Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

no code implementations • 17 Jan 2022 • Guilherme Cassales, Heitor Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger

Such strategies can significantly reduce energy consumption in 96% of the experimental scenarios evaluated.

Cloud Computing Edge-computing

Improving the performance of bagging ensembles for data streams through mini-batching

no code implementations • 18 Dec 2021 • Guilherme Cassales, Heitor Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger

This paper proposes a mini-batching strategy that can improve memory access locality and performance of several ensemble algorithms for stream mining in multi-core environments.

Ensemble Learning Incremental Learning

Modelling Energy Consumption based on Resource Utilization

no code implementations • 15 Sep 2017 • Lucas Venezian Povoa, Cesar Marcondes, Hermes Senger

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids.

Management regression