MapReduce has gradually become the framework of choice for \"Big data\". The MapReduce model allows for efficient and swift processing of large scale data with a cluster of compute nodes. However, the efficiency here comes at a price. The performance of widely used MapReduce implementations such as Hadoop suffers in heterogeneous and load-imbalanced clusters. The authors show the disparity in performance between homogeneous and heterogeneous clusters in this paper to be high. Subsequently, they present MARLA, a MapReduce framework capable of performing well not only in homogeneous settings, but also when the cluster exhibits heterogeneous properties.