Understanding Instance Recommendations and Outliers

FAQ: Why has x instance been recommended? Wouldn’t y instance be better suited? Often, a cloud instance can appear to be over-provisioned at first glance, and the reports may not explain this in an obvious way without added context or a practiced eye. Understanding how and why Cloudamize makes its recommendations is valuable when reading results.

Note that the below applies largely to recommendations for usage-based designs. Hardware-based designs follow a different, simpler logic that is more geared towards a lift-and-shift approach.

Key Concepts

At its core, Cloudamize will follow a simple flow of logic to produce a cloud instance recommendation.

First and foremost, a recommendation must be fit for purpose:

• All performance requirements must be met (CPU performance, memory utilization, storage size/throughput/IOPS)

• Any special requirements must be met based on Designer parameters (e.g., special licensing, tenancy, price plan, region, etc.)

If our database of the IaaS providers' offerings cannot produce a recommendation that is fit for purpose, the recommendation will default back to a hardware-based recommendation. This will show in the results as the entry in the Constraints column having the appendage “(Hardware Mapping)”, e.g. “CPU (Hardware Mapping)” or “Memory (Hardware Mapping)”.

The recommendations engine will consider all instance/storage/licensing combinations that are fit for purpose and will then make a simple secondary choice: Which fit-for-purpose combination is cheapest overall?

When calculating the cost of an instance, all relevant costs are combined; compute cost (base instance multiplied by on-time percentage), storage cost, network costs, license cost, etc.). This total cost is used when comparing instance costs, so occasionally, a more expensive base instance might be cheaper overall due to, for example, a favorable storage configuration. The lowest total cost of all fit-for-purpose instance/storage/licensing combinations is considered the cheapest overall.

Fit for purpose and cheapest overall combine to produce the key logic behind a Cloudamize recommendation:

Outliers: How to Find the Relevant Current Performance Indicator

You may query a particular recommendation when its obvious markers appear to be over-provisioned to some degree (e.g. an instance with a far more powerful CPU than is necessary may be recommended, to the degree that its predicted CPU usage may be as low as 3% against a Service Level Target of 80% in some cases).

Understanding the reason such an outlier instance has been chosen involves looking at some key data points, beginning with the Constraints column in the recommendation output.

The Constraints column lists the key performance requirement that carries the largest weight in deciding whether the instance recommendation is fit for purpose (e.g. it may list “CPU”, in which case the CPU performance is the primary deciding factor in deciding that this instance meets the design Service Level Targets and usage requirements).

Usually, when an instance appears over-provisioned, the value in the Constraints column is a key indicator of why the over-provisioning has occurred. Crucially, the instance will usually be over-provisioned in a manner not related to the Constraints column entry (e.g. an instance recommendation where the CPU is over-provisioned will not list “CPU” in the Constraints column, as another factor has decided that over-provisioning had to take place to meet fit for purpose requirements).

Reading the constraints column entry is the starting point of figuring out an outlier recommendation.

Once you know the constraint on the recommendation, you can examine the relevant tab and data points. What you are looking for is a current performance indicator that is large and/or very close to the predicted performance indicator (e.g. a particular disk has a very high throughput value in its current performance, which is common on highly active databases).

When you have identified the current performance indicator that is driving the recommendation, you can then begin some research with the relevant IaaS provider.

IaaS Provider Calculators: Pricing and Availability Considerations

Once we know the relevant current performance indicator, we need to consult both Cloudamize Designer and IaaS Provider information to find the reasoning behind the outlier recommendation.

It is important to understand Designer restrictions: we must check which design contains the outliers (or is being queried) to check any applicable Designer values that affect this particular performance indicator (e.g. if we have identified Network Throughput as the relevant data point, we can check any Design parameters that scale the SLT for this particular value in the design, such as the Network Scaling slider).

The design may also restrict instance types directly, set a particular SLT for CPU, have a different method of hosting (shared tenancy, etc.) or have licensing requirements (such as BYOL), or be tied to a particular Pricing Plan. All these factors can affect availability depending on the IaaS provider’s offerings.

To see what effect the relevant current performance indicator has, we then consult with the providers themselves, using their offerings calculators:

Amazon Web Services: AWS Pricing Calculator

Microsoft Azure: Pricing Calculator | Microsoft Azure

Google Cloud: Google Cloud Pricing Calculator

Additionally, the providers have documentation that can be consulted to see the capabilities of instance ranges, etc., which can also provide clues.

Within the calculators and/or documentation for the providers, we take the approach of “how do I fit any instance with this requirement (the relevant current performance indicator) onto the cloud platform?”

Further Information

If you are unsure at any point about a particular recommendation and have checked the above, reach out to your Technical Account Manager or the Cloudamize Technical Helpdesk team for advice and assistance.