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.

Our use case: Planning for sizing servers as we move them to the cloud. We use it as a substitute for VMware DRS. It does a much better job of leveling compute workload across an ESX cluster. We have a lot fewer issues with ready queue, etc. It is just a more sophisticated modeling tool for leveling VMs across an ESX infrastructure.

It is hosted on-prem, but we're looking at their SaaS offering for reporting. We do some reporting with Power BI on-premise, and it's deployed to servers that we have in Azure and on-prem.

The proactive monitoring of all our open enrollment applications has improved our organization. We have used it to size applications that we are moving to the cloud. Therefore, when we move them out there, we have them appropriately sized. We use it for reporting to current application owners, showing them where they are wasting money. There are easy things to find for an application, e.g., they decommissioned the server, but they never took care of the storage. Without a tool like this, that storage would just sit there forever, with us getting billed for it.

The solution handles applications, virtualization, cloud, on-prem compute, storage, and network in our environment, everything except containers because they are in an initial experimentation phase for us. The only production apps we have which use containers are a couple of vendor apps. Nothing we have developed, that's in use, is containerized yet. We are headed in that direction. We are just a little behind the curve.

Turbonomic understands the resource relationships at each of these layers (applications, virtualization, cloud, on-prem compute, storage, and network in our environment) and the risks to performance for each. It gives you a picture across the board of how those resources interact with each other and which ones are important. It's not looking at one aspect of performance, instead it is looking at 20 to 30 different things to give recommendations.

It provides a proactive approach to avoiding performance degradation. It's looking at the trends and when is the server going to run out of capacity. Our monitoring tools tell us when CPU or memory has been at 90 percent for 10 minutes. However, at that point, depending on the situation, we may be out of time. This points out, "Hey, in three weeks, you're not going to be looking good here. You need to add this stuff in advance."

We are notifying people in advance that they will have a problem as opposed to them opening tickets for a problem.

We have response-time SLAs for our applications. They are all different. It just depends on the application. Turbonomic has affected our ability to meet those SLAs in the ability to catch any performance problems before they start to occur. We are getting proactive notifications. If we have a sizing problem and there's growth happening over a trended period of time that shows that we're going to run out of capacity, rather than let the application team open a ticket, we're saying, "Hey, we're seeing latency in the application. Let's get 30 people on a bridge to research the latency." Well, the bridge never happens and the 30 people never get on it, this is because we proactively added capacity before it ever got to that point.

Turbonomic has saved human resource time and cost involved in monitoring and optimizing our estate. For our bridges, when we have a problem, we are willing to pay a little bit extra for infrastructure. We're willing to pull a lot more people than we're probably going to need onto our bridge to research the problem, rather than maybe getting the obvious team on, then having them call two more, and then the problem gets stretched out. We tend to ring the dinner bell and everybody comes running, then people go away as they prove that it's not their issue. So, you could easily end up with 30 to 40 people on every bridge for a brief period of time. Those man-hours rack up fast. Anything we can do to avoid that type of troubleshooting saves us a lot of money. Even more importantly, it keeps us productive on other projects we're working on, rather than at the end of the month going, "We're behind on these three projects. How could that have happened?" Well, "Remember there was that major problem with application ABC, and 50 people sat on a bridge for three days for 20 hours a day trying to resolve it."

In some cases you completely avoid the situation. A lot of our apps are really complex. A simple resource add in advance to a server might save us from having a ripple effect later. If we have a major application, as an example, and to get data for that application, it calls an API in another application, then pulls data from it. Well, the data it asks for: 80 percent of it's in that app, but 20 percent of it's in the next app. There is another API from that call to get that data to add it to the data from application B to send it back to application A. If you have sometimes a minor performance problem in application C that causes an outage in application A, which can be a nightmare to try and diagnose those types of problems, especially if those relationships aren't documented well. It is very difficult to quantify the savings, but If we can avoid problems like that, then the savings are big.

We are using monitoring and thresholds to assure application performance. It is great, but at the point where our monitoring tools are alerting, then we already have a problem in a lot of cases, though not always. The way we have things set up, we get warnings when resource utilization reaches 80 percent, because we try to keep it at 70 percent. We get alerts, which is kind of like, "Oh no," but we can do something about it when the applications are at 90 percent. The problem is there are so many alerts and it's such a huge environment. Because there is too much work going on, they get ignored. So, they can work into the 90s, and you end up a lot more often in a critical state. That's why the proactive monitoring of all our open enrollment stuff is really beneficial to us.

