Energy-Efficient Data Processing at Sweet Spot Frequencies



The processing of Big Data often includes sorting as a ba- sic operator. Indeed, it has been shown that many software applications spend up to 25% of their time sorting data. Moreover, for compute- bound applications, the most energy-efficient executions have shown to use a CPU speed lower than the maximum speed: the CPU sweet spot frequency. In this paper, we use these findings to run Big Data intensive applications in a more energy-efficient way. We give empirical evidence that data-intensive analytic tasks are more energy-efficient when CPU(s) operate(s) at sweet spots frequencies. Our approach uses a novel high- precision, fine-grained energy measurement infrastructure to investigate the energy (joules) consumed by different sorting algorithms. Our experiments show that algorithms can have different sweet spot frequencies for the same computational task. To leverage these findings, we describe how a new kind of self-adaptive software applications can be engineered to increase their energy-efficiency.


Energy-Efficient, Data Processing


Energy-Efficient Data Processing


On the Move to Meaningful Internet Systems (OTM 2014), January 2014


Cited by

Year 2015 : 2 citations

 Energy bugs in mobile devices: A survey, A Demidem, H Elmiligi, F Gebali - … , Computers and Signal …, 2015 -

 Energy-efficient Benchmarking for Energy-efficient Software, D Pukhkaiev - 2015 -