IBM Turbonomic Reviews

We use Turbonomic to evaluate all of our virtualized clusters. Initially, we were only using Turbonomic for our long-term VMware stacks. Now we are monitoring VMware ESXi 7 and Nutanix AHV stacks. On the server side, we have 400 VMs. We don't evaluate the VDI side because we have 1,100 seats, so it's too expensive. We made a special contract on ELA for vRealize Ops for VDI on that side. It wasn't horrible. It's just the bare minimum to show us if there's a problem in the stack.

We mainly use Turbonomic as a heat map, but we aren't drilling down into the performance of individual applications like Kubernetes. That's Docker or Swarm, but we use other tools to monitor the transaction levels, etc., instead of Turbonomic.

Turbonomic is our overall heat map in our NOC. We fire it up, and when we see a red flag, we dig into it, and off we go, but the basic application components do not have our Dockers linked to them. It's just mainly working on the surface of the virtualization stack itself.

Our infrastructure is solid enough that I get a VDI call about every three weeks. My server farms are built like tanks, so it takes a lot to take them down. We can sleep well at night. Everything is on-prem. The only cloud solutions we use at the college are SaaS systems. We don't put much in the cloud because cloud environments are too vulnerable to hacks and exploits. 

We're going from a silo system to HCI — from 450 hard disks to hybrid flash. While we undergo a significant infrastructure change, we're using Turbonomic to watch VMware because it has aged, and our migration isn't happening the way we want. We will probably reevaluate when the next contract is up for Turbonomic instead.

Once we switch to pure Nutanix, we will reevaluate Turbonomic. I will probably keep it because management is used to Turbonomic's reporting. That saves me much OPEX time building those reports out of Nutanix by hand. I've been here for 16 years, and my CIO has been here for 17 years. They're used to the reports we've been developing over the last decade. We developed them using VMTurbo. We set the standard with that first tool for reporting.

We use Nutanix Prism Central to manage everything on the Nutanix side, but Turbonomic provides ancillary information that gives me a holistic view of reporting and more features that Prism Central doesn't cover. Turbonomic provides linkages, visual aids, graphs, charts, etc. 

I'm the one who uses it. It's up on my NOC screen. We log and monitor it pretty much every day. Then, once a month, it generates reports on its own. In that sense, it's used daily or monitored daily. We watch what it's reporting every day on the heat map. Regarding issues and such, maybe every couple of weeks we have something pop up that we look at. 

Turbonomic helped us with cluster projections. We have different-sized hosts in a single cluster. I have two-socket and four-socket hosts sitting in a cluster, so the impacts aren't easy to understand in aggregate. Turbonomic helps to evaluate what will happen in hypothetical configurations. I can forecast the effect of dropping one server and adding another. If I drop a pair of 48 cores and add a single 96 at a different gigahertz, will that be adequate? It can tell you if you need to add more cores to manage your server hardware purchasing. 

It also assists us in evaluating performance risks. The dashboards show what current risks are happening, and we use the planning features to see the what-ifs. We check the heat map daily. If something pops up there, we check it out to prevent issues from happening down the chain. It's mainly on the VMware side with the older VMXs. We haven't found anything on the Nutanix side to be worried about.

Turbonomic has helped us address performance degradation under VMware. It identifies when there's a bottleneck in the storage line, so we can start moving some virtual disks around to different ones. It helps in the older silo structure. The performance degradation is on the VMware or the fiber channel SAN side. Some of the SANs are nine years old. 

It is able to identify points before we even noticed them. We're meeting all our SLAs because it never gets to the point where they catch something. They might say, "Oh, it seems a little slow," and then they'll return from lunch, saying, "Oh, it's okay again." 

We log into it in the morning and let it sit up in the NOC. We take a peek when it shows something. We'll check it out if it's red, but it'll usually clear up if it's yellow. For example, all systems might run at 110% immediately before registration closes while students try to get their last class for their senior year registered before the other students. We'll return to our normal 20-30% usage in about an hour and a half. 

They won't notice a thing because we'll be moving to more of a Kubernetes Docker-style system with Nutanix Carbon. I will probably try to integrate that with Turbonomic. We will probably connect Turbonomic deeper into that stack because that will be able to pull and spin up new Dockers automatically on hardware and not within anything else, giving the server room to spin up another Docker. Theoretically, I've got room for about 600 more containers, and we currently use 15. 