There have been quite a few use cases, even some that were probably unintended. 

1. Reduce our footprint and cost. It handled that perfectly. 
2. Handle our RI purchasing, which is what we are in the process of doing now. 
3. Automating shutdowns and startups so we can turn machines off when they are not being used. We have several machines in this category. We are going to continue to add more to it, once we go through some finalization. We are using it to delete unattached volumes to manage databases.

The unintended use case was that we started looking at what else could we save. We realized that we had a ton of data in Blob Storage for backups. Turbonomic can't see that, but it brought it to light because we wanted to find a way to look at our overall spending. So, we have saved a bunch of money by reducing that footprint. 

It's on-prem, but we are in the process of moving into the cloud.

It helped reshape the organization. We used to have around 46 admins who could create virtual machines in Azure, and it was very difficult to manage them. It became exceedingly expensive, getting to a point where it received attention from the president of the company. Installing Turbonomic gave us sort of a governor over our cloud environment, where people started to really understand that everything you do has a cost. Now, we have like four or five admins. If someone wants a VM, we run a plan based off: available RIs, cheapest costs, and can it go into shut down mode at night? This has helped us create a standard of implementation going forward to prevent us from spending the money in the first place. While I'm sure Turbonomic would say that's their overall goal, we just never really saw it that way. 

I didn't know it was going to change how we submit tickets and work orders for adding service to the environment. That has all changed for the better.

I know when we are moving workloads from on-prem to the cloud, we have not been utilizing the planning in Turbonomic as much as we should. I think that is changing. People who have access to Turbonomic are now realizing, "Here are the specs of my machine. Turbonomic will put it in the cheapest compute resource template that it can find." 

This is starting to change how we even consider building something. We don't ask the end user, "What do you need?" Instead, we say, "What do you want to host?" Then, we look at other virtual machines out there. If we want to host a website, then it will be X number of users per day, month, or year. We look at some of our other marketplaces, then we make a plan to see what Turbonomic recommends, as it takes into account the disk, IOPS, RI, CPU, RAM, etc. This helps us prevent the spend in the first place. The idea where we build then save on the backend, that is what's changing. We are no longer just building blindly, then going to Turbonomic, and saying, "Okay, fix what we built." We are saying, "Turbonomic, tell us what we should build." I would rather do it when we build a machine than have to take a website down and schedule maintenance at two in the morning on Saturday when I am at the bar.

It gives people like me, who are engineers, the ability to launch environments without having to create a bunch of changes down the road. I think that's a wake-up call for a lot of the people at the company. Because we would just build our build, where we were spending $5,000 more than we should, so then we have to put in change tickets and scale all that back. I don't have to do that anymore.

If we see a system or service increasing usage, we can then anticipate building it out bigger to manage traffic and workflow. With version 8, it will almost be like having a tool to manage our machines actively versus just actions.

We wanted the performance assurance because we have seasonal spikes in our volume. One of the use cases was making sure that we could adjust for seasonal spikes in volume. 

Another use case was taking a look at how we increase our density and make a more effective utilization of the assets that we have on the floor. 

The third use case was the planning, being able to adjust for mergers, acquisitions, divestitures, and quickly being able to separate out the infrastructure required to support that workload.

We just upgraded and are using the latest on-prem version. 

We use Turbonomic for our on-prem hosting: servers, storage, and containers. We also use it in Azure. We are trying to use it across multiple hosting environments. The networking team is not really using it. Instead, I am there from a hosting standpoint, where the main focus is on servers and storage, then the linkage to applications with the resources that they are using.

It integrates into our other tools that we have been able to stitch together. When I take a look at an infrastructure cluster, I can see what applications are running on it. I can see down to the transaction level who is actually causing a performance constraint. We can then go back to our application teams to get that issue resolved.

When I start to take a look at a cluster level, I can look to see which application is running in that cluster. Then, we can get down into specific transactions. We can then watch to see how workload is trending and identify where we may need to add more hosts into the environment. With our transactions, we use Turbonomic linked into AppDynamics. When it links in and pulls the application data, it also helps us dig down. So, if I see my utilization trending up, then is it something on the infrastructure side or the application side? Is it something the application team needs to address? Or, is it something my infrastructure team can address? This allows us to make fact-based decisions.

