Efficient traffic allocation
I suggest you try a simple solution: randomly decide whether to assign users to an experiment when they visit the corresponding page.

Suppose you want to have 100 users to a particular experiment, call it experiment E.  When a user visits the page for experiment E, if that user hasn't been previously assigned to any experiment, flip a biased coin and assign the user to experiment E if that coin comes up heads.  How do you decide what the probability of heads should be?  If historically you see 2000 visitors per two-week period, and you want 100 users, then the heads probability should be about 1/20.  Then after two weeks you should get about the desired number of users. 

This assumes you have historical data on the number of visitors to each page, to set the probability appropriately.  If you don't have historical data, there are some alternative strategies you could use.  For instance, if you want 100 users for experiment E, you could randomly pick 100 different times throughout the two-week period; for each selected time, take the next unassigned user who visits that page (the first one who visits the page after that time) and assign them to experiment E.  In this way you are randomly selecting 100 different users.

These techniques treat each experiment independently, which simplifies the assignment process.  This is simple and I suspect it might work well enough for running experiments in practice.  Only consider something more complicated if you have evidence that this won't work.  (How can you tell whether it will work?  You could take your historical data and simulate the strategy on historical data and see if it gets you enough users for each experiment.)