We're centralized IT. I use Turbonomic mainly as a showback because we don't charge our different departments. There technically is no charge in our current Red Hat licenses, and that's picked up. We pick that up and get requests in. There is no self-service here.

The instructor says, "I need 400 cores and two terabytes of RAM to run my analysis." I'm like, "That's how they run it on a supercomputer. We don't have those here. Now, if your research grant wants to buy us one, sure, we'll set it up. Tell us where the half-million dollars is, and we'll set it up for you." There's no self-service here, but we use it for a showback.

We had Turbonomic load-balancing all our clusters, and we did not let VMware load-balance our clusters because of the algorithms. Their marketing and share algorithms were much more precise than VMware's because I had disparate-sized servers. 

VMware liked to put a heavy load on my little boxes and leave my big boxes alone, or it stuffed the big boxes full and left the little boxes alone. Turbonomic keeps everything about even. Their algorithm for load balancing was much cleaner until the ESXi 7 than VMware. That made the hardware more cost-effective because I didn't have little guys sleeping in a corner someplace sucking up hardware, power, and cooling while not doing any work all day.

Resource starvation has never been an issue for use. We run different resource pools, and we've never had any service hit 100%. I have redundancies and reserve capacities needed to weather any storm. We use Turbonomic primarily to monitor and maintain equal resources on all servers. 

We've never had a server hit 100%. I might have one hit 80% periodically before they moved something around. We've been in the VMware game since 2.X back when a monster server had four cores and 32 gigs of RAM. We've been virtualized over 80% for the last 12 years. We've been heavily virtualized for over 80% of the previous decade. We knew virtualization was the way it was going and went for it.

It reduced our operational expenditures because I have reclaimed some of the time typically spent generating reports. It's part of our system, and we just use it. Turbonomic is part of our network operations center. The dashboard is on my screen, so I can see if the indicators turn yellow or red. I can address the issue before it gets to the point where I'm getting calls from the service desk.

Turbonomics helps us understand resource usage and enables us to make decisions about how to utilize those resources. For example, let's say you check your monthly water bill and see that it's up 25 percent, but you don't know why. It keeps going like that until you check your toilet and find that the seal is slightly broken. 

Every now and then, a tool will keep running when you're not using it. Turbonomics will identify when the toilet is running and fix it for you, so your bill goes up. It automatically makes the adjustments.  

Our organization uses cloud-based infrastructure, so everything is metered. You're wasting money if you have a system running but you're not using it. Turbonomic can take that system offline or suspend it. It can also adjust resources so that they're optimal for a particular workload. It gives you visibility and also takes action automatically. 

Turbonomic offers a single platform for optimizing complex, mixed environments. With APM integration, it can tell you how each application is performing and rationalize the resources to ensure the optimal configuration for performance, so there's no waste. It will make those adjustments for you based on application trends. It covers the complete stack all the way up from your UI down to your core host machine if you're running on a VM or the physical machine if you're running on a device. It tries to ensure consistency and hasn't caused any additional overhead on my applications. 

The automation features are helpful because engineers no longer need to focus on fixing issues. They can spend their time on innovation and more important things. Right now, a lot of the work is still being done manually. When I get alerts, I send a staff member to make adjustments, but I can't do that in real time. It might take days or weeks to address a ticket. The meter is constantly running while we're waiting to fix that so that money is going down the drain.

It's hard to quantify how much time we save using Turbonomic, but it's around 20 percent. I only do analysis and remediation on maybe Tuesday and Thursday. It's not about the time that I waste; it's about the time and cost we can recover. I no longer need to wait until Tuesday. If something happens on the weekend, the tool can fix it on Sunday. It does save time, but the bigger aspect is cost savings.

The core expense of our setup is cloud costs, so cloud management is a huge piece of our financial operations. We're constantly looking at cloud spending and ways to make that more efficient. 

We leverage IBM Turbonomic to manage our 28 on-premises data centers, effectively optimizing resources and ensuring their operational efficiency. It helps us automate workload scaling to guarantee consistent application performance. Additionally, while we primarily use Turbonomic for on-premises management, we also utilize it within our AWS environment for cost optimization purposes. This allows us to identify and implement cost-saving measures within the cloud.