In our organization, optimizing application performance is a continuous process that is beyond human scale. We would not be able to do the number of actions that Turbonomic takes on a daily, weekly, and monthly basis. It is humanly impossible with the little micro adjustments that it can make. That is a huge differentiator. If you just figure each action could take anywhere very conservatively from five to 10 minutes to act upon, then you multiply that out by thousands of actions every month, it is easily something where you could say, "I am saving a couple of FTEs."

On Windows 2008, whenever we did a large scale OS upgrade, it was kind of taking a look at what resources were allocated to each of the applications and server instances. Then, you basically would replicate that. Being able to use Turbonomic, we have been quickly able to go through and take a look, and say, "Okay, wow. This may have been what was previously allocated to you. We now realize that your utilization doesn't require that level." We are able to actually downsize as we go through and rebuild. This part, the planning aspect, is really good.

One of the things that we completed this year was starting to tag applications so we can pull up more critical applications and take a look at their resources needs. We can have a specific dashboard per critical application.

We primarily use it as a cost reduction tool regarding our cloud spending in Azure, as far as performance optimization or awareness. We use Turbonomic to identify opportunities where we can optimize our environments from a cost perspective, leveraging the utilization metrics to validate resources are right-sized correctly to avoid overprovisioning of public cloud workloads. We also use Turbonomic to identify workloads that require additional resources to avoid performance constraints. 

We use the tools to assist in the orchestration of Turbonomic generated decisions so we can incorporate those decisions through automation policies, which allow us to alleviate long man-hours of having someone be available after hours or on a weekend to actually perform an action. The decisions from those actions are scheduled in the majority of cases at a specific date and time. They are executed without having anyone standing by to click a button. Some of those automated orchestrations are performed automatically without us having to even review the decision, based on some constraints that we have configured. So, the tool identifies the resource that has a decision identified to either address a performance issue or takes a cost-saving optimization, then it will automatically implement that decision at the specific times that we may have defined within the business to minimize the impact as much as possible.

There are some cases where we might have to take a quick look at them manually and see if it makes sense to implement that action at a specific date and time. We then place the recommendation into a schedule that orchestrates the automation so we are not tying up essential IT people to take those actions. We take these actions for our public cloud offering within Azure. We don't use it so much for on-prem workloads. We don't have any other public cloud offerings, like AWS or GCP. 

We do have it monitor our on-prem workloads, but we do not really have much of an interest in the on-prem because we're in the process of a lift and shift migration for removing all workloads in the cloud. So, we are not really doing too much on-prem. We do use it for some migration planning and cost optimization to see what the workload would look like once we migrated into the cloud. 

From our on-prem perspective, we use it for some of the migration planning and cost planning. However, most of our implementations with this are for optimization and performance in the public cloud.

It provides application metrics and estimates the impact of taking a suggested action from two aspects: 

1. It shows you what that impact is from the financial aspect of a public cloud offering. So, it will show you if that action will end up costing you more money or saving you money. Then, it also will show you what that action will be like from a performance and resource utilization perspective. It will tell you when you make the change, what that resource utilization consumption will look like from a percentage perspective, if you will be consuming more or fewer resources, and if you're going to have enough resource overhead for performance spikes.
2. It will give you the ability to forecast, but the utilization consumption's going to be in the future term. So, you can kind of gauge whether the action that you're taking now is going to look and work for you in the long term.

In our organization, optimizing application performance is a continuous process that is beyond human scale. We see tremendous value in Turbonomic to help us close that gap as much as possible within our organization. Essentially, Turbonomic will provide us with a recommendation on how to address a workload in real time based on its actual utilization. Then, we have pre-defined time slots where those actions can be implemented with minimal impact on the business because some of the changes may require the rebooting of the server. So, we don't want to reboot the server at 2:00 in the afternoon when everyone is using it, but we might have a dedicated time slot that says, "After 5:00 today or 2:00 in the morning when no one is using it, this server can be rebooted."

We have leveraged Turbonomic to not only ingest the data from the utilization of workloads to come up with performance-based driven decisions. We also have used Turbonomic to help orchestrate and initiate those actions automatically for a very large portion of our organization without us having to even be involved at all. For some more sensitive workloads, we look at them and coordinate with the business whether we will take action at another date and time.

