Search…
Azure Sizing

Basics

SCEPman depends mainly on the CPU resources. Memory and disc are less important.
A SCEPman instance in one Azure S1 App Service Plan can serve up to 400 requests per minute. Requests are
  • SCEP issuing requests and
  • OCSP requests.

Dependencies

The load for your SCEPman service has several dependencies and varies in the different environments. Important dependencies are:
  1. 1.
    Distribution of requests
  2. 2.
    Frequency of logins to network resources
  3. 3.
    Frequency of certificate requests/renewals
Especially the distribution of the requests has a high importance. If all clients do requests at the same time, your SCEPman instances get heavy load.
Please do not assign SCEP profiles to a large number of users devices at once, since this may result in a request-peak at your SCEPman instances.

Recommendation

We recommend the following sizing in Azure Compute Units (ACU) for the Azure App Service Plans as a starting point:
Amount of users/clients
Singular design
Redundant design
< 2000 clients
100 ACUs (e.g. 1 x S1)
2 x 100 ACUs
(e.g. 2 x S1)
< 5000 clients
200 ACUs
(e.g. 1 x P1V2)
2 x 200 ACUs
(e.g. 2 x P1V2)
< 10.000 clients
400 ACUs
(e.g. 1 x P1V3)
2 x 400 ACUs (e.g. 2 x P1V3)
< 25.000 clients
800 ACUs
(e.g. 1 x P2V3)
2 x 800 ACUs (e.g. 2 x P2V3)
< 50.000 clients
1600 ACUs
(e.g. 1 x P3V3)
2 x 1600 ACUs
(e.g. 2 x P3V3)
< 100.000 clients
3200 ACUs
(e.g. 2 x P3V3)
2 x 3200 ACUs
(e.g. 2 x 2 x P3V3)

Fine tuning

Every environment has its own load distribution over the day. In many environments the morning (start of work) generates a peak in terms of load at your SCEPman.

Manual Scale

You can adapt the computing power for your App Service to your individual daily load distribution with the Azure App Service Scale Out features. E.g. you could define 2 x S1 in the morning from 08:00-10:00 to cover the morning peak, while you reduce to 1 x S1 for the rest of the day.

Auto Scale

Alternatively you can use the Azure App Autoscaling feature to adapt to needed resources. Learn more about that in Autoscaling.

Manual vs. Auto Scale

If you are able to predict your load well (e.g. derived from load history), we recommend Manual Scale over Auto Scale, since Auto Scale has to behave lazy (hysteresis) to prevent flapping between scales.
Copy link
Edit on GitHub
Outline
Basics
Dependencies
Recommendation
Fine tuning
Manual Scale
Auto Scale
Manual vs. Auto Scale