Application-driven Energy-efficient Architecture Explorations for Big Data


Building energy-efficient systems is critical for big data ap- plications. This paper investigates and compares the energy consumption and the execution time of a typical Hadoop- based big data application running on a traditional Xeon- based cluster and an Atom-based (Micro-server) cluster. Our experimental results show that the micro-server platform is more energy-efficient than the Xeon-based platform. Our experimental results also reveal that data compression and decompression accounts for a considerable percentage of the total execution time. More precisely, data compression/ de- compression occupies 7-11% of the execution time of the map tasks and 37.9-41.2% of the execution time of the reduce tasks. Based on our findings, we demonstrate the necessity of using a heterogeneous architecture for energy-efficient big data processing. The desired architecture takes the advan- tages of both micro-server processors and hardware compres- sion/ decompression accelerators. In addition, we propose a mechanism that enables the accelerators to perform more efficient data compression/decompression.