Google Compute Engine Reviews

I use the solution in my company for a website, specifically for some web applications. I just use Google Compute Engine as a server for my website and my web application.

As far as I know, Google offers the biggest cloud services, and I use it. With the tool, I can scale the services.

I work for an IT company as an architect. I design all the infrastructure and systems, and Google Compute Engine is one of the things that we provide solutions for. Customers using the Google Compute Engine use it for lift and shift models when moving from their on-prem environment to the Google Cloud. Usually, they are running the same type of workload they run on their on-premises on the Google Cloud.

One of GCE's best features is the managed instance groups. We typically use managed instance groups for high availability. You can set certain parameters for managed instance groups where if the load of the computer or server increases beyond 80%, for example, the solution will automatically spawn another instance, and the load will be automatically divided between two systems. If the load is 80% of one of the VMs or GCEs, once the load is divided, it comes down to 40%, so the availability of your systems goes up. However, that all depends on the parameters or configurations we put on the instance group.

You also have regular health checks on these managed instance groups, which are configurable. If these health checks determine something wrong with the VM, they will automatically kick off or spawn a new GCE instance. This way, the outage time is less. Previously, on-premises, unless somebody reported the issue to the helpdesk saying that a particular service was unavailable, then a support team would need to troubleshoot what went wrong, which takes a long time. At least 30 minutes to one hour. But by using these managed instance groups, we can reduce the outage time, and second, we can configure them with minimal resources, bringing down our cost. And if the load increases, the managed instance groups automatically respond to new things. Subsequently, our costs decrease.

We have a wide range of VMs. There are general-purpose VMs that can be used for hosting general-purpose applications. If some of our applications are memory intensive, then we have a lot of VMs in the M1 series. We can use a range of memory-optimized VMs for these things. We have C-series VMs for compute-intensive applications. If we use some mathematical formulas and require a very high throughput from that, there are GPU-optimized VMs used for machine learning or 3D visualizations in rendering software. GPU-enabled VMs are pretty powerful and responsive.

Again, the best part is that we can spin them up when we need them, and once we're done with our work, we can shut them down, allowing tremendous cost savings for any customer. Previously, if we wanted a very high-configuration VM, we had to own the entire hardware and have it on our on-prem data center. And once we'd done with a particular activity, the system would just be lying there on our premises. That is not the case now. We use and decommission it, so we're only billed for the time we're using the product. One of the best things is the preemptible VMs or Spot VMs. These are the cheapest VMs in Google Cloud, but it has a string attached to it where Google can shut down these VMs whenever Google teams split. You only get about 90 seconds notice before they shut down this particular VM. There are scenarios where customers can use these preemptible VMs, for example, when running a batch job. Batch jobs are run once or twice daily, depending on the customer's requirement. Once we are done running these batches, we can decommission the VM. Even if, in the middle of this batch job, Google shuts down these VMs, we can pick up the processing from wherever the VM left off. These are some of the beautiful things we have on Google Cloud concerning the Compute Engine.

I have worked with SAP migration. During SAP migration, we have different flexibility of the classes of compute for the load for SAP's load. My whole career was in SAP. So when I moved to cloud, I also did migration of SAP loads into the cloud.

Google Compute is highly scalable. Their private network is as big as the global internet, which is a significant advantage. Additionally, the flexibility of compute and storage types is quite adapted and designed for SAP's big model.

Google Compute Engine is the base and the fundamental part of the infrastructure as a product of the Google Cloud platform. Everything is based on the Compute Engine.

We use Google Compute Engine to create instances and then use the instance to connect and integrate with other products on the platform. I'm a data scientist and I use Vertex AI a lot. Each Vertex AI has a fragment called Compute Engine. With Compute Engine, you can integrate the database and use Vertex AI with it.

With Compute Engine, Google manages all hardware. You don't need to provision or pre-provision your computer engine. You don't have to worry about where your Compute Engine is, whether it's in Frankfurt or New York since it solely depends on your demand. It is quite elastic. For example, ten years ago we used the on-premise Compute Engine and we constantly had to worry about scalability. With Google Cloud Compute Engine, there is no need to worry about capacity. If you need it bigger, you can upgrade it. If you need it smaller, you can downgrade. Another thing I like about it is that you pay-as-you-go. I think that the minimum you have to pay for Compute Engine is only one hour. If you don't have to use it, you can just stop it and save a lot of money that way. If you wish to use it continuously, Google offers you a discounted contract. It is very cost-effective and I like its availability as well. 

About eighty people at our company use Google Compute Engine. The data engineering team and the data science team use it.

One of the use cases is autoscaling. It has been quite robust and easy to use for autoscaling. Second, within the console itself, the whole developer experience is far superior as compared to other cloud providers. 

The third, mainly from a networking perspective, is the ability to use multiple regions for load balancing and networking capabilities. I can switch between multiple regions as and when required. 

For specific use cases, in certain types of machines, we don't always get the required compute power. But, it depends from region to region, and sometimes there are resource crunches, and you may not get the required configuration as you need.

The benefits have been mainly from the faster adoption or the ease with which I have deployed this for consumers or my engineering teams. The pace with which I can bring up an infra, that is, the time to market, has been important for me. So that is fast. Again, it's more time-based only, but, yes, the ease with which I was able to migrate from on-prem to Google Compute Engine was quite seamless without having to worry about whether my workloads would continue to run the same or not. So, that was a seamless experience. 

Another one I would specifically highlight is the fact that, from a perspective, most of the cloud providers give that, but I probably am a bit biased towards Google and the way in which it has its storage layer linked.  So, the persistent disk for the local SSDs provides better throughput as compared to some of the other providers, plus it now has these GPUs and TPUs as well.  So that helps a lot.

Compute Engine is an option to create virtual machines in GCP. We can set up our service now. We can use Compute Engine for load balancing, and we can categorize it into three different types: general purpose, compute optimized, and memory-optimized. There are a lot of CPU options to choose from, so we can select the one that best suits our needs. We can create Compute Engine instances anywhere in the world, and we can deploy our services to them. This ensures that our application has a global presence.

Another feature of Compute Engine is its networking capability. It provides networking capability to VPCs, which are private cloud networks. Compute Engine also provides instance templates. We can define our virtual machine configuration in an instance template and then use that template to create new virtual machines. We don't need to specify the region or other details when we create an instance template because instance templates are global services. We can use an instance template to create a new virtual machine at any time with the same configuration.

Another service provided by Compute Engine is managed instances. It allows us to create a group of identical virtual machines. The virtual machines in a managed instance group all have the same configuration. We can ensure that a minimum number of instances are always up and running, and if an instance fails, Compute Engine will automatically create a new instance with the same configuration. This ensures that our service is never unavailable.

Compute Engine also provides a load balancer to distribute traffic to our virtual machines. The load balancer provides a single IP address for our service, and it distributes traffic to the virtual machines in our managed instance group. This allows us to scale our service up or down without having to change any client code.

It's the managed instance. You never feel any delay or service. If something happens, it will automatically create a new instance with the same configuration.  

Google Compute Engine is a typical VM. We use it in Kubernetes host machines. 

The solution is readily available, and software engineers can provision it. It is scalable and allows self-service. 

We use the solution to host a self-hosted helpdesk.

The solution helps to direct SSH into the machine at the click of a button. It also helps to deploy container images right from the UI. There is no need to manage the containers on the machine. I also like the tool’s Spot provision model.