How to resample one dataset to conform to the distribution of another dataset?
One simple way is to transform your distribution linearly. That should work fine if the distribution of your data has changed approximately linearly. 


  Question is how to change the distribution of DS_A to match
  distribution of DS_B, with respect to multiple features?


That being said you could transform your feature distribution DS_A by 


Substructing mean(DS_A) and add the mean(DS_B)
Divide with the standard deviation of DS_A and multiply with the standard deviation of DS_B


Long story short:You change the mean and the standard Deviation of the DS_A distribution to match the DS_B.

Here is a code in python that apply this transformation to two gaussian distributions

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

m1, m2 = 0, 10
s1, s2 = 1, 3

x1 = np.random.normal(m1,s1, 1000)
x2 = np.random.normal(m2,s2, 1000)

sns.distplot(x1, hist_kws=dict(alpha=0.1), color='red', label='Distribution 1')
sns.distplot(x2, hist_kws=dict(alpha=0.1), color='green', label='Distribution 2')

estimated_x1_mean = np.mean(x1)
estimated_x1_sd   = np.std(x1)
estimated_x2_mean = np.mean(x2)
estimated_x2_sd   = np.std(x2)

x2_new = (x2 - estimated_x2_mean + estimated_x1_mean)  * estimated_x1_sd / estimated_x2_sd
sns.distplot(x2_new, color='blue', hist_kws=dict(alpha=0.1, edgecolor='black'), label='Distribution 2 after Transformation')
plt.legend()


And the result