Relevance feedback based online learning model for resource bottleneck prediction in cloud servers
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DOI: 10.1016/j.neucom.2020.04.080
Abstract: Abstract Cloud servers are highly prone to resource bottleneck failures. In this work, we propose an ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck identification. The workload at cloud servers is highly… read more here.
Keywords: cloud servers; online learning; resource bottleneck; model ... See more keywords