Abstract
As cloud traffic never stops growing in minutes, hours and daily basis, proactive cloud resources orchestration becomes a prerequisite. In this paper, we investigate an Amazon case study, in which we intend to compare respectively univariate and multivariate predictions of multimodal AWS instances demand and their related instances resources occupancies. For this purpose, we implemented four nonlinear deep neural network (DNNs) models, namely: LSTM, GRU with their bidirectional variants BiLSTM and BiGRU. Experimentation test scenarios demonstrated the performance of BiGRU models above other candidate models, achieving until (0.71, 0.11, 0.26, 0.97) of RMSE values, respectively while predicting four instances families’ future demands. Adopting an extended BiGRU version, we further demonstrate how multivariate predictions remain much less accurate than univariate forecasting scenarios.
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Hamzaoui, I., Duthil, B., Courboulay, V., Hicham, M. (2022). Predicting Instances Demand and Occupancy Toward Efficient VMs Rightsizing and Resources Allocation Strategies: Amazon Case Study. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_37
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