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eploying 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 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engine guides send feedback stateful managed instance groups stay organized with collections save and categorize content based on your preferences you can build highly available deployments of stateful workloads on vm instances using stateful managed instance groups stateful migs stateful workloads include applications with stateful data or configurations such as databases legacy monolith applications and long running batch computations with checkpointing with stateful migs you can improve the uptime and resiliency of such stateful applications with autohealing automatic recovery of failed workloads multi zone deployments and automated rolling updates a stateful managed instance group preserves the unique state of each instance including instance name attached persistent disks ip addresses and metadata on vm restart recreation autohealing or update this page describes when to use stateful migs and provides a high level overview of how they work for more information see how stateful migs work to learn how to set up a stateful mig see configuring stateful migs how stateful workloads are different from stateless workloads you can use managed instance groups to support both stateful and stateless workloads the key difference between stateful and stateless workloads is that stateful workloads preserve individual vm state for example a database shard or app configuration on the vm s disks while stateless workloads like a web frontend don t retain any state on the individual vms you treat vms with stateful workloads like custom built machinery you care about vm identity name ip address metadata and data on each individual machine you cannot easily scale stateful workloads horizontally because scaling could require data replication creation or deletion of data shards or changing the overall application configuration when recreating or updating a machine with a stateful workload you must preserve the vm s unique state examples of stateful applications include cassandra elasticsearch mongodb mysql postgresql and kafka you treat vms with stateless workloads as interchangeable and only care about the number of serving vms that you have no one vm is treated any differently than another you can quickly scale stateless workloads horizontally by adding or removing vms when updating your application you can delete machines and replace them with new ones with different names ip addresses metadata and disks when a stateless vm is deleted or recreated all data on the machine is lost the disks are deleted or recreated from scratch a web frontend is an example of a stateless application stateful mig stateless mig workload stateful workloads where disks ip addresses and or metadata are preserved on vm recreate operations highly available and scalable stateless workloads where disks and ip addresses are recreated from scratch on horizontal scaling autohealing auto updating and vm recreation mig features autohealing automated rolling updates multi zone deployments autohealing automated rolling updates multi zone deployments autoscaling preservable items instance names persistent disks including support for disks that are not defined in the instance template instance specific metadata ip addresses instance names all migs support custom and preservable instance names when to use stateful migs consider using stateful managed instance groups stateful migs whenever you deploy a stateful application or cluster to compute engine and would like to improve its availability with autohealing and multi zone deployments or you want to simplify updates of stateful instances by orchestrating update rollouts and controlling the allowed level of disruption to the instances stateful migs are intended for applications with stateful data or configuration such as databases for example cassandra elasticsearch mongodb and zookeeper before deciding on stateful migs consider using fully managed services for example mysql and postgresql are available in cloud sql to focus on your applications and not have to deal with vms data processing applications for example kafka and flink before deciding on stateful migs consider using fully managed services for example dataflow or managed service for apache spark to focus on your data processing tasks and not have to deal with vms other stateful applications for example teamcity jenkins bamboo dns servers with stateful ip address and custom stateful workloads legacy monolith applications these applications store application state on a boot disk or additional persistent disks or they rely on stateful configuration such as specific vm instance names ip addresses or metadata key values batch workloads with checkpointing with this configuration you can preserve checkpointed results of long running computation in anticipation of workload or vm failure or instance preemption stateful migs can recreate a failed machine while preserving its data disk so that your computation can continue from the last checkpoint to achieve resilience against zonal failure your application must replicate data across multiple instances at the application level for example elasticsearch and cassandra support such functionality you can use a regional mig to make such an application resilient to zonal failure by deploying redundant replicas to multiple zones and relying on your application s data replication functionality in the event of a zonal failure your data is served from available replicas in the remaining zones review the limitations to verify if a stateful mig fully meets your requirements note your workload must start automatically on vm boot so that it can continue serving or computing after autohealing auto updating and instance recreation events what makes a mig stateful a mig is considered stateful if you have created a stateful configuration you can create a stateful configuration when you create your mig or you can convert a group from stateless to stateful after its creation by adding a configuration you create a stateful ...
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