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description= This document describes how to plan and implement active-passive and active-inactive disaster recovery for OpenShift deployments on Google Cloud.;
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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 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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 openshift on google cloud disaster recovery strategies for active passive and active inactive setups stay organized with collections save and categorize content based on your preferences this document describes how to plan and implement active passive and active inactive disaster recovery for openshift deployments on google cloud to help you to achieve minimal downtime and rapid recovery in the event of a disaster it provides best practices for backing up data managing configuration as code and handling secrets to help to ensure that you can quickly recover your applications in the event of a disaster this document is intended for system administrators cloud architects and application developers who are responsible for maintaining the availability and resilience of applications on an openshift container platform that s deployed on google cloud this document is part of a series that focuses on the application level strategies that ensure your workloads remain highly available and quickly recoverable in the face of failures it assumes that you have read best practices for disaster recovery with openshift the documents in this series are as follows disaster recovery for openshift on google cloud best practices for high availability with openshift openshift on google cloud disaster recovery strategies for active passive and active inactive setups this page architectures for disaster recovery this section describes architectures for active passive and active inactive disaster recovery scenarios products used google compute engine google cloud global external https load balancer google cloud passthrough network load balancers cloud dns network endpoint groups cloud storage cloud sql persistent disk cloud storage secret manager cloud monitoring vpc network active passive deployments the following diagram shows an active passive deployment scenario for openshift on google cloud as shown in the preceding diagram in an active passive deployment for disaster recovery an openshift cluster in the primary region handles all production traffic a secondary cluster in a different region is kept ready to take over if the primary fails this setup ensures minimal downtime by having the secondary cluster pre provisioned and in a warm state meaning it s set up with the necessary infrastructure and application components but not actively serving traffic until needed application data is replicated to the passive cluster to minimize data loss aligning with the rpo one of the regional clusters acts as the primary active site and handles all of the production traffic a secondary cluster in a different region is the standby for disaster recovery the secondary cluster is kept in a warm state and is ready to take over with minimal delay in case of a primary cluster failure description of components in an active passive dr scenario the architecture has the following configuration primary openshift cluster active located in the primary google cloud region this cluster runs the production workload and actively serves all user traffic under normal operating conditions secondary openshift cluster passive located in a separate google cloud region for fault isolation this cluster acts as the warm standby cluster it s partially set up and running and is ready to take over if the primary system fails it has the necessary infrastructure openshift configuration and application components deployed on it but it doesn t serve live production traffic until a failover event is triggered google cloud regions geographically isolated locations that provide the foundation for disaster recovery using separate regions ensures that a large scale event impacting one region doesn t affect the standby cluster global external https load balancer acts as the single global entry point for application traffic under normal conditions it s configured to route all traffic to the primary active cluster its health checks monitor the primary cluster s availability data replication mechanism continuous process or tools that are responsible for copying essential application data from the primary cluster to the secondary cluster for example databases or persistent volumes state this approach ensures data consistency and minimizes data loss during a failover helping you to meet your rpo monitoring and health checks systems that continuously assess the health and availability of the primary cluster and its applications for example cloud monitoring load balancer health checks internal cluster monitoring these systems are important for the quick detection of any failures failover mechanism a predefined process manual semi automated or fully automated to redirect traffic from the primary to the secondary cluster upon detection of an unrecoverable failure in the primary this process typically involves updating the global load balancer s backend configuration to target the secondary cluster making it the new active site vpc network the underlying google cloud network infrastructure that creates the necessary connectivity between regions for data replication and management active inactive deployments active inactive dr involves maintaining a secondary region as a standby which is activated only during disasters unlike active passive setups where data is continuously replicated this strategy relies on periodic backups that are stored in cloud storage with infrastructure provisioned and data restored during failover you can use tools such as velero integrated with openshift api for data protection oadp to perform periodic backups this approach minimizes costs making it ideal for applications that can tolerate longer recovery times it can also help organizations to align with extended recovery time objectives rto and recovery point objectives rpo in an active inactive dr scenario data is regularly backed up to the standby region but not actively replicated the infrastructure is provisioned as part of the failover process and data is restored from the most recent backup you can use the openshift api for data protection oadp which is based on the velero open source project to perform regular backups we recommend that you store these backups in cloud storage buckets with versioning enabled in the event of a disaster you can use oadp to restore the contents of the cluster this approach minimizes ongoing costs but results in longer rto and potentially higher rpo compared to active passive this setup is suitable for applications with longer recovery time objectives the following diagram shows an active inactive deployment and the failover process the failover process is as follows a dr event is triggered when a monitored service becomes unavailable a pipeline automatically provisions infrastructure in the dr region a new openshift cluster is provisioned application data secrets and objects are restored from the latest backup through oadp cloud dns record is updated to point to the regional load balancers in the dr region as shown in the preceding diagram two separate openshift regional clusters are deployed each in a different google cloud region such as us central1 and europe west1 each cluster must be highly available within its region and use multiple zones to allow for redundancy description of components in an active inactive dr scenario the architecture has the following configuration primary region region a contains the fully operational openshift cluster serving production traffic secondary region region b initially contains minimal resources vpc and subnets infrastructure compute engine instances and ocp is provisioned during failover backup storage google cloud storage buckets store periodic backups oadp or velero for application objects as well as pvs and database backups we recommend that you use versioning and cross region replication for the bucket configuration management git repository stores infrastructure as code iac for example terraform and kubernetes or openshift manifests for gitops backup tooling oadp velero configured in the primary cluster to perform scheduled backups to cloud storage orchestration scripts or automation tools trigger infrastructure provisioning and restore processes during failover use cases this section provides examples of the different use cases for active passive and active inactive deployments active passive dr use cases active passive dr is recommended for the following use cases applications that require ...
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