Abstract: Systems and methods for generating runtime predictions in distributed computer architectures are disclosed. According to one embodiment, a method for generating runtime predictions in a distributed computer architectures may include: (1) receiving, by a runtime prediction computer program executed by an electronic device, training data regarding completion of a plurality of risk compute jobs; (2) extracting, by the runtime prediction computer program, bucketing statistics and instrument level features from the data; (3) training, by the runtime prediction computer program, a machine learning model with the training data, the extracted bucketing statistics, and the instrument level features; (4) receiving, by the runtime prediction computer program, a plurality of jobs for a period of time; and (5) calculating, by the runