With increasingly inexpensive storage and growing processing power, the cloud has rapidly become the environment of choice to store and analyze data for a variety of applications. Most large-scale data computations in the cloud heavily rely on the MapReduce paradigm and on its Hadoop implementation. Nevertheless, this exponential growth in popularity has significantly impacted power consumption in cloud infrastructures. In this paper, we focus on MapReduce processing and we investigate the impact of dynamically scaling the frequency of compute nodes on the performance and energy consumption of a Hadoop cluster. To this end, a series of experiments are conducted to explore the implications of Dynamic Voltage and Frequency Scaling (DVFS) settings on power consumption in Hadoop clusters. By enabling various existing DVFS governors (i.e., performance, powersave, ondemand, conservative and userspace) in a Hadoop cluster, we observe significant variation in performance and power consumption across different applications: the different DVFS settings are only sub-optimal for several representative MapReduce applications. Furthermore, our results reveal that the current CPU governors do not exactly reflect their design goal and may even become ineffective to manage the power consumption in Hadoop clusters. This study aims at providing a clearer understanding of the interplay between performance and power management in Hadoop clusters and therefore offers useful insight into designing power-aware techniques for Hadoop systems.

Big Data systems (e.g., Google MapReduce, Apache Hadoop, Apache Spark) rely increasingly on speculative execution to mask slow tasks, also known as stragglers, because a job's execution time is dominated by the slowest task instance. Big Data systems typically identify stragglers and speculatively run copies of those tasks with the expectation that a copy may complete faster to shorten job execution times. There is a rich body of recent results on straggler mitigation in MapReduce. However, the majority of these do not consider the problem of accurately detecting stragglers. Instead, they adopt a particular straggler detection approach and then study its effectiveness in terms of performance, e.g., reduction in job completion time, or efficiency, e.g., high resource utilization. In this paper, we consider a complete framework for straggler detection and mitigation. We start with a set of metrics that can be used to characterize and detect stragglers including Precision, Recall, Detection Latency, Undetected Time and Fake Positive. We then develop an architectural model by which these metrics can be linked to measures of performance including execution time and system energy overheads. We further conduct a series of experiments to demonstrate which metrics and approaches are more effective in detecting stragglers and are also predictive of effectiveness in terms of performance and energy efficiencies. For example, our results indicate that the default Hadoop straggler detector could be made more effective. In certain case, Precision is low and only 55% of those detected are actual stragglers and the Recall, i.e., percent of actual detected stragglers, is also relatively low at 56%. For the same case, the hierarchical approach (i.e., a green-driven detector based on the default one) achieves a Precision of 99% and a Recall of 29%. This increase in Precision can be translated to achieve lower execution time and energy consumption, and thus higher performance and energy efficiency; compared to the default Hadoop mechanism, the energy consumption is reduced by almost 31%. These results demonstrate how our framework can offer useful insights and be applied in practical settings to characterize and design new straggler detection mechanisms for MapReduce systems. This work is supported by the ANR KerStream project (ANR-16-CE25-0014-01) and the Stack/Apollo connect talent project. The experiments presented in this paper were carried out using the Grid'5000/ALADDIN-G5K experimental testbed, an initiative from the French Ministry of Research through the ACI GRID incentive action, INRIA, CNRS and RENATER and other contributing partners (see http://www.grid5000.fr/ for details).

Hadoop emerged as an important system for largescale 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)-resulted from resource contention and heterogeneity in clouds-by launching speculative task copies on other machines. However, speculative execution is not cost-free 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 in-between 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.

Energy consumption is an important concern for large-scale data-centers, which results in huge monetary cost for data-center operators. Due to the hardware heterogeneity and contentions between concurrent workloads, straggler mitigation is important to many Big Data applications running in large-scale data-centers and the speculative execution technique is widely-used to handle stragglers. Although a large number of studies have been proposed to improve the performance of Big Data applications using speculative execution, few of them have studied the energy efficiency of their solutions. In this paper, we propose two techniques to improve the energy efficiency of speculative executions while ensuring comparable performance. Specifically, we propose a hierarchical straggler detection mechanism which can greatly reduce the number of killed speculative copies and hence save the energy consumption. We also propose an energy-aware speculative copy allocation method which considers the trade-off between performance and energy when allocating speculative copies. We implement both techniques into Hadoop and evaluate them using representative MapReduce benchmarks. Results show that our solution can reduce the energy waste on killed speculative copies by up to 100% and improve the energy efficiency by 20% compared to state-of-the-art mechanisms.