If you are tasked with finding the best tool for optimize your IT organization, you have your work cut out for you.
Anybody with a dartboard can claim ownership of a server capacity planning tool. Unfortunately, companies selling dartboards for capacity planning aren't likely to be very honest about the sophistication of their tools. Buyer beware.
If you have an unlimited IT budget, dartboards are fine capacity planning tools. But the rest of us need accurate predictions of future workload needs.
Accuracy in capacity planning helps balance IT risk and health. Too much risk means you could lose revenue and customers from downtime. But you also don’t want to achieve IT health through overspending. A good capacity planning tool will provide the appropriate metrics for both these factors.
So how do you avoid buying a dartboard when what you really need are accurate predictions? What makes a good capacity planning tool? What should a buyer in your position be looking for?
First off, monitoring performance is not the same as capacity planning. Planning involves proactive provisioning for future demand.
Getting an alarm 15 minutes before users complain can only enable you to move reactively. While alarms are essential components for capacity management, they don’t do enough on their own.
Many capacity planners keep historical data and use it to plot trends, hoping those trends can accurately predict future performance.
While this strategy is better than nothing, you should beware of companies that tout trending as capacity planning: it’s not a great solution in most situations.
This strategy assumes that workloads increase at a steady rate—or at a rate based on some statistical formula that isn’t necessarily accurate. This means trending often collapses when it comes time to add a new workload or consolidate servers. It also falsely assumes that computer system performance is linear.
A capacity planning tool needs to assess more than just past system performance in order to make accurate predictions about the future.
Performance doesn’t scale linearly. Those who use trending as a capacity planning tool simply try to keep utilization rates below 100 percent. This kind of inexact estimation practically guarantees over-provisioning, and companies who do this are often oblivious to the fact that they’re wasting money and hamstringing their IT department.
Be wary of tools that do "capacity planning" for server consolidation by adding together the resource utilization of each of the workloads being considered for consolidation.
After normalizing CPU utilization to account for differences in computing capability, the utilization for each workload is added together to determine how much of the target CPU will be utilized after consolidation. A similar calculation is performed for other resources such as memory, I/O, and the network.
This kind of simplistic procedure can be effective enough to find potential candidates for consolidation. But it leaves way too much out of the equation to be solely responsible for the final decision when consolidating important workloads.
The indicators of IT health and risk you would use with this method would be artificial and not based on actual behavior. This will inevitably lead to over-provisioning, outages, and other unpleasantries.
You need a tool that understands the details of your server architecture, your applications' use of that architecture, and how workloads will interact when consolidated.
It’s always been true that the best capacity planning tools use some sort of performance modeling. But due to the time-intensive nature of the traditional modeling, capacity planners only use it for highly critical applications.
Thankfully, automated predictive analytics have made it possible to do this kind of advanced capacity planning across your entire infrastructure, not just with the most important servers.
Sometimes when people talk about a "model," they mean a description or diagram. That's not the kind of model we are talking about in this case.
You do need a description of the systems involved. But that description is really just one step in a good capacity planning process. What you want is a tool that can look at that description along with information regarding the incoming workloads, and predict how the systems will perform.
There are at least two methods used by capacity planning tools that use modeling to predict performance: simulation modeling and analytic modeling.
A good simulation modeling tool will create a queuing network based on the system being modeled and pretend to run the incoming workloads on that network.
These simulations can be highly accurate, but a lot of work is needed to adequately describe the systems with enough detail to produce dependable results.
It makes plenty of sense to use flexibility simulation models to plan for those “what-if” scenarios. For example, you might use it to determine how long a proposed investment in CPU infrastructure will last so that you can construct a business case for management.
This is still the preferred method for networks, but it’s so resource-intensive that it’s practically impossible to use as a capacity planning tool for servers. For your most critical capacity planning needs, you’ll need something that utilizes queueing theory.
Simple Queuing Network
While analytic modeling also takes queuing into account, it doesn’t pretend to run the incoming workloads on the model.
In a good analytic modeling tool, formulas based on queuing theory are used to mathematically calculate processing times and delays. This type of modeling is much quicker—and not nearly as tedious to set up. The results can be just as accurate as simulation modeling results.
It’s important to pick the right data to model and ensure that it represents the appropriate situations.
When the process of selecting and contextualizing data isn’t automated, you must rely heavily on the skills of the analyst doing the work and run the risk of making mistakes. Without automation, it becomes extremely easy to miss important data and get inaccurate projections of future needs.
To get the most out of your capacity planning tool, you’ll want it working for you 24/7.
Automated predictive analytics are what capacity planners will find most useful in their day-to-day monitoring of applications and systems, keeping a constant eye on the predicted performance of large numbers of infrastructure elements serving applications and business services.
Want to cover your bases? Get a tool that can do both analytic and simulation modeling.
What makes a good capacity planning tool? Any advanced method of capacity should take a number of complex factors into account. Learn more about a few of the most important criteria in our Buyers Guide: Capacity Planning Tool Checklist.