony with google cloud install the compute engine symphony provider install the google kubernetes symphony provider troubleshoot ibm spectrum symphony others load testing distributed load testing using kubernetes ssh port forwarding and load testing analytics monte carlo methods using apache spark machine learning run tensorflow inference workloads with tensorrt5 and nvidia t4 gpu monitor monitor logs view audit logs view usage reports view compute engine operations migrate from activity logs to audit logs view activity logs monitor resources monitor vm and sole tenant node usage observe and monitor vms monitor gpu performance monitor gpu performance on linux vms monitor gpu performance on windows vms monitor disks monitor disk health monitor the replica states of regional disks monitor disks list of metrics for pools monitor pools monitor reservations organize resources using labels scale autoscale groups of vms about autoscaling groups of vms create and manage autoscalers scale based on cpu utilization scale based on predictions scale based on load balancing serving capacity scale based on monitoring metrics scale based on schedules use an autoscaling policy with multiple signals manage autoscalers understand autoscaler decisions view autoscaler logs autoscale node groups reserve vm capacity choose a reservation type sharing reservations best practices for shared reservations allow a project to share reservations on demand reservations about on demand reservations create an on demand reservation for a single project for multiple projects combine an on demand reservation with a cud modify an on demand reservation delete an on demand reservation future reservations about future reservations create a reservation request for a single project for multiple projects modify a reservation request delete a reservation request future reservations in calendar mode about future reservations in calendar mode create a reservation request in calendar mode delete a reservation request in calendar mode view reservations or reservation requests consume a reservation prevent vms from consuming reservations load balancing about load balancing and scaling add an instance group to a load balancer request routing to a multi region external https load balancer cross region load balancing for microsoft iis backends set up internal tcp udp load balancing build reliable and scalable applications use autohealing for highly available applications use load balancing for highly available applications use autoscaling for highly scalable applications globally autoscale a web service on compute engine patterns for scalable and resilient applications patterns for using floating ip addresses on compute engine optimize resource utilization use recommendations to manage resources apply machine type recommendations to vms configure machine type recommendations apply machine type recommendations to migs view and apply idle resources recommendations view and understand vm insights view and understand mig insights manage idle vm recommendations idle vm recommendations overview view and apply idle vm recommendations configure idle vm recommendations manage reservation recommendations reservation recommendations overview view and apply idle reservation recommendations view and apply underutilized reservation recommendations configure idle reservation recommendations configure underutilized reservation recommendations overcommit cpus on sole tenant vms manual live migration about manual live migration manually live migrate vms share sole tenant node groups next generation dynamic resource management cost savings get discounts for committed usage about commitments and committed use discounts cuds resource based cuds purchase resource based commitments without attached reservations with attached reservations for os licenses manage resource based commitments renew commitments automatically extend commitment terms merge and split commitments upgrade commitments share resource based cuds across projects get discounts for sustained usage disk performance optimize hyperdisk performance optimize persistent disk performance optimize local ssd performance workload performance set the number of threads per core customize the number of visible cpu cores analyze the cpu performance using the pmu pmu overview enable the pmu in vms manage the pmu in vms network performance network bandwidth use google virtual nic use irdma network driver use idpf network interface configure a vm with higher bandwidth reduce latency by using compact placement policies optimize tcp network communication optimize tcp network performance optimize tcp network resiliency benchmark higher bandwidth vms optimize app latency with load balancing use dpdk to improve network performance network performance and gpu vms networking and gpu machines use higher network bandwidth patterns for using multiple host nics troubleshoot general tips troubleshoot connectivity troubleshoot rdp troubleshoot ssh troubleshoot os login troubleshoot vms troubleshoot vm operations troubleshoot vm creation troubleshoot resource availability errors troubleshoot bulk api vm creation troubleshoot vm reboots and shutdowns troubleshoot vm suspension troubleshoot vm updates troubleshoot unresponsive vms troubleshoot vm startup troubleshoot fstab errors troubleshoot kernel panic collecting core dumps rescue an inaccessible vm troubleshoot cpu bus locks troubleshoot cpu soft lockups troubleshoot vm configurations troubleshoot arm vms troubleshoot gpu vms troubleshoot nvidia gpu errors generate a nvidia bug report for blackwell gpus troubleshoot nested virtualization troubleshoot using vm screenshots troubleshoot sole tenant nodes troubleshoot vm performance issues troubleshoot sudoers files troubleshoot windows vms troubleshoot windows vms collecting diagnostic information troubleshoot using the serial console troubleshoot using the serial console viewing serial port output troubleshoot instance groups troubleshoot managed instance groups migs troubleshoot os management troubleshoot licenses troubleshoot image import and export troubleshooting sles pay as you go registration troubleshooting ubuntu pro registration troubleshoot metadata server troubleshoot metadata server troubleshoot networking issues troubleshoot common networking issues troubleshoot network drivers troubleshoot vm performance issues troubleshoot storage troubleshoot disk creation troubleshoot full disks and disk resizing troubleshoot disk encryption troubleshoot nvme disks troubleshoot instant snapshots troubleshoot standard snapshots troubleshoot reservations and commitments