  The emerging large-scale and data-hungry algorithms require the computations
to be delegated from a central server to several worker nodes. One major
challenge in the distributed computations is to tackle delays and failures
caused by the stragglers. To address this challenge, introducing efficient
amount of redundant computations via distributed coded computation has received
significant attention. Recent approaches in this area have mainly focused on
introducing minimum computational redundancies to tolerate certain number of
stragglers. To the best of our knowledge, the current literature lacks a
unified end-to-end design in a heterogeneous setting where the workers can vary
in their computation and communication capabilities. The contribution of this
paper is to devise a novel framework for joint scheduling-coding, in a setting
where the workers and the arrival of stream computational jobs are based on
stochastic models. In our initial joint scheme, we propose a systematic
framework that illustrates how to select a set of workers and how to split the
computational load among the selected workers based on their differences in
order to minimize the average in-order job execution delay. Through
simulations, we demonstrate that the performance of our framework is
dramatically better than the performance of naive method that splits the
computational load uniformly among the workers, and it is close to the ideal
performance.
