Google Compute Engine vs. Red Hat OpenStack Platform

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.

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Best suited for - any organization where you have people who already have expertise on OpenStack, Linux & IP networking. Otherwise, the maintenance & operations will be difficult. When the number of deployed VMs reaches its capacity, it becomes very difficult to manage Red Hat OpenStack because there are no in-built fault management & performance management tools available within Red Hat OpenStack. Not suited for - Organizations where people have a culture of working on automated GUI-based tools. Here VMware wins over Red Hat OpenStack. Also where you have mission-critical applications where downtime cannot be tolerated.

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• Scaling - whether it's traffic spikes or just steady growth, Google Compute Engine's auto-scaling makes sure we've got the compute power we need without any manual juggling acts
• Load balancing - Keeping things smooth with that load balancing across multiple VMs, so our users don't have to deal with slow load times or downtime even when things get crazy busy
• Customizability - Mix and match configs for CPU, RAM, storage and whatnot to suit our specific app needs

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• Scaling of application components (VMs).
• Managing the networking between virtual machines.
• Management of VNFs & the underlying infrastructure.
• Availability & uptime of VMs because of features like VM migration & evacuation.

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• Built-in monitoring via Stackdriver is quite expensive for what it provides.
• Initially provided quotas (ie. max compute units one can use) are very low and it took several requests to get an appropriate amount.
• Support on GCE is limited to their knowledge base and forums. For more hands-on support provided by Google, you must pay for their Premium services.

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• User management really needs improvement - when compared to AWS or GCP.
• Security of the overall platform needs to be improved.
• The whole architecture needs to be modular which is not. Ex - Upgrading any particular component (nova, neutron, cinder) should be possible without upgrading the whole Red Hat OpenStack version.
• The creation of HEAT templates for complex applications is still a challenge & has a dependency on external tools.
• Stack creation still requires parameters modification at controllers & compute because of the complex nova-scheduler algorithm.

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Its pretty good, easy and good performance. Also, interface is very good for starters compared to competitors. Infra as Code (IaC) using Terraform even added easiness for creation, management and deletion of compute Virtual Machines (VM). Overall, very good and very easy cloud based compute platform which simplified infrastructure, very much recommend.

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Having interacted with several cloud services, GCE stands out to me as more usable than most. The naming and locating of features is a little more intuitive than most I've interacted with, and hinting is also quite helpful. Getting staff up to speed has proven to be overall less painful than others.

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Google Compute Engine works well for cloud project with lesser geographical audience. It sometimes gives error while everything is set up perfectly. We also keep on check any updates available because that's one reason of site getting down. Google Compute Engine is ultimately a top solution to build an app and publish it online within a few minutes

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It works great all the time except for occasional issues, but overall, I am very happy with the performance. It delivers on the promise it makes and as per the SLAs provided. Networking is great with a premium network, and AZs are also widespread across geographies. Overall, it is a great infra item to have, which you can scale as you want.

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• The documentation needs to be better for intermediate users - There are first steps that one can easily follow, but after that, the documentation is often spotty or not in a form where one can follow the steps and accomplish the task. Also, the documentation and the product often go out of sync, where the commands from the documentation do not work with the current version of the product.
• Google support was great and their presence on site was very helpful in dealing with various issues.

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Google Compute Engine provides a one stop solution for all the complex features and the UI is better than Amazon's EC2 and Azure Machine Learning for ease of usability. It's always good to have an eco-system of products from Google as it's one of the most used search engine and IoT services provider, which helps with ease of integration and updates in the future.

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Only because of low cost & zero licensing of Red Hat OpenStack

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It works really well with other Google Cloud services, making it easy to build scalable solutions across different teams and locations.

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• With Google Compute we don't have the overhead of managing our own data centers reducing costs and reducing the staff needed to manage systems.
• As I said earlier, Google's costs are ~1/2 of AWS, so we are able to see a ROI much faster.

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• Saved CAPEX for sure (I can't quote a figure).
• Saved Opex also - because a large support community is already available.
• Increased complexity of system setup though.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info