I'm pretty new to Google Compute Engine, I have 5 types of machines and lets' say 10 instances of each type. I don't wan't to do load balancing on them, so I can't use managed instance groups.
is there any 'smarter' way to copy my files to those VMs and run my software on the VMs remotely and automatically than doing this manually?
Basic startup scripts with gcloud/gsutil to copy from Google Storage and then to the respective VMs.
Related
I want to run the same Python script with different parameters on several instances on google compute engine. Currently I setup all my instances by creating an instance group. Then I ssh into each machine and start the Python script with the correct parameters.
I'm able to automate setup that's common for all the VMs, such as mounting buckets and so on, by using startup scripts. But I still have to ssh into each VM and start the Python script with a different parameter for each VM. Hence, I'm wondering if there's some clever and simple way of running the same Python script with different parameters on each instance.
Any suggestions? Thank you!
One solution is to use metadata: Create your instances separately instead of with an instance group. Make them identical (ie use the same script) except for metadata - use the metadata to give each instance it's unique parameters. In your script, fetch the metadata to determine how to proceed separately.
I have a vm instance on google compute engine, which deploys a python web application. But now i want to have a snapshot of the online vm deplyed on my local server. Is it feasible to do that? if yes, how do i proceed to do that?
I'm thinking about upgrading my company's integration server with a the repos on a separate disk that would be shared with a backup server. Like so:
[Main Integration Server] ---R/W--- [Repo Vdisk] ---R/O--- [Backup Integration Server]
My problem is that according to the GCE docs, if I attach the same Vdisk to more than one instance, all instances must only access the disk in read-only mode. What I'm looking to do would be to have one instance access it in read-write, and one in read-only mode.
Is this at all possible without powering up a third instance to act as a sort of "storage server"?
As you quoted from the docs and as mentioned in my earlier answer, if you attach a single persistent disk to multiple instances, they must all mount it in read-only mode.
Since you're looking for a fully-managed storage alternative so you don't have to run and manage another VM yourself, consider using Google Cloud Storage and mount your bucket with gcsfuse which will make it look like a regular mounted filesystem.
I have a perl job that runs for 5 mins at the top of every hour. What is the most cost effective way of running this job on the Google Cloud infrastructure? Running a compute engine VM seems too heavy-weight for this since I'd get charged for the other 55 mins of no use. I don't understand the "Managed VMs" well enough, but it seems like this might be an option, but I'm not sure if pricing is rounded to the hour. Does anyone have any ideas what the best option is so that I only get charged for 120 mins of usage (24 times run * 5 minutes). The script also uses some image processing binaries, so converting to Python won't do the trick.
Managed VMs are linked to Google App Engine. If you have an App in GAE, managed VMs are used to configure the hosting environment for you App using VMs that run on Google Compute Engine and these applications are subject to Java and Python run time. This link can give you an idea on pricing on GAE, however Perl is not a supported language in GAE.
On GCE, you can start up an instance, do the task and then delete the instance without deleting the persistence disk, this will allow you to recreate the instance using this disk, however you will still be charged for the provisioned disk space and you will need to create a script that will spin up the instance and delete it. You can also create a snapshot of your disk and recreate your instance based on the snapshot, this will be little bit less expensive that keeping the disk.
Also, you should look at the type of persistence disks (PD) on GCE, at this link, take a look at the examples provided, since based on your operation, regular PD or SSD PD can make a big difference on price.
You can use the pricing calculator to estimate your charges
When you deploy to App Engine using a managed VM, an compute engine instance (managed by google) is created for you. All request to App Engine will be forwarded to the created compute engine instance.
To run your script in App Engine as a Managed VM, you will have to dockerize your project, as the managed VM runs a docker container.
I don't see a reason to use App Engine managed VM (just for running a script), as the cost will be same as using a compute engine instance.
Probably the most cost effective way is to create a script that:
Launches a compute engine instance
Install perl
Copies your script to the instance
Runs you script in the created instance
To schedule the execution, you can put at home/office a cron job that executes the above script.
I am trying evaluate Google Compute Engine (GCE) for a cloud project in our company. We have some experience in working with Amazon Web Services but would like to know if GCE is a better alternative for our project.
I have following questions. Our choice for the project will be based on the answers for the questions so please help me with these queries.
Is there an equivalent of AWS Route53 and Elastic Load Balancer on Google cloud? If they are not available then how do we load balance GCE instances?
Is there a concept like regions? (such as us-east-coast-1, us-west-coast-1, etc…). Helpful in making sure that the service is not affected during natural calamities.
Is there an equivalent of Cloud Watch to help us auto scale compute engine instances based on load?
Can we setup a private cloud on Google cloud platform?
Can we get persistent public IP addresses for GCE instances?
Are there any advantages (in terms of tighter integration OR pricing) when using Google services such as Google Analytics, YouTube, DoubleClick, etc?
Load Balancing
Google Cloud Platform's Compute Engine (GCE) recently added a Load Balancing feature. It's lower level than ELB (it only supports UDP / TCP, not HTTP(S)).
Regions
GCE has feature parity. AWS Regions correspond to GCE Regions, and AWS Availability Zones to GCE Zones
Autoscaling (CloudWatch)
Google Compute Engine does not have autoscaling, but Google App Engine does. Third party tools such as Scalr or RightScale are however compatible with Google Compute Engine
Disclaimer: I do work at Scalr.
Private Cloud
Did you mean dedicated instances? Those are not available in GCE.
If you meant VPC, then you can use GCE networks to achieve isolation. You'll also wish to disable ephemeral external IP addresses for the instances you want to isolate.
Persistent IPs
GCE has persistent IPs, they are called "Reserved Addresses"
Integration with other services
You will likely get better latency to Google services you use in your backend (I recall a couple presentations at Google I/O talking about Google App Engine + BigQuery).
For frontend services (Google Analytics), you'll likely see not benefit, since this depends on your users, not your servers.