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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 choose a compute engine deployment strategy for your workload stay organized with collections save and categorize content based on your preferences as a cloud architect or it administrator when you plan to run an application in compute engine you need to design a vm topology that you can provision and operate efficiently compute engine offers a range of deployment options for example you could deploy a group of vms that you manage as a single entity or you could provision and manage the vms as individual resources each approach has distinct merits and limitations how do you choose an optimal deployment strategy start by assessing the key requirements of your application review the available deployment options and their relative merits select a strategy that meets your requirements and makes optimal use of the capabilities of compute engine note compute engine provides robust and flexible cloud infrastructure for hosting commercial off the shelf applications and for migrating workloads from your on premises data centers to learn about the hosting options in google cloud for other use cases such as deploying serverless functions and running containerized applications in a kubernetes cluster see app hosting on google cloud assess your workload use the following questions to analyze the key requirements of the workload that you want to deploy your answers will help you map the capabilities of each deployment option listed in the next section to the requirements of your workload for an interactive ai powered alternative to this decision flow you can prompt gemini in the google cloud console with your project goals to get tailored deployment recommendations for more information see design your compute infrastructure with gemini important when migrating an on premises workload to the cloud consider any special requirements for the cloud version of the application for example the on premises deployment might run in a single data center whereas you might need the cloud topology to span more than one google cloud zone for higher availability application state is the application stateful a stateful application stores certain data such as the client or session id until that data is no longer necessary for example in an online shopping app the shopping cart service might store details of items that are added or removed as the user continues shopping and persist the final cart state when the user starts the check out process a stateless application does not need to store any client transaction or session data for example a web server might close a session after serving the content that the client requested to learn more about stateful and stateless applications see how stateful workloads are different from stateless workloads should any instance specific metadata be preserved when your vms reboot or when compute engine recreates autoheals the vms provisioning should the vms use a mix of machine types or images for example do some vms need memory optimized machine types while the others use general purpose machine types should the infrastructure scale automatically in tune with changes in load so that you maintain an optimal balance between cost and response time can all the vms run within a single zone vpc network and subnet should the application run in the same zone as certain other resources for example does the application require a low latency connection with a database operations do you want to manage the vms as a single group for example would you like to automate rolling out application updates across all the vms do you need to use a custom or third party tool to manage the vms do you need control over handling failed vms for example if a vm fails would you like it to remain stopped while you determine the root cause for the failure do you need control over the start stop suspend resume sequence or schedule of your vms for example to save cost do you plan to stop the vms during weekends or for certain hours of the day resilience does the application need protection against zonal failures in other words if a zone is down would you like the application to continue serving requests from vms in other zones in the region if a vm stops or crashes for any reason or if the application doesn t respond to requests should compute engine recreate the vm automatically does the application need fixed internal or external ip addresses for the host vms now that you ve assessed your requirements learn about the deployment options that compute engine offers review the available deployment options review and understand the features and relative advantages of the options that you can consider for deploying your workloads to compute engine standalone vms with this option you choose the machine type image disks and other attributes individually for each vm that you provision and you manage the vms as separate resources unmanaged instance group you can provision standalone vms and add them to an instance group you can then use the unmanaged instance group as a backend to a load balancer managed instance group mig a mig is a group of identical or similarly configured instances that you provision by using an instance template you can make a mig stateful so that specific disks or metadata are preserved for a stateless mig you can enable autoscaling and configure a scaling policy while creating a mig you can choose to deploy the vms within a single zone or distribute them across more than one zone in a region for high availability the following table summarizes the key features of each deployment option capability standalone vms unmanaged instance group statef...
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