Implement benchmark scenario WeightedWorkloadOnTreeDataset

[eric-maynard]

This implements a new scenario, WeightedWorkloadOnTreeDataset, that supports the configuration of multiple distributions over which to weight reads & writes against the catalog.

Compared with ReadUpdateTreeDataset, this allows us to understand how performance changes when reads or writes frequently hit the same tables.

Sampling

The distributions are defined in the config file like so:

    # Distributions for readers
    # ...
    readers = [
      { count = 8, mean = 0.3, variance = 0.0278 }
    ]

count is simply the number of threads which will sample from the distribution, while mean and variance describe the Gaussian distribution to sample from. These values are generally expected to fall between 0 and 1.0 and when they don't the distribution will be repeatedly resampled.

For an extreme example, refer to the following:

In this case, about 50% of samples should fall below 0.0 and therefore be resampled. This allows us to create highly concentrated or uniform distributions as needed.

Once a value in [0, 1] is obtained, this value is mapped to a table where 1.0 is the highest table (e.g. T_2048) in the tree dataset and 0.0 is T_0.

To help developers understand the distributions they've defined, some information is printed when the new simulation is run:

. . .

### Writer distributions ###
Summary for Distribution(2,0.7,0.0278):
  Range         | % of Samples | Visualization
  --------------|--------------|------------------
  [0.0 - 0.1) |   0.02%      | 
  [0.1 - 0.2) |   0.14%      | 
  [0.2 - 0.3) |   0.71%      | 
  [0.3 - 0.4) |   2.86%      | █
  [0.4 - 0.5) |   8.40%      | ████
  [0.5 - 0.6) |  16.36%      | ████████
  [0.6 - 0.7) |  23.44%      | ████████████
  [0.7 - 0.8) |  23.37%      | ████████████
  [0.8 - 0.9) |  16.56%      | ████████
  [0.9 - 1.0) |   8.15%      | ████

  The most frequently selected table was chosen in ~6% of samples
. . .