Rightsizing is about finding the optimal cloud configuration options to ensure that you get the performance you need—within any given constraints you are operating under—at the lowest possible cost. This is a simple proposition, but deceptively so. For one thing, business requirements are constantly changing, meaning that your workloads must adapt to support them, which in turn changes their operating parameters. And even when business requirements remain constant, the workloads themselves can evolve or drift as usage changes. The other complicating factor is that cloud service providers offer more configuration options than you could possibly evaluate. We are talking hundreds of thousands. This is why many cloud cost optimization tools provide rightsizing recommendations.

The idea behind rightsizing recommendations is to help you reduce the number of choices down to a manageable list of the most relevant options that will help you save money while maintaining desired performance levels. This really is a must-have capability. Except…

There is a big problem that no one talks about

Just because something checks the box on a vendor’s feature list, it does not automatically mean it will deliver the implied benefits and value to you. Too often, the rightsizing recommendations provided by optimization tools cannot actually be implemented, rendering that feature unusable. Why? There are a few reasons, so let us break them down.

Recommendations do not take specific limitations into account

A company was running an application on a specific—older—version of an OS. It had not been updated in years because doing so would require a big lift. The application was running well, so there was simply no reason to fix what was not broken. For them, recommendations that require a newer version of the OS were useless. And without a way to tell their tool about their OS requirement for this particular instance, they will never get a recommendation that can be implemented. Another company had an application running a memory-heavy database, so recommendations that cut memory to any degree were non-starters. It is not that reducing memory was completely off the table, they just needed to prove that the action would not have an adverse effect. Which leads to the next issue.

You have to take the recommendations on faith

Many tools say, here are our recommendations—trust us! Some will provide very basic categorization (easy/medium/hard, or baseline/aggressive) but you cannot tweak the underlying data science, which is a black box. How comfortable are you with making mission-critical-workload-impacting changes on blind trust? Thought not.

Recommendations do not show the bigger picture

In many organizations, you have to deliver the recommendations to a different team to implement and it can sometimes be difficult to ensure adoption. The development team, for example, is not traditionally used to thinking about cost. If, focusing on functionality and security, they found through app testing that doubling the size of CPU worked, that is what they are going to go with. They are not likely to experiment with how to get to the lowest CPU threshold that still enables the application to operate as requires. They are also not likely to want to implement a change to save the company $100 per month. This is understandable—they do not want to risk breaking anything. But if you could prove that the application will not be negatively affected and that this is in the best interest of the business, then you are more likely to be able to profit from those recommendations.

One-size-fits-all recommendations do not fit everyone

Different teams have different goals. For production applications that are stable and running in volume, any opportunity to squeeze out inefficiencies can lead to significant savings. Development teams, on the other hand, benefit from experimentation. You do not want to stifle innovation for the sake of pinching pennies. Likewise, different workloads have different requirements. If you apply the same set of parameters to your customer-facing applications as you do you to backup workloads, you either risk limiting performance where you cannot afford to, or more likely, you overspend when you do not need to.

What to look for in a recommendation engine

If any these issues sounds familiar, it might be time to find a new source for rightsizing recommendations. Here are the key requirements to be on the lookout for:

  • Customization: You need to be able to set parameters based on your specific requirements. If you have particular CPU, network, memory, or disk needs, you need to be able to factor those in. If you have certain constraints, such as OS version, those need to be taken into account.
  • What-if analysis: Getting a recommendation to save money is one thing; understanding the impact beyond cost is something else altogether. You want to be able to see the effect the change will have and tune the recommendation accordingly.
  • Multi-policy support: You must be able to support the varying needs of all constituents across the organization. The only way you can do that is to apply different policies to different areas.
  • Integration with change management: For some organizations, having recommendations integrated with change management, such as ServiceNow or Jira, may be a nice-to-have rather than a requirement, but it makes the process of applying the recommendations much simpler. Embedding them into operational workflow can improve adoption of those changes. After all, the only way to get value from recommendations is to actually implement them.

Get rightsizing recommendations you can use with Virtana Optimize

Virtana Optimize radically simplifies management of your hybrid cloud IT infrastructure by optimizing cost, capacity, and performance in real time on an ongoing basis. Virtana Optimize provides better visibility into cloud spend with the ability to drill deep into specific services to understand what is driving increased costs, and it provides rightsizing recommendations to meet a wide range of needs across the organization. We offer a free version of Virtana Optimize that can get you started by identifying cost savings with recommendations you can act on now, and you can upgrade to a paid tier when you are ready for expanded recommendation capabilities, multi-policy support, and integration with ServiceNow and Jira.

Get started for free

David McNerney
David McNerney

Product Manager, Virtana

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