Experimental Evaluation of Multi-Round Matrix Multiplication on MapReduce

A common approach in the design of MapReduce algorithms is to minimize the number of rounds. Indeed, there are many examples in the literature of monolithic MapReduce algorithms, which are algorithms requiring just one or two rounds. However, we claim that the design of monolithic algorithms may not be the best approach in cloud systems. Indeed, multi-round algorithms may exploit some features of cloud platforms by suitably setting the round number according to the execution context. In this paper we carry out an experimental study of multi-round MapReduce algorithms aiming at investigating the performance of the multi-round approach. We use matrix multiplication as a case study. We first propose a scalable Hadoop library, named M3, for matrix multiplication in the dense and sparse cases which allows to tradeoff round number with the amount of data shuffled in each round and the amount of memory required by reduce functions. Then, we present an extensive study of this library on an in-house cluster and on Amazon Web Services aiming at showing its performance and at comparing monolithic and multi-round approaches. The experiments show that, even without a low level optimization, it is possible to design multi-round algorithms with a small running time overhead

On data skewness, stragglers, and MapReduce progress indicators

We tackle the problem of predicting the performance of MapReduce applications, designing accurate progress indicators that keep programmers informed on the percentage of completed computation time during the execution of a job. Through extensive experiments, we show that state-of-the-art progress indicators (including the one provided by Hadoop) can be seriously harmed by data skewness, load unbalancing, and straggling tasks. This is mainly due to their implicit assumption that the running time depends linearly on the input size. We thus design a novel profile-guided progress indicator, called NearestFit, that operates without the linear hypothesis assumption and exploits a careful combination of nearest neighbor regression and statistical curve fitting techniques. Our theoretical progress model requires fine-grained profile data, that can be very difficult to manage in practice. To overcome this issue, we resort to computing accurate approximations for some of the quantities used in our model through space- and time-efficient data streaming algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive empirical assessment over the Amazon EC2 platform on a variety of real-world benchmarks shows that NearestFit is practical w.r.t. space and time overheads and that its accuracy is generally very good, even in scenarios where competitors incur non-negligible errors and wide prediction fluctuations. Overall, NearestFit significantly improves the current state-of-art on progress analysis for MapReduce