Before revision:  Redundancy could mitigate stragglers by reducing the dependence of the computing engine on every server. Nevertheless, redundancy could yet be a burden to the system and aggravate stragglers. In this paper, we consider task replication as the redundancy technique andstudy the optimum redundancy planning to improve the performance of a master-worker distributed computing system. We start with optimum assignment policy of a given set of redundant tasks to a set of workers. Using the results from majorization theory, we show thatif the service time of workersis a stochastically (decreasing and) convex random variable , a balanced assignment of non-overlapping batches of tasks minimizes the average job compute time.
After revision:  Tasks are grouped into batches and assigned to one or more workers for execution. We first consider the case when the batches do not overlap and, using the results from majorization theory, we show thatif the service time of workersis a stochastically (decreasing and) convex random variable , a balanced assignment of non-overlapping batches of tasks minimizes the average job compute time.