A Good option to manage your Self hosted apps.
September 22, 2024

A Good option to manage your Self hosted apps.

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google Compute Engine

So currently, we are using Google Compute Engine for our Jenkins Server, where our Retail Finance jobs are configured. We are using two GCEs of type CentOS 7: one for our Master Jenkins controller, where all the pipelines are configured, and the other for Google Computer Engine, which is being used as a slave agent, which our job uses to run the pipeline. Our slave GCE uses the memory-optimized machine configuration of series M2 because, daily, multiple jobs run simultaneously, which almost uses more than 20 executors as multiple QAs run their unit testing workloads on the same server. In contrast, our Jenkins master uses the simple general-purpose machine configuration as it only has the job and credentials info.

Pros

  • It provides the ability to create your instance using the Google cloud command line if you do not have access to the Google Cloud Console and have appropriate permissions.
  • It offers multiple machine-type configurations that you can choose from depending on your organizational workload.
  • It comes with an instance scheduling option, which helps you schedule your VMs and save your bills on unnecessary running GCES.

Cons

  • GPU is not available for N2 series GCEs, understandable for E2 series but the N2 series GCE must have the option to add GPUs.
  • Instance costs are very high for memory-optimised machine types. A single instance costs us almost $40000 in a month.
  • A bit challenging and complex using Google cloud to automate GCE instance VM creations.
  • The negative impact observed only is mostly related to higher instance costs.
  • Earlier, we were using an AWS EC2 instance for the Jenkins Controller and an AWS ECS for the slave. The problem we faced was that the tools installed on AWS ECS automatically updated, breaking our running pipeline. Then, we migrated to GCEs, which were actually better and easy to set up.
  • We normally use the in-browser option to administrate our GCEs, which is actually more time-saving than other log in options with appropriate access configured at the organizational level.
So far, I have used it and have not experienced any latency, lag, or Data Loss. It is a better option if you are running heavy workloads or critical applications. If your organization is partnered with Google Cloud, it also provides multiple computing options, apart from GCEs, such as Google Kubernetes Engine, where you can run your microservices.
So far, I have not experienced any outages or application errors related to Google Compute Engine. I have had a smooth experience whether accessing, setting up, or managing. For the unplanned outage, we always have a failover setup, which has not been triggered until now in the regions that we have deployed.
When configuring Amazon ECS, it is a bit confusing as you are not able to find the actual issue. You need to enable Additional AppInsights to get detailed level info, which is not a concern when configuring on the Instance Level. Moreover, Azure VM does not provide an in-browser option; instead, it is Azure Bastion, but for that, you have to enable a dedicated subnet, which is a bit unnecessary.

Do you think Google Compute Engine delivers good value for the price?

Not sure

Are you happy with Google Compute Engine's feature set?

Yes

Did Google Compute Engine live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of Google Compute Engine go as expected?

Yes

Would you buy Google Compute Engine again?

Yes

You can use Google Cloud Compute Engine as an option to configure your Gitlab, GitHub, and Azure DevOps self-hosted runners. This allows full control and management of your runners rather than using the default runners, which you cannot manage. Additionally, they can be used as a workspace, which you can provide to the employees, where they can test their workloads or use them as a local host and then deploy to the actual production-grade instance.

Google Compute Engine Feature Ratings

Infrastructure-as-a-Service (IaaS)
8.3
Dynamic scaling
9
Elastic load balancing
10
Pre-configured templates
7
Pre-defined machine images
8
Operating system support
7
Security controls
9

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