Google Compute Engine vs. Longhorn Block Storage

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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Longhorn is performing well as storage for databases and in almost any solution that uses exclusive access to volumes (ReadWriteOnce in Kubernetes nomenclature). When write access is required from many clients (ReadWriteMany) Longhorn Block Storage covers its volumes with NFS (file-based) access. Longhorn Block Storage also is well fitted in every architecture where data security (snapshots, backups, multiple replicas) is more important than access speed (in terms on IOPS and MiB/s).

• 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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• Creates read-write many (RWX) volumes
• Longhorn Block Storage is an easy to deploy solution
• Scheduled and on-demand volume snapshots can be created using web GUI
• Volume backups can be stored offsite on any S3 compatible storage solution
• Backups and snapshots can be restored using web GUI

• 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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• ReadWriteMany Longhorn volumes are still using NFS (file-based) protocol in the core.
• Using iSCSI as main protocol instead of FC ties Longhorn to Ethernet-based LAN which is in most architectures much slower that FC-based SAN.
• Longhorn could implement S3 as alternative access protocol to its volumes.
• Backups, and snapshots configuration could be configured at each volume-level by administrators (maybe from additional CRD object?), because currently is configured at storage-class level which is not granular enough.

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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Longhorn is mature software defined storage solution that is still developed and receive new functionalities. From the beginning every Longhorn volume have multiple (at least two) replicas, can leverage manual or automatic snapshots and backup to external S3 volume. Longhorn provides nice and clear GUI for administrators, but also can be managed from CLI.

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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GlusterFS was first Persistent Storage solution used in our Kubernetes-based clusters. It is file-based what in some usages led us to many data corruptions. CEPH is object-based persistent storage which can be used as file-based Persistent Storage in Kubernetes. It is also is much more resource-hungry than other solutions including Longhorn. Dell PowerScale (or Isilon) is a hardware-software solution, that provides volumes that can be accessed by file-based NFS and CIFS protocols. Recently was added access to its volumes with object-based S3 protocol. Longhorn is in the middle. It is block-based, it is build on industry standards like iSCSI, performs very well on 10Gbit or faster LAN and commodity hardware (or in virtual machines)

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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• It has provided a highly available storage solution for almost all our Kubernetes deployments
• We can deploy new app versions with peace in mind because we have working data backups
• Application development is faster because devs can play with data and easily restore it when needed