Scalar entities for k means clustering
While doing clustering analysis, you would prefer to have features which discriminate data points and help you discover more homogeneous segments. However, if the value of a particular feature or variable is same for large percentage of the data, then that feature would not add any information for clustering.

Let us take an example, say you are dealing with bank customers data and there's a feature called 'country' in it. If in the given data, most of the customers have country as USA, then you would want to simply drop that feature as it adds no useful information. This is the extreme case in which there is single unique value.

In your network data logs example, port numbers and protocols would be mostly the same. The web server might be listening on port 443 for HTTPS protocol connection. So in that case, the two columns can be excluded from clustering analysis.