Energy efficiency in cloud-based MapReduce applications through better performance estimation

Abstract

An important issue for efficient execution of MapReduce jobs on a cloud platform is selecting the best fitting virtual machine (VM) configuration(s) among the miscellany of choices that cloud providers offer. Wise selection of VM configurations can lead to better performance, cost and energy consumption. Therefore, it is crucial to explore the available configurations and choose the best one for each given MapReduce application. Executing the given application on all the configurations for comparison is a costly, time and energy consuming process. An alternative is to run the application on a subset of configurations (sample configurations) and estimate its performance on other configurations based on the obtained values on those sample configurations. We show that the choice of these sample configurations highly affects accuracy of later estimations. Our Smart Configuration Selection (SCS) scheme chooses better representatives from among all configurations by once-off analysis of given performance figures of the benchmarks so as to increase the accuracy of estimations of missing values, and consequently, to more accurately choose the configuration providing the highest performance. The results show that the SCS choice of sample configurations is very close to the best choice, and can reduce estimation error to 7.11% from the original 16.02% of random configuration selection. Furthermore, this more accurate performance estimation saves 24.3% energy on average.