Abstract
Studies show that virtual machines (VMs) in cloud are easily forgotten with non-productive status. This incurs unnecessary cost for cloud tenants and resource waste for cloud providers. As a solution to this problem, we present our Cloud Server Idleness Identification (CSI2) system. The CSI2 system collects data from the servers in cloud, performs analytics against the dataset to identify the idle servers, then provides suggestions to the owners of the idle servers. Once the confirmation from the owners are received, the idle servers are deleted or archived. We not only design and implement the CSI2 system, but also bring it alive into production environment.
How to accurately identify the idleness in cloud is the challenging part of this problem, because there is a trade-off between the cost saving and the user experience. We build a machine learning model to handle this challenge. In addition to that, we also build an advanced tool based on Bayesian optimization (BO) to help us finely tune the hyperparameters of the models. It turns out that our finely tuned models works accurately, successfully handling the aforementioned conflict, and outperforms its predecessors with a F1 score of 0.89.
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Duan, J. et al. (2019). CSI2: Cloud Server Idleness Identification by Advanced Machine Learning in Theories and Practice. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_19
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DOI: https://doi.org/10.1007/978-3-030-33702-5_19
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