  Serverless clouds allocate multiple tasks (e.g., micro-services) from
multiple users on a shared pool of computing resources. This enables serverless
cloud providers to reduce their resource usage by transparently aggregate
similar tasks of a certain context (e.g., video processing) that share the
whole or part of their computation. To this end, it is crucial to know the
amount of time-saving achieved by aggregating the tasks. Lack of such knowledge
can lead to uninformed merging and scheduling decisions that, in turn, can
cause deadline violation of either the merged tasks or other following tasks.
Accordingly, in this paper, we study the problem of estimating execution-time
saving resulted from merging tasks with the example in the context of video
processing. To learn the execution-time saving in different forms of merging,
we first establish a set of benchmarking videos and examine a wide variety of
video processing tasks -- with and without merging in place. We observed that
although merging can save up to 44% in the execution-time, the number of
possible merging cases is intractable. Hence, in the second part, we leverage
the benchmarking results and develop a method based on Gradient Boosting
Decision Tree (GBDT) to estimate the time-saving for any given task merging
case. Experimental results show that the method can estimate the time-saving
with the error rate of 0.04, measured based on Root Mean Square Error (RMSE).
