Characterization for MapReduce on the Cloud

MapReduce is now a pervasive analytics engine on the cloud. Hadoop is an open source implementation of MapReduce and is currently enjoying wide popularity. Hadoop offers a high-dimensional space of configuration parameters, which makes it difficult for practitioners to set for efficient and cost-effective execution. In this work we observe that MapReduce application performance is highly influenced by map concurrency. Map concurrency is defined in terms of two configurable parameters, the number of available map slots and the number of map tasks running over the slots. We show that some inherent MapReduce characteristics enable well-informed prediction of map concurrency. We propose Map Concurrency Characterization (MC), a standalone utility program that can predict the best map concurrency for any given MapReduce application. By leveraging the generated predicted information, MC can judiciously guide Map phase configuration and, consequently, improve Hadoop performance. Unlike many of relevant schemes, MC does not employ simulation, dynamic instrumentation, and/or static analysis of unmodified job code to predict map concurrency. In contrast, MC utilizes a simple, yet effective mathematical model, which exploits the MapReduce characteristics that impact map concurrency. We implemented MC and conducted comprehensive experiments on a private cloud and on Amazon EC2 using Hadoop 0.20.2. Our results show that MC can correctly predict the best map concurrencies for the tested benchmarks and provide up to 2.2X speedup in runtime.