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ver best practices for sql server vms create create a high performance sql server vm add a sql server license to an existing linux server add a sql server license to an existing windows server configure sql server on google cloud using google cloud netapp volumes use file storage to configure sql server failover cluster instance use block storage to configure sql server always on availability groups configure windows set up alwayson availability groups using an internal load balancer set up alwayson availability groups using a distributed network name set up a failover cluster vm that uses s2d set up a failover cluster vm with multi writer disks configure linux set up a sql server cluster on linux with always on availability groups and pacemaker set up a sql server failover cluster on linux with multi writer disks migrate migrate a sql server database from aws ec2 to compute engine migrate a sql server database from windows to linux disaster recovery disaster recovery for microsoft sql server disaster recovery for microsoft sql server on persistent disk disaster recovery for microsoft sql server on hyperdisk deploying microsoft sql server for multi regional disaster recovery back up sql 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 next generation dynamic resource management stay organized with collections save and categorize content based on your preferences n4a vms powered by google axion processors n4d vms powered by 5th generation amd epyc turin processors and n4 vms powered by 5th generation intel xeon processors use titanium these machine series also use next generation dynamic resource management to drive cost efficiency by making better use of the physical resources available on host machines and also uses a custom built cpu scheduler and performance aware live migration to balance workload performance needs with available resources these are the same technologies that google search google ads google maps and youtube services use to run their latency sensitive workloads efficiently next generation dynamic resource management also has better numa affinity more accurate prediction of resource requirements and faster rebalancing using performance aware live migration how dynamic resource management works virtual cpus vcpus are implemented as threads that are scheduled to run on demand like any other thread on a host when the vcpu has work to do the work is assigned to an available physical cpu on which to run until it goes to sleep again similarly virtual ram is mapped to physical host pages using page tables that are populated when a guest physical page is first accessed this mapping remains fixed until the vm indicates that a guest physical page is no longer needed dynamic resource management enables compute engine to better use the available physical cpus by scheduling vms to servers based on resource demand and scheduling vcpu threads to physical cpus such that wait time is minimized in most cases we can do this seamlessly so google cloud can run vms more efficiently on fewer servers components of dynamic resource management compute engine uses the following technologies for dynamic resource management larger more efficient physical servers core count and ram density have steadily increased such that now the host servers have far more resources than any individual vm google continually benchmarks new hardware and looks for platforms that are cost effective and perform well for the widest variety of cloud workloads and services allowing you to take advantage of the newest technologies when they re available intelligent vm placement google s cluster management system observes the cpu ram and other resource demands of vms running on a physical server it uses this information to predict how a newly added vm will perform on that server it then searches across thousands of servers to find the best location to add a vm these observations ensure that when a new vm is placed it is compatible with its neighbors and unlikely to experience interference from those instances performance aware live migration after vms are placed on a host compute engine continuously monitors vm performance and wait times if the resource demands of the vms increase compute engine can use live migration to transparently shift workloads to other hosts in the data center the live migration policy is guided by a predictive approach that gives compute engine time to shift the load often before any wait time is experienced by the vms hypervisor cpu scheduler the hypervisor cpu scheduler dynamically maps virtual cpu and memory to the physical cpu and memory of the host server on demand this dynamic management drives cost efficiency in vms by making better use of the physical resources efficient use of resources means compute engine can run vms more efficiently on fewer servers allowing google cloud to pass on savings to users note dynamic resource management applies to n4a n4d n4 and e2 vms first generation dynamic resource management e2 was the first vm series to offer dynamic resource management using a virtio memory balloon device virtio memory balloon device with e2 vms memory ballooning is an interface mechanism between host and guest to dynamically adjust the size of the reserved memory for the guest e2 uses a virtio memory balloon device to implement memory ballooning through the virtio memory balloon device a host can explicitly ask a guest to yield a certain amount of free memory pages also called memory balloon inflation and reclaim the memory so that the host can use the free memory for other vms likewise the virtio memory balloon device can return memory pages back to the guest by deflating the memory balloon e2 vms are the only machine family that uses the memory balloon device compute engine e2 vm instances that are based on a public image have a virtio memory balloon device which monitors the guest operating system s memory use the guest operating system communicates its available memory to the host system the host reallocates any unused memory to other processes on demand thereby using memory more effectively ...