We primarily use it in the public cloud for servers. We also monitor storage and databases within Azure. This is another added benefit that we like about Turbonomic. When we look at a decision, we are looking at how that decision is being driven based from a storage perspective, the IOPS being driven to a specific storage solution within our public cloud offering, its decisions based on specific DTU utilization from a database perspective, or if it is even a percentage of memory or CPU consumption. It takes into account all those various aspects and never puts us in a position where we take a decision or action without accommodating these other pieces and having them negatively impact us.

That level of monitoring is what has given us the confidence to allow Turbonomic to implement actions automatically without having IT oversight micromanage decisions, because it provides that holistic view, takes into account all those aspects, and ensures that a decision that is implemented never puts you into a point of contention or concern. We have the confidence to allow the appliance of the software solution to take actions without little to no IT oversight.

Turbonomic has identified areas within our public cloud where we had storage that was not being used at all. So, it provided us with insight into what that unused storage was so we could delete the unused storage and save on the recurring consumption cost. That was very helpful.

We have identified numerous workloads which have been overprovisioned by an administrator. We were able to essentially right-size workloads to use less resources, which cost us less money in our public cloud offering, e.g., a configuration with less memory or less CPU than what it was originally configured for. That helps us reduce our cloud consumption significantly.

In addition to ensuring that workloads are right-sized correctly, we have been able to save even more with our public cloud consumption by identifying workloads where we could purchase reserved instances, essentially long-term contracts for specific workload sizes. This allows us, on average, to save an additional 33% or more on our server run rates.

Turbonomic provides a proactive approach to avoiding performance degradation. It has allowed us to detect issues before they have actually become issues. Traditionally, in IT, we would not be aware of an issue until someone from the business came to us with an issue, then we would investigate the issue. In some cases, we would spend a couple hours trying to figure out what the issue was, then determine if something needed more resources, like more memory. Since Turbonomic, we have been able to almost immediately identify that our system needs more resources and take the action right then and there. Or, Turbonomic has identified there is an issue and we take an action, then notify the business that an action was taken in order to preemptively avoid a business impact.

Previously, a business impact use case would potentially take us hours. With Turbonomic, whenever we run into a business impact use case now, before we even log into a system to initially troubleshoot it, the first thing we do is go to Turbonomic and see, "What is Turbonomic telling us? What is the workload like now? What has it looked like in the last 24 hours or week? Do we see any trends to help guide us towards identifying where we should go from a troubleshooting perspective?" From that aspect, Turbonomic has definitely helped guide our path to resolution.

We have a hybrid cloud setup that includes some on-prem resources, and then we have AWS as our primary cloud provider. We have one or two resources on the Google Cloud Platform, but we don't target those with Turbonomic. Our company has a couple of different teams using Turbonomic. Our on-premise VMware virtualization and Windows group use Turbonomic to manage our on-prem resources. They use it to make sure that they're the correct size. 

I'm on the cloud engineering team, and I use it in a unique way. We use it for right-sizing VMs in AWS. We're using it to improve performance efficiency in our Kubernetes containers and make sure the requests are in line with what they should be. If an application has way more memory allocated than it needs, Turbonomic helps us decide to scale that back.

We have a platform that we use for our internal deployments. I use our API to get data and transform it for use in our platform. I've developed APIs that go in between our internal platform and Turbonomic. When our developers create and release code, these APIs allow them to take advantage of Turbonomic without using it directly. It's built into our platform so they can benefit from the performance improvements Turbonomic can recommend, but they don't need access to Turbonomic.

Cost savings is a significant benefit, especially with our AWS computing. It cuts down on human error. For example, sometimes someone will spin up some resources in AWS and then forget about it. We can go into Turbonomic's reporting and see that a virtual machine is idle, so you might want to scale it down. If it's not being used, you should delete it. Then you can save X amount of money. Turbonomic will automatically apply those things for you and tell you how much you're going to save. It's integrated with your AWS billing report and everything; it can give you real data. You click a button, and it'll apply all the functions for you, so you save a bunch of money. I would say that that's a huge part of it for us.

We have a couple of different use cases, and it was essential for us to meet all of them without the need to go to several different vendors. Turbonomic can manage on-premises and cloud-native resources all in the same place, providing direct cost benefits through our cloud providers and our on-prem hardware storage.

Turbonomic has also helped us improve our efficiency as an organization. We can better understand the actual cost of our applications and how to optimize, so we've become more efficient and cut down some of the extra expenses. It's also useful for capacity planning. We can understand how much resources we're using right now and how much we'll need when we bring on new clients for our software solution.

