There are several popular resampling techniques, which are often used in practice, such as bootstrapping, permutation test, jackknife, etc. There are numerous articles & books discuss these techniques, for example Philip I Good (2010) Permutation, Parametric, and Bootstrap Tests of Hypotheses

My question is which resampling technique has gained the more popularity and easier to implement? Bootstrapping or permutation tests? 

Both are popular and useful, but primarily for different uses.  The permutation test is best for testing hypotheses and bootstrapping is best for estimating confidence intervals.

Permutation tests test a specific null hypothesis of exchangeability, i.e. that only the random sampling/randomization explains the difference seen.  This is the common case for things like t-tests and ANOVA.  It can also be expanded to things like time series (null hypothesis that there is no serial correlation) or regression (null hypothesis of no relationship).  Permutation tests can be used to create confidence intervals, but it requires many more assumptions, that may or may not be reasonable (so other methods are preferred).  The Mann-Whitney/Wilcoxon test is actually a special case of a permutation test, so they are much more popular than some realize.

The bootstrap estimates the variability of the sampling process and works well for estimating confidence intervals.  You can do a test of hypothesis this way but it tends to be less powerful than the permutation test for cases that the permutation test assumptions hold.
