Altair SAO: Software Usage Metrics – 2

In the previous SAO article, I talked about capacity utilization, a key metric, especially for shared licenses.  I will discuss additional metrics, how they can provide objective utilization measures, and what role they can play in decision making – which is always about maintaining an optimal software license inventory.

Altair SAO provides a year-to-date (YTD) Summary Usage Report.  It should be inspected every 6 months or longer.  This report provides a numerical summary of about 15 metrics for every software feature in the system that has some usage, and allows for inspecting outliers of every metric.  You may drill down to further inspect usage patterns in detail.  The following diagram shows a few important metrics that warrant close inspection:

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Key Metrics

1.       Licenses

This metric refers to the total available licenses in the system for a software feature, and the associate peak license usage.  Comparison of peak with total available licenses can easily identify software features that have excessive license inventory.

2.       Distinct Users

This measure shows the total number of distinct users who accessed a software feature.  If the current license inventory serves a percentage of total users (Distinct Users/Total Users), and the user count in the future is expected to change, a new estimate for Distinct Users can be calculated and applied to the license count.

3.       Daily Usage Variance

This describes the daily usage profile.  For batch jobs, this is typically very close to a circle with a very small standard deviation, which will be a small fraction of the average.  If it is not, high-performance computing (HPC) tuning, job submission parameter tuning, and license inventory tuning is warranted.

4.       Run Duration

This metric measures the average run duration and maximum run duration.  It can be used to group users into different usage categories like Small, Medium, Large or similar.

5.       Denials

There are two important measures, Raw Denials total and Effective denials.  When a user gets a denial of service, many times, the user tries to get a license multiple times in a short duration, which in reality can be considered as one denial.  Effective denials can be estimated using some algorithms that can include heuristics.

6.       Tokens/Run

In most cases, a software feature checks out a set number of tokens.  Some software features check out a variable number of tokens through the lifecycle of 1 run.  Tokens per run provides an average number of tokens used by the software.  This metric can be used to estimate future token needs (license counts).


Additional Metrics

1.       Number of Runs

This is a simple count of how many times a software feature was executed.  It can be used to compare relative usage with respect to other similar software features.

2.       Licenses/Distinct User

One can use this metric in conjunction with the denials to estimate if the current licensing levels are adequate for the user group.

3.       Denials/Run

This metric can be used by a company to determine its threshold of denials tolerance.  For example, if there were 1 denial for every 2 runs, perhaps they have too few licenses.  A company might decide that its denial tolerance is 5% (tolerate 5 effective denials for 100 runs).

4.       Total Usage

This metric allows companies to know which software features are used the most.

5.       Maximum Saturation %

Saturation % is defined as PEAK/Available licenses.  If the PEAK is equal to the total available license, the features is running at 100% saturation, and another request for a license will result in a denial.  This metric will show which software features ran at high saturation levels during the reporting time-span.


I will discuss each of these metrics at length in a series of future posts.

Alhad Joshi
Alhad Joshi

About Alhad Joshi

Alhad Joshi, Vice President of Global Enterprise Analytics Solutions, is responsible for pre-sales and consulting activities associated with Altair's business analytics portfolio. This entails business development and solution architecture guidance. Alhad has managed the development, support, and marketing of Altair's highly scalable and robust Software Asset Optimization (SAO) solution released as a commercial offering in 2012, as well as business intelligence applications for the utilities industry. During his 21-year tenure at Altair, Alhad has worked in software solutions and systems integration, developing solutions that capture best practices in product development and computer-aided engineering. Prior to joining Altair, Alhad worked as a senior developer on the finite element analysis of a printed circuit boards module, creating pre-through-post utilities and automation software. He holds a Master of Science from Ohio University and a Bachelor of Science from the Indian Institute of Technology – Bombay in Mechanical Engineering.