Abstract
This chapter demonstrates the effectiveness of the ICON loop when applied to energy cost optimization in a data centre. The objective is to schedule the execution of customer tasks such that the overall energy cost is minimised. This is complicated by the fact that the real-time energy price is not known a-priori, therefore machine learning techniques are employed to produce a forecast price vector ahead of time. In practice such a forecast needs to adapt to changes in the world affecting the pricing model over time. Therefore, the model needs to adapt in an iterative process, realised by employing the ICON loop approach.
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Hurley, B., O’Sullivan, B., Simonis, H. (2016). ICON Loop Energy Show Case. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_15
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