Benchmarking and Performance Modelling of MapReduce Communication Pattern Understanding and predicting the performance of big data applications running in the cloud or onpremises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the lowlevel internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the lowlevel internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average, the error rate in both setups is xc2xb110% from the measured values.

On Understanding the Energy Impact of Speculative Execution in Hadoop Hadoop emerged as an important system for large scale data analysis. Speculative execution is a key feature in Hadoop that is extensively leveraged in clouds: it is used to mask slow tasks (i.e., stragglers) xe2x80x94 resulted from resource contention and heterogeneity in clouds xe2x80x94 by launching speculative task copies on other machines. However, speculative execution is not costfree and may result in performance degradation and extra resource and energy consumption. While prior literature has been dedicated to improving stragglers detection to cope with the inevitable heterogeneity in clouds, little work is focusing on understanding the implications of speculative execution on the performance and energy consumption in Hadoop cluster. In this paper, we have designed a set of experiments to evaluate the impact of speculative execution on the performance and energy consumption of Hadoop in homo and heterogeneous environments. Our studies reveal that speculative execution may sometimes reduce, sometimes increase the energy consumption of Hadoop clusters. This strongly depends on the reduction in the execution time of MapReduce applications and on the extra power consumption introduced by speculative execution. Moreover, we show that the extra power consumption varies inbetween applications and is contributed to by three main factors: the duration of speculative tasks, the idle time, and the allocation of speculative tasks. To the best of our knowledge, our work provides the first deep look into the energy efficiency of speculative execution in Hadoop.