Resource Demand Profiling of Monolithic Workflows.

We propose a novel approach for resource demand profiling of resource-intensive monolithic workflows that consist of different phases. Workflow profiling aims to estimate the resource demands of workflows. Such estimates are important for workflow scheduling in data centers and enable the efficient use of available resources. Our approach considers the workflows as black boxes, in other words, our approach can fully rely on recorded system-level metrics, which is the standard scenario from the perspective of data center operators. Our approach first performs an offline analysis of a dataset of resource consumption values of different runs of a considered workflow. For this analysis, we apply the time series segmentation algorithm PELT and the clustering algorithm DBSCAN. This analysis extracts individual phases and the respective resource demands. We then use the results of this analysis to train a Hidden Markov Model in a supervised manner for online phase detection. Furthermore, we provide a method to update the resource demand profiles at run-time of the workflows based on this phase detection. We test our approach on Earth Observation workflows that process satellite data. The results imply that our approach already works in some common scenarios. On the other hand, for cases where the behavior of individual phases is changed too much by contention, we identify room and next steps for improvements.