Turbonomic has helped us manage multiple facets of our business-critical functions. Our company provides a software platform for our clients. They log into a portal that's hosted either in the cloud or on-premises. Turbonomic can monitor those applications as well as the underlying storage and computing resources. It's monitoring the applications themselves, the production environments, development, and QA for future changes. We can understand how changes are going to impact our production.

It depends on the system that we're looking at. We have a change-management process for our business-critical things and our production resources. With that, we either schedule a change or manually execute the change during a planned maintenance window. Our change management board approved other functions, like development and QA-type resources that aren't in production that we're developing. We can automate those kinds of things all the time. I know that our storage team automates a ton of tasks, but I'm not exactly sure. I assume they wouldn't be automating production resources either.

We follow some pretty strict change management policies. Applying some of these resources will require restarting your process. We would do it either in a change management window that we schedule through Turbonomic or manually apply it. 

Turbonomic's application-driven prioritization helps us identify where risks are coming from while proactively preventing performance degradation. It's nice to be able to avoid problems before they happen. I don't have to wake up in the middle of the night and respond to some alert because one of our applications ran out of memory, and people couldn't use our product. It's helped me get some sleep. Our storage teams are super stoked about that, too, because they had all sorts of alarms going off all the time, and they set up a ton of automation with Turbonomic to handle that all for them. We've seen a significant reduction in open tickets for application issues.

We pretty much use it only for load balancing between hosts.

We're a payroll company and Turbonomic is really important for us from about November until March, each year, because our end-of-year processing increases our load by six to seven times. That's especially true in November and December when companies are running their last payrolls. If we're going to be losing any customers, they definitely have to finalize everything all at one shot. In addition, companies that pay out bonuses at the end of the year also have to be running all these extra payrolls. There are a slew of reasons for extra payrolls at that time of year. They may need to do some cleanup if they messed up something and didn't do so all year long. At that point, they have to do it before December 31st. And after December 31st is the beginning of tax preparation, so our systems are very heavily utilized.

It does a great job year-round, but we're in a situation where we have plenty of resources during most of the year, but at year-end, depending on how busy it gets, it can overwhelm the systems if you're not careful, depending on where a VM sits, on which host.

We have parts of our payroll engine that run pretty hot, depending on what's happening. Those pieces of the payroll engine and the SQL servers tend to overrun a server pretty quickly, if you're not careful. But Turbonomic always does a great job of making sure that that is not happening.

Our company is growing like a monster. Even during COVID we've been growing, where a lot of companies, unfortunately, are not. Because we've gained a lot of new customers during COVID, because other payroll companies have gone under or because our pricing model is good, the result has been a lot more load on our systems. And even though we've had much more load, Turbonomic has done a great job of keeping everything balanced. We're able to completely utilize our systems before having to introduce more hardware.

Its whole job is to manage our business-critical applications by understanding the underlying supply chain of resources. In my opinion, applications are applications. When your application is running, it's reserving a piece of memory, it's utilizing the CPU, and it's utilizing the network interface. There is a certain limit to the hardware that's available to it. Applications need watching. If we were to use the pieces where it digs down inside the application, it would probably even do better, but we're not using those pieces yet. In the end, no matter what the application is doing, it's using resources, either on a regular basis or on a random basis, and Turbonomic looks for patterns and spikes and how long these patterns last. It looks at the risk of a spike only happening for 30 seconds versus 30 minutes. It makes decisions to move other things off or move off the application in question. It does a great job keeping that balance.

We hardly do any manual execution. Maintenance windows are when it's probably most important to us. For example, when we're upgrading or doing a repair on an ESX host, we're really taxing the other servers because those CPUs and all the I/O capability on that host are lost. That's when it's even more important for Turbononic to keep the rest of the system healthy enough and to keep the system going without showing any degradation. It helps us while we're doing maintenance.

It provides a proactive approach to avoiding performance degradation, absolutely. It always looks at history and looks for patterns. In a lot of cases, it knows that there's a scheduled task or that, on average, a customer is always like hopping on around three o'clock. After a week or two of seeing the exact same things happening—although that timeframe really depends on how big your organization is, the bigger your organization, the longer it takes to understand the full dynamic—it really understood all the patterns and all the schedules. In the past, we'd see systems starting to get a little hotter and then a little hotter still. With Turbonomic, that issue would just go away by shuffling stuff around. After a week or two we wouldn't see that thing getting warm anymore. When that application or server got hot, it never put that host into any kind of jeopardy anymore, not even a little bit where we thought, "Hey, we should look at that."

