Abstract: Systems and methods related to prediction-based resource orchestration in a service fabric are described. An example method for reducing an interruption rate associated with a customer application offered as a collection of services includes predicting one or more quiet time periods associated with each of the service replicas associated with a service fabric, where each of the one or more quiet time periods corresponds to a low value of a predicted load specifying a consumption of a metric by a respective service replica on a given node. The method further includes during the one or more quiet time periods predicted by a trained machine learning model, performing an impact-less failover for one or more of the service replicas by scheduling a move for the one or more service replicas from a first node associated with the service fabric to a second node associated with the service fabric.

Abstract: A de-asphalting system for solvent de-asphalting including a desasphalter and a controller. The deasphalter defines a contacting zone and a separation zone. The contact zone contacts a feed, including asphaltenes, and a solvent to form a mixture, where the contacting of the feed and the solvent causes at least a portion of the asphaltenes to precipitate out of the mixture. The contacting is disposed at an operating temperature. The separation zone separates the mixture into a de-asphalted oil-comprising material fraction (“S+PDAO”) and a asphaltene-rich material fraction. The asphaltene-rich material fraction includes the precipitated asphaltenes. The controller controls at least one operating parameter of the deasphalter based on at least on a refractive index of the S+PDAO phase.