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site title: Next generation dynamic resource management     Compute Engine     Google Cloud Documentation

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s 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 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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 compute engine collects and uses this data to make more accurate rightsizing recommendations verifying the driver installation to check if your image has the virtio memory balloon device driver installed and loaded run the following command linux most linux distributions include the virtio memory balloon device driver to verify that your image has the driver installed and loaded run sudo modinfo virtio_balloon dev null echo balloon driver is installed echo balloon driver is not installed sudo lsmod grep virtio_balloon dev null echo balloon driver is loaded echo balloon driver is not loaded in linux kernels before 5 2 the linux memory system sometimes mistakenly prevents large allocations when the balloon device is present this is rarely an issue in practice but we recommend changing the virtual memory overcommit_memory setting to 1 to prevent the issue from occurring this change is already made by default in all google provided images published since february 9 2021 to fix the setting use the following command to change the value from 0 to 1 sudo sbin sysctl w vm overcommit_memory 1 to persist this change across reboots add the following to your etc sysctl conf file vm overcommit_memory 1 windows compute engine windows images include the virtio balloon device however custom windows images don t to verify whether your windows image has the driver installed run googet verify google compute engine driver balloon disabling the virtio memory balloon device using the virtio memory balloon device enables compute engine to utilize memory resources more effectively so google cloud can offer e2 vms at lower prices you can opt out of the virtio memory balloon device by disabling the device driver after disabling the virtio memory balloon device you will continue to receive rightsizing recommendations however they might not be as accurate linux to disable the device in linux run the following command sudo rmmod virtio_balloon you can add this command to the vm s startup script to automatically disable the device upon vm boot windows to disable the device on windows run the following command googet noconfirm remove google compute engine driver balloon you can put this command into the vm s startup script to automatically disable the device upon vm boot what s next read the blog about dynamic resource management review the n4d machine series information review the n4 machine series information review the n4a machine series information review the e2 machine series information create a vm send feedback except as otherwise noted the content of this page is licensed under the creative commons attribution 4 0 license and code samples are licensed under the apache 2 0 license for details see the google developers site policies java is a registered trademark of oracle and or its affiliates last updated 2026 07 14 utc need to tell us more easy to understand easytounderstand thumb up solved my problem solvedmyproblem thumb up other otherup thumb up hard to understand hardtounderstand thumb down incorrect information or sample code 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