Towards Efficient Power Management in MapReduce: Investigation of CPU-Frequencies Scaling on Power Efficiency in Hadoop

Shadi Ibrahim (1) , Diana Moise (2) , Houssem-Eddine Chihoub (3) , Alexandra Carpen-Amarie (4) , Luc Bougé (1) , Gabriel Antoniu (1)

1 KerData - Scalable Storage for Clouds and Beyond
2 InIT Cloud Computing Lab, ZHAW Winterthur
3 ZENITH - Scientific Data Management
4 TU Wien - Vienna University of Technology = Technische Universität Wien

With increasingly inexpensive cloud storage and increasingly powerful cloud processing, the cloud has rapidly become the environment to store and analyze data. Most of the large-scale data computations in the cloud heavily rely on the MapReduce paradigm and its Hadoop implementation. Nevertheless, this exponential growth in popularity has significantly impacted power consumption in cloud infrastructures. In this paper, we focus on MapReduce 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 Frequency scaling (DVFS) settings on power consumption in Hadoop-clusters. By adapting existing DVFS governors (i.e., performance, powersave, ondemand, conservative and userspace) in the Hadoop cluster, we observe significant variation in performance and power consumption of the cluster with different applications when applying these governors: the different DVFS settings are only sub-optimal for different 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 more clear understanding of the interplay between per- formance and power management in Hadoop cluster and therefore offers useful insight into designing power-aware techniques for Hadoop systems.

Mots clés: MapReduce; Governors; Power management; DVFS; Hadoop

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Informatique

Shadi Ibrahim, Diana Moise, Houssem-Eddine Chihoub, Alexandra Carpen-Amarie, Luc Bougé, et al.. Towards Efficient Power Management in MapReduce: Investigation of CPU-Frequencies Scaling on Power Efficiency in Hadoop. ARMS-CC: Adaptive Resource Management and Scheduling for Cloud Computing, Jul 2014, Paris, France. pp.147-164, ⟨10.1007/978-3-319-13464-2_11⟩. ⟨hal-01077285⟩