Online load balancing for MapReduce with skewed data input

MapReduce has emerged as a powerful tool for distributed and scalable processing of voluminous data. In this paper, we, for the first time, examine the problem of accommodating data skew in MapReduce with online operations. Different from earlier heuristics in the very late reduce stage or after seeing all the data, we address the skew from the beginning of data input, and make no assumption about a priori knowledge of the data distribution nor require synchronized operations. We examine the input in a continuous fashion and adaptively assign tasks with a load-balanced strategy. We show that the optimal strategy is a constrained version of online minimum makespan and, in the MapReduce context where pairs with identical keys must be scheduled to the same machine, there is an online algorithm with a provable 2-competitive ratio. We further suggest a sample-based enhancement, which, probabilistically, achieves a 3/2-competitive ratio with a bounded error.