Energy Consumption Analysis of Java Commandline Options In 2018 Turing Award lecture, John L. Hennessy discusses softwarecentric opportunities to save Moorexe2x80x99s law. The software is a major consumer of energy in ICT, IoT, and edge systems, but even then the research to make it energy efficient remains fractional. Java is one of the most commonly used languages to develop software for these systems. Java has various commandline options that an application user can use for JVM tuning to enhance the performance of an application. However, there is no study about how these Java commandline options impact the energy consumption of an application. In this work, we explore the impact of various Java commandline options on SPECjvm2008 benchmarks in terms of energy consumption and execution time using different JDKs. Our key findings are: 1) Oracle JDK is more energy efficient than Open JDK, 2) Xint commandline option is the least energy efficient, 3) UseG1GC commandline option is the most energy efficient, and 4) Active energy and execution time show a high correlation.

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.