We have definitely seen a reduction in tickets open for application performance issues. In some cases we get tickets for performance problems because the developer wrote crappy code and wants to blame us in infrastructure. But I can show them, "Look, we're not overloaded." They look at some of the logs we provide them that show that the servers are operating at full capacity and usually they find that there's some kind of weird issue with a database query that they wrote. The solution has definitely reduced tickets for application performance problems a little bit, but it has mostly decreased troubleshooting time.

It's helped us to always meet our SLAs. In previous companies I've worked at, the company was either so cheap, or the boss didn't understand how the infrastructure works, that we got to the point where we were over utilizing our hardware. That's where Turbonomic really makes a difference because without it you can be way over-committing what you have versus what you need. You always have servers that are sitting there idle, so it's best if you can balance the ones that run hot with the ones that run idle and shuffle around use of them based on what you're utilizing. If you don't have Turbonomic, then whatever boxes happen to be on there could be in overdrive to the point where you start ballooning and paging. That can cause a denial of service because you don't have enough resources to handle the workload. It's really great to try to maximize every inch of your infrastructure, to make sure you're utilizing everything to its fullest capacity.

Whoever is on call loves Turbonomic because if we didn't have it we'd be getting alerted much more often. That's especially true at three o'clock in the morning when some backup or something that's not even important is running and the pager will go off because it hits some threshold for too long. Turbonomic sees, "Okay, this server does this at this time and it's going to use a lot more resources than anybody else," and it just shuffles everything away.

We are in the healthcare industry and we use this solution for ensuring proper resource allocation for our virtual servers and our virtual desktops.

We use Turbonomic as a single platform to manage our full application stack, and having a single source of truth for application performance management is very important to us. The fewer places you have to go to make changes, the better. Having that available in a single pane of glass to make those changes makes it easier on our admins. Rather than having to go into multiple solutions to make changes, they do it all right there.

Turbonomic provides visibility and analytics in our environment from the application layer, all the way down the stack to underlying infrastructure resources. We don't use it as much on the application level as we do on the hardware and resources level.

This is a feature that's becoming more important to us. We're really starting to look at the analytics more nowadays and will in the future. It was not as important before but has become more so in the past year.

This visibility has definitely helped reduce our time to resolution. We have not quantified how much but it is due to having the visibility and the ability to monitor what's going on and make those changes in real-time. We just didn't have a baseline to compare and see how much it's improved.

With respect to any alerts that come up, it helps us to interpret those faster. This is something that is very important because it triages a lot of stuff that we don't have to then spend extra time doing, especially being a small team. It saves us at least a couple of hours per week.

Using this product helps us to reduce performance risk because it shows us where resources are needed but not yet allocated. Based on that, we're able to see whether those resources needed to be updated. In turn, that helps to limit application downtime or employees waiting for their jobs to get done.

It has definitely helped to reduce performance degradation. It helps us keep up with the changing environment and workloads that change over the course of days or weeks. Prior to this, it was all manual for us and we'd have to react. Now, we're able to be proactive.

This has also had a positive impactive on our applications' response time to SLAs. We're able to keep up and be proactive by fixing issues before the user even notices that there is a problem. This is important because it's great from a customer experience standpoint.

They never experience the problems they had in the past, where they would have to call us to say that their machine was running slowly, and then we'd have to figure out what was going on. Now, we know beforehand that they need additional resources, and many times, we're able to address that before they even realize it.

Generally speaking, using this solution has helped to eliminate resource constraints and it's helped us to understand what resources we need. In terms of that, we are able to modify our plans for the future concerning the acquisition of new hardware because we're able to satisfy the need with what we already have, rather than thinking we need to buy more. Turbonomic helps us to balance that out better, ensuring that we're not over-resourced in terms of hardware, and having resources sitting idle, which is very costly.

Turbonomic has helped our engineers focus on innovation because it has freed their time quite a bit. In the past, we had one person that would spend a lot of time trying to find where things were going wrong. This was precipitated by users saying that their machines were slow or not performing very well. Our staff would have to go in and figure out what was going on, then make the appropriate changes. Now, Turbonomic does that and our staff can focus on other tasks that need to be done.