One of our key challenges was finding a tool that could provide a holistic view of our entire environment and report back key data points, such as CPU and memory usage. This would allow us to identify potential areas for cost savings. We were frequently receiving requests to increase resources like memory and CPU, so we needed a tool that not only gave us historical data but also empowered us to take action, whether that meant automating changes or manually making the changes and IBM Turbonomic does that.

We use IBM Turbonomic in a hybrid cloud environment. Although it supports multi-cloud capabilities, we currently operate in a single-cloud setting.

Turbonomic offers visibility into our environment's performance, spanning across applications, underlying infrastructure, and protection resources. 

The visibility and analytics help to bridge the data gap between disparate IT teams such as applications and infrastructure. This is important for awareness collaboration, cost saving, and helping to design and improve our application.

Enhanced visibility and data analytics have contributed to a significant reduction in our mean time to resolve. Tools like Turbonomic provide crucial visualization and insights, empowering us to make data-driven decisions instead of relying on assumptions as we did before. This newfound transparency translates to a massive improvement, going from complete darkness to having a clear 100 percent view of the situation.

Although our applications are not optimized for the cloud we have seen some improvement in response time.

IBM Turbonomic empowers us to achieve more with fewer people thanks to automation. Previously, customers frequently contacted us requesting resource increases to resolve issues. Now, we have a tool that allows us to objectively assess their needs, leading to a deeper understanding of our applications. This solution also generates significant cost savings in the cloud and optimizes hardware utilization within our data centers. Its AI algorithm intelligently allocates servers on hosts, maximizing efficiency without compromising performance. By fine-tuning resource allocation without causing performance bottlenecks, Turbonomic extends the lifespan of existing hardware, postponing the need for new purchases. This effectively stretches our capital expenditure budget. We started to see the benefits of IBM Turbonomic within the first 60 days.

IBM is a fantastic partner. Their tech support has been outstanding, and the product itself is excellent - a very solid offering.

By automating resource management with Turbonomic, our engineers are freed up to focus on more strategic initiatives like innovation and ongoing organizational projects. Previously, manually adding resources was a time-consuming process that interrupted workflows. Now, automation handles scaling efficiently, saving us thousands of man-hours and significant costs.

It has illuminated the need for SetOps. It has highlighted areas of overspending, and the actions we've taken have demonstrated significant cost savings.

IBM Turbonomic has positively impacted our overall application performance.

IBM Turbonomic has helped reduce both CAPEX and OPEX. It has also significantly reduced cloud build times.

We use it to optimize costs and resource efficiency across our environments and  present infrastructure change requests to the business.

We had a lack of knowledge about things at our product level, and Turbonomic helped us resolve that. From an integration perspective, it also helped us find connectivity between our gateway tool products.

When it comes to optimizing costs and resource efficiency, before Turbonomic, we would add big, expensive storage and scale up across the tenant. Now, we are able to allocate the resources we need. We can also justify to the business, based on usage, why we are going with those resources. We have all kinds of proof to explain to the business how we are scaling down.

The visibility and analytics into our environment’s performance, from the APM down to the infrastructure, help us illustrate and clarify for the business the types of infrastructure changes we are suggesting. And they give us approval. Although we can collect the same information from Azure, Turbonomic is very user-friendly, and we can also automate notifications.

Regarding policy implementation, that can be implemented by a skilled engineer in five to 10 minutes. But if that same task is assigned to a new engineer who is not familiar with Turbonic, he would have to reference the previous document and the previous policy. Implementing the same thing would take that new engineer 15 to 20 minutes.

We primarily used it for the rightsizing of VMs. I have used it in both Azure and AWS. We also used it for the right categorization of disks.

We also used it quite a bit for comparison. We wanted to see if we migrate from on-prem to the cloud, what the cost would look like. We used it for what-if analysis.

Rightsizing and right categorization are part of optimization exercises. Turbonomic provides a single platform to help optimize costs and resource efficiency.

It provides good visibility of performance at the resource level. This visibility and analytics have helped bridge the data gap between disparate IT teams such as Applications and Infrastructure.

