Improving Performance and Energy Efficiency of the Classification of Data Streams on Edge Computing
ResumoIn this work, we propose a loop transformation to improve performance and energy efficiency of ensembles. We compare the performance of our technique with three other strategies for improving energy efficiency and throughput in data stream classification using six state-of-the-art ensemble algorithms and four benchmark datasets. Our results show that software strategies can significantly reduce energy consumption. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, mini-batching with loop fusion improved energy efficiency by 136% on average and 456% in the best case.
Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., and Senger, H. (2021). Improving the performance of bagging ensembles for data streams through mini-batching. Information Sciences, 580:260–282.
Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., and Senger, H. (2022). Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. IEEE Trans. on Network and Service Management.
Domingos, P. and Hulten, G. (2000). Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 71–80. ACM SIGKDD.
Gomes, H. M., Barddal, J. P., Enembreck, F., and Bifet, A. (2017). A survey on ensemble learning for data stream classification. ACM Computing Surveys, 50(2):1–36.
Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., and Ahmed, A. (2019). Edge computing: A survey. Future Generation Computer Systems, 97:219–235.
Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., Carvalho, A. C. d., and Gama, J. (2013). Data stream clustering: A survey. ACM Computing Surveys, 46(1):1–31.