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ations 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 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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 stateful mig stateless mig template based provisioning of a group of vms mix of machine types and images fixed internal or external ip addresses automatic healing of failed vms control over vm start stop and suspend resume operations setting a group of vms as a load balancer backend disk and metadata preservation for stateful workloads controlled update of specific vms automatic rolling update of all the vms automatic and predictive horizontal scaling the following diagrams show sample deployments side by side to help you understand the key differences standalone vms stateful mig stateless mig this example shows three vms created individually this example shows a mig containing three similarly configured vms provisioned by using an instance template this example shows a mig containing three identical vms provisioned by using an instance template each vm in this example uses a distinct machine type image disks and other attributes the vms were added individually to an unmanaged instance group the vms in this example use a machine type and image defined in an instance template a stateful policy ensures that the boot disks attached to all the vms are stateful per instance configurations add the required stateful data disks in this example the vms inherit the machine type and image from an instance template the disks are recreated when the vm is updated or recreated the mig creates and deletes vms automatically based on an autoscaling configuration you ve now assessed your workload reviewed the deployment options that compute engine offers and are ready to choose a deployment approach select a deployment strategy the recommendations discussed here are based on a mapping of specific workload characteristics to the capabilities of each compute engine deployment option use the following decision making flow if you prefer a visual guide see the decision tree later in this document choose between standalone vms and instance groups requirements recommended deployment strategy at least one of the following requirements is essential for your workload the application must run on vms that use a mix of machine types or images the application needs fixed internal or external ip addresses for the host vms you need control over handling failed vms you need control over your vms start stop or suspend resume operations you need to use a custom script or third party tool to provision and remove vms choose standalone vms if all the standalone vms can run in a single zone vpc network and subnet consider adding the vms to an unmanaged instance group you can then use the unmanaged instance group as a backend to a load balancer skip the remainder of this decision making flow none of the above requirements is essential for your use case use a mig to set up a compute engine topology that is easy to manage highly available and scalable proceed to the next step choose between a stateful and stateless mig requirements recommended mig type the application requires disk and metadata preservation that is the application is stateful choose a stateful mig and configure the disks that compute engine should preserve during disruptive events like vm recreation autohealing and updates proceed to the next step the application is not stateful choose a stateless mig and take advantage of the autoscaling capability during disruptive operations compute engine recreates disks according to the instance template proceed to the next step choose between a zonal and regional mig requirements recommended mig type the application must run in a single zone or protection against zonal failures is not essential choose a zonal mig the application must continue to run even when a zonal failure occurs choose a regional mig important do review and consider the limitations of each mig type decision tree the following diagram guides you through the factors to consider when deciding your compute engine deployment strategy what s next learn more about instance templates learn how stateful migs work learn more about regional migs create a mig autoscale groups of instances migrate an existing workload to a stateful mig 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 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