The visibility and analytics from Turbonomic have not helped reduce our mean time to resolution. We only used it for cost savings and not optimization.

Turbonomic has not impacted our application performance. You can do it if you integrate it with a tool like Dynatrace but not in itself.

Turbonomic can optimize the monitoring of public cloud, private cloud, hybrid cloud, and/or Kubernetes. That is where it specializes. With respect to the cloud, their algorithm is pretty good, and their recommendations are relatively trustworthy as compared to other tools. For cloud optimization, it is pretty good. It is also pretty good for balancing on-prem resources.

On the on-prem side, we had some automation or scheduling in place. On the cloud side, we did not do any scheduling. On the on-prem side, it would automatically go and make the changes needed, but on the cloud side, we took the recommendations, and we made the changes ourselves. We did not schedule them in the cloud. It is hard to quantify the time saved, but the analysis part is pretty good. We must have saved time and money.

Turbonomic helped to optimize costs and automate the changes on-prem. There were savings, but I do not have an exact number because we did it in phases. The first time, there would be more savings, and from the second round, they would slow down because you already reaped the benefit from the first-time recommendations. We did not do all the changes at once, so I do not have the numbers, but typically, any organization would have 20% savings in VMs and disks. Turbonomic does a good job. It depends on how big an organization is, but on average, the tool can cut down the VM cost by 20%.

Initially, our use case was to reduce cloud spend. But Turbonomic is much more than just a reduction-in-cloud-spend tool. As we went on, it became more about optimizing applications and making sure that they function as expected, while reducing the cost of cloud resources. It became a question of how we make applications function properly, at speed, with the best cost possible, and without creating any risk for the application itself.

Turbonomic has shed light on processes, on how applications actually function for people. The folks in the IT organization still tend to build large, to oversize things, to make sure that their applications perform properly. Turbonomic sheds light on what could be a more efficient application and deployment.

We use it in a multi-cloud environment.

We don't use the full functionality of Turbonomic because our company is subject to regulations around making those changes. Some of that functionality would require going through a change process. We've been using it more for heuristics and analysis on the right-sizing of our VMs and VMware.

At the resource level, Turbonomic has enabled us to right-size our converged infrastructure to a more appropriate level. Instead of using 12, we can use 10. It has been really good in helping us size the environment for our compute.

Another benefit is that it has helped reduce performance degradation. That happens at the application layer sometimes, and then a reset happens and everything is fine again. I would estimate it reduces performance degradation by 10 percent.

It has helped us streamline a lot of those applications. We're leveraging faster configurations on our VMs. Those systems that are being virtualized are operating with better peak performance whenever it's required, and that's what Turbonomic really does. It gives us insight into those peaks and valleys that we tend to go through.

The solution has also reduced resource congestion and starvation. For us, it's always a matter of refreshes. I like the forecasting tool that Turbonomic has where you punch in what you have today and it assesses the history of that setup. Then you can say, "I want to replace it with a snazzy, new compute/storage component," and it will provide a recommendation. That is a very good forecasting tool.

I belong to the on-premises team. We're a telecom company with a private and public cloud, but we don't use Turbonomic for the cloud infrastructure. We use Turbonomic for capacity forecasting and analysis. We do not use the solution as much on the application layer. We scan only the infrastructure. Turbonomic isn't providing any useful reports on the application layer. We did some application groupings, but it didn't help us because we didn't receive any application information. 

There are more than 50 Turbonomic users at the company, including admins and developers. There is a 10-person admin team, and the rest are end-users with limited access to see the reports on their machines.

Turbonomic provides recommendations about ideal resource levels. It helps us identify where we lack capacity and require more resources. These forecasts save time because we can avoid a capacity crisis. It tells us where to place the machines, so resources are automatically balanced. Those recommendations are there from the tool. That has helped.

It is a standard tool that helps to analyze capacity metrics. Without Turbonomic, we would struggle to manage capacity planning. It is essential to have a tool like Turbonomic because we rely on it for VMware capacity planning.

Turbonomic helped to reduce performance degradation by forecasting utilization and notifying us when we need to increase hardware resources before it reaches a critical threshold. Our SLAs require us to maintain 24/7 availability.