Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every node. We begin by considering that no cluster is truly homogeneous with respect to energy consumption. From there we develop a MapReduce framework that can evaluate the current status of each node and dynamically react to estimated power usage. Inso doing, we shift power consumption work toward more energy efficient nodes which are currently consuming less power. Our work shows that given an ideal framework configuration, certain nodes may consume only 62.3% of the dynamic power they consumed when the same framework was configured as it would be in a traditional MapReduce implementation.
Temperature measurement, Power demand, Stress, Temperature sensors, Energy consumption, Correlation, Heterogeneous, MapReduce, Energy, Dynamic
Jessica Hartog, Zacharia Fadika, Elif Dede, Madhusudhan Govindaraju, "Configuring a MapReduce Framework for Dynamic and Efficient Energy Adaptation", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 914-921, doi:10.1109/CLOUD.2012.137