Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fireand-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm that faces repeating problem instances, it may be beneficial to learn from network states and the outcome of acceptance decisions of the past. In this paper, we propose ISMAEL, a Machine Learning framework for predicting the acceptance of Virtual Clusters, one of the most common virtual network abstractions in data centers. ISMAEL can be configured with, and learn from, different existing algorithms by combining fixed-size feature representations for graphs with a Convolutional Neural Network or a Fully Connected Deep Neural Network. We report on extensive simulations, which demonstrate that it is possible to mimic existing, computationally-intensive admission control algorithms with an accuracy of up to 94 %, while significantly reducing runtime.

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