LAUSR

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 dynamic in nature. To support continuous online learning of resource bottleneck identification models, we propose relevance feedback based online learning of proposed ensemble model. Here we propose to analyse, catastrophe forgetting and incremental architectural evolution as two fundamental challenges associated with online adaptation of LSTM-based multiclass classifier models. To avoid catastrophic forgetting, we propose a combination of distillation loss and the standard crossentropy loss. For architectural evolution, we propose and analyse three different alternatives to update the architecture of the bottleneck identification model on the fly. We evaluate the proposed approaches on a real world dataset collected in an industrial case study and on a dataset collected in a virtual environment setup using Docker containers. The experimental results show that the proposed approaches outperform existing state-of-the-art methods for bottleneck identification.