troubleshoot reservation creation troubleshoot reservation consumption troubleshooting reservation monitoring troubleshoot reservation updates troubleshoot future reservation creation and updates troubleshoot automatic commitment renewal troubleshoot quota errors troubleshoot concurrent operation quota errors troubleshoot workload authentication troubleshoot default service accounts troubleshoot workload to workload authentication ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation compute compute engine guides send feedback about reservations stay organized with collections save and categorize content based on your preferences this document gives you an overview of reservations to learn more about the different types of reservations see choose a reservation type when you create a reservation compute engine verifies that the requested capacity is available in the specified zone if so then compute engine reserves resources creates the reservation and the following is enabled you can consume the reserved resources to create virtual machine vm instances and the reserved resources remain available until you delete the reservation you re charged for the reserved resources at the same on demand rate as running vms including any applicable discounts as long as the reservation exists reservations are useful for growth migrations or disaster recovery how reservations work a reservation provides a high level of assurance of capacity for one or more vms with the configuration the user specifies you might also use a reservation with compute engine commitments or other products that use vms when you create a reservation you define the following properties auto delete the auto delete option specifies to automatically delete the reservation regardless if it s fully consumed or not if you enable the auto delete option the reservation is deleted within two hours from your specified date and time by default or at custom date and time automatically deleting reservations can be useful to avoid unnecessary charges for the reservations that aren t consumed for some time consumption type automatic or specific an automatically consumed reservation default can be consumed by vms with a reservation affinity property that allows them to automatically consume any of these reservations this consumption type is useful if you create and delete lots of vms and you want to consume your reservations whenever possible note if a project has vms that can automatically consume multiple reservations all single project reservations are consumed before any shared reservations otherwise the reservation that a vm automatically consumes is random this order helps improve the utilization and availability of your reservations by ensuring that your projects consume their designated single project reservations before consuming any widely available shared reservations a specifically targeted reservation can only be consumed by vms with a reservation affinity property that targets that specific reservation this consumption type makes it easier to track and control which vms consume which reservations share type single project or shared a single project reservation default can only be consumed by vms that are located in the same project as the reservation a shared reservation can be consumed by vms in the project where the reservation is located and any other project that the reservation is shared with using shared reservations can help improve the utilization of your reservations and reduce the number of reservations that you need to create and manage for more information see how shared reservations work in this document sharing policy the sharing policy specifies if a reservation of gpu vms can be consumed by custom training jobs or prediction jobs in vertex ai by default custom training jobs or prediction jobs aren t allowed to consume reservations of gpu vms to change this see how to create or update reservations to be consumed in vertex ai vm count the vm count is the number of vms with matching properties and zone that you want to reserve when creating a reservation after you create the reservation you can modify the vm count vm properties the vm properties describe the hardware requirements memory and cpus and optional resources local ssd disks and gpus for the vms that you want to reserve when creating a reservation you can specify these properties directly specify the properties based on an existing vm or specify the properties by using an instance template a vm can consume a reservation only if both the vm s properties and reservation s vm properties exactly match for more information see requirements in this document optional resource placement policy compact a compact placement policy indicates that the reserved vms should be located as close to each other as possible to reduce network latency between them when you stop suspend or delete a vm that consumes a reservation the vm no longer counts toward the reservation the reserved capacity becomes available again if you want to delete a reservation to release the reserved capacity but keep any vms that consume the reservation then consider the following you can delete an automatically consumed reservation without stopping or suspending vms after you delete the reservation any vms that were consuming it keep running you keep incurring charges for them you can only delete a specifically targeted reservation if no vms consume it if you stop or suspend vms then after you delete the reservation you can only restart or resume the vms if you create a new specifically targeted reservation with a name zone and properties that match the deleted reservation how shared reservations work each vm in a shared reservation can be consumed by a vm in either the project that created the reservation owner project or any of the projects the reservation is shared with consumer projects when a vm stops consuming a shared reservation the shared reservation can be consumed by a different vm in any of the projects that the reservation is shared with if a shared reservation reserves multiple vms vms from multiple projects can consume the same shared reservation simultaneously by default projects can t create and modify shared reservations to create and modify a shared reservation in a project the project must be added to the allowlist of the shared reservations owner projects compute sharedreservationsownerprojects organizational policy constraint if you share a reservation then it is affected by additional quota requirements and has different consumption behavior than single project res...
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