Google Kubernetes Engine vs. IBM Turbonomic

If your application is complex, if it's planet-scale, or if you need autoscaling, then Kubernetes is best suited. If your application is straightforward, you can opt for App Engine or Cloud Run. In many cases, you can prefer to run the cloud on GKE. But once you deploy on Kubernetes, you get the flexibility to try different things. But if you don't seek flexibility, it's not an option for you.

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Datacenter Consolidation and Hardware Optimization: This scenario is relevant to you as a hardware manager. It applies when you have physical servers (like Power or System z) and want to maximize virtual machine density. Why it works: IBM Turbonomic analyzes the peak usage times of each VM. If VM "A" is active during the day and VM "B" at night, it places them on the same physical host. Ideal scenario: Data migration projects or when you're told, "[...], there's no budget for more servers this year, make everything fit on what we have." Consolidación de Datacenters y Optimización de Hardware,Este escenario te toca de cerca como encargado de Hardware. Cuando tienes servidores físicos (como los Power o System z) y quieres maximizar la densidad de máquinas virtuales.Por qué funciona: IBM Turbonomic analiza las horas pico de cada VM. Si la VM "A" es activa de día y la VM "B" de noche, las coloca en el mismo host físico.Escenario ideal: Proyectos de migración de datos o cuando te dicen: "[...], no hay presupuesto para más servidores este año, haz que quepa todo en lo que tenemos". This review was originally written in Spanish and has been translated into English using a third-party translation tool. While we strive for accuracy, some nuances or meanings may not be perfectly captured.

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• Engine upgrade rollout strategy - well documented and configurable
• Integration with other Google Cloud services like the Compute Engine, SaaS databases, and some cloud networking like Cloud Armor
• Graphical interface for a lot of operations - either for a quick peek/overview or actual work done by administrators and/or developers (via the Google Cloud Console, for example)

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• Presentation is nice. Its easy to understand what your looking at and the data that is being presented to you.
• Properly identify resource utilization and recommendations for action on how VMs can be improved and resources can be better utilized.
• It was also able to tell us the same information and analysis for cloud resources. I was not expecting that.

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• Support of IPv6.
• Better GitOps.
• A "serverless" Kubernetes so we can install Google config connector will be really awesome.
• Container-native load balancers do not support internal TCP/UDP load balancers or network load balancers.

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• It would be nice if the UI included a break-down of features that are both licensed as well as un-licensed. That way, you could not only see what you have, but what you don't.
• The right-sizing recommendations are great, but very little info is given about why the recommendation is being made. More info would not only increase understanding, but would also help drive decision-making.

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We are certainly happy with Turbonomic as a whole and have invested quite a bit of time and effort into learning the ins and outs of the product. We have our reporting setup the way we want it and have gained definite value from these features. I will say though that many products nowadays are offering more native monitoring, reporting, and alerting features which may eventually steer us away from this product

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• Google Kubernetes Engine has a good UI and documentation that facilitates setup and helps get projects moving along quickly
• Its built-in logging integrations with StackDriver make it easier to monitor the application and log issues quickly
• Automated orchestration, deployment, and scaling of nodes and networking are all easily configurable with yaml files

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Excellent approach to larger VM organizational management. They have an very clean integrated dashboard that allows us to see everything in our environment and what that is doing in real-time. It works on multiple hyper-visors really well and integrates capacity planning on my local site as well as my cloud locations.

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VMTurbo has not caused any outages by not doing what we expect it to do.

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It allocates resources among applications by showing more on the cost breakdown by cloud service, with metrics on cloud provider information like Azure Management, Identity, Networking, Storage with costs per day, and total services costs. This then could facilitate and show the corresponding actions thereafter upon scaling.

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Very good Kubernetes distribution with a reasonable total price. Integration with storage and load balancer for ingress and services speed up every process deployment.

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When I contact support I get a quick response and they are able to solve my problem quickly. I also get a sense that they want to make sure that we are getting value from the product and walk me through whatever steps are needed to accomplish my goals.

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Alex (from VMTurbo) has worked with the product for years and helped develop the product. He was very knowledgeable and was able to provide our support team with details knowledge on how to get our deployment configured correctly as well as help with another VMTurbo POC within another customers environment.

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After buying VMTurbo Operations Manager, I was invited to an online user training event. I felt this training was effective and dug just deep enough to be informative yet still keep my attention. Additionally, the webinar was free.

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The implementation was very simple. Just upload an OVA file and power on the VM. Once it comes up enter some networking information and you can then access the web interface. From there, just begin configuring the system for your environment by adding you license and the various virtual environments and storage through the inventory tab

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GKE spins up new nodes a LOT faster than AKS. GKE's auto scaler runs a lot smoother than AKS. GKE has a lot more Kubernetes features baked in natively.

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As the organization had experience of years in using IBM products, we had the confidence that they will provide us with great support. And we needed a reliable solution as a financial institute to ensure continuous operations. Even though the price was very high, we made the correct decision to go ahead with IBM Turbonomic as the feedback from existing users in the region was very positive. We needed a solution which was capable of handling our automation requirements. All these were green in IBM Turbonomic.

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It’s very scalable.

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• When issues came up, we reached out to some folks at GCP and they seemed to be very prompt and attentive to our needs. They were always willing to help and provide additional details or recommendations or links to resources. This kind of support is very helpful as it allows us to navigate GKE with more confidence.

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Professional services were always there to guide us in our transformation to the cloud. They understood our business model and then were able to provide guidance on what we needed from the tool.

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• Reduced cloud computing costs.
• Easier management of applications.
• Extra time investment to learn how to setup applications in Google versus Amazon.

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• Application performance has been a big one. With Turbonomic keeping everything running at top performance, it can make changes when extra resources are need, quicker than somebody being notified and then making the necessary changes.
• Turbonomic has been a great cost savings for us on multiple occasions. We use it every time we are improving servers.
• With the planning feature we get the best performance form new hardware purchases

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