Business processes orchestrate service requests in a structured fashion. Process knowledge, however, has rarely been used to predict and decide about cloud infrastructure resource usage. In this paper, we present an approach for BPM-aware cloud computing that builds on process knowledge to improve the timeliness and quality of resource scaling decisions. We introduce an IaaS resource controller based on fuzzy theory that monitors process execution and that is used to predict and control resource requirements for subsequent process tasks. In a laboratory experiment, we evaluate the controller design against a commercially available state-of-the-art auto scaler. Based on the results, we discuss improvements and limitations, and suggest directions for further research.
Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.
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