Abstract
In this paper, we propose a fast and accurate block-level operation (writing or reading) and transferred size prediction method based on Regularized Extreme Learning Machine, which represents a key component towards sustainable, green data center. The proposed RELM-based method can produce competitive performance at fast learning speed. Benefitting from the random weights of RELM, these two prediction tasks can be unified as one, thus reducing the training time to half. Experiments on SNIA shows that block-level operation type prediction can reach an accuracy of 99.04%, while the transferred size prediction is at 0.0234 NRMSE.
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Deng, WY., Su, C.L., Ong, YS. (2015). Access Behavior Prediction in Distributed Storage System Using Regularized Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_33
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DOI: https://doi.org/10.1007/978-3-319-14066-7_33
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