We had more than enough RAM for our working set. Unfortunately, due to MongoDB's poor memory managed and non-counting B-trees, even our hot data would sometimes be purged out of memory for cold, unused data, causing serious performance degradation.
If you have solid (even reasonable) IO, then moving things in and out of working memory is not painful. We have some customers on non-EBS spindles that have very large working sets (as compared to memory) ... faulting 400-500 times per second, and hardly notice performance slow downs.
I think your suggestions are legit, but faulting performance has just as much to do with IO congestion. That applies to insert/update performance as well.
There's really two knobs to turn on Mongo, RAM and disk speed. Our particular cluster doesn't have enough RAM for the dataset to fit in memory, but could double its performance (or more) if each key range was mmapped individually rather than the entire datastore the shard is responsible for just because of how the OS manages pages. We haven't broken down to implement it yet, but with the performance vs. cost tradeoffs, we may have to pretty soon.
But the point is it shouldn't need to do that I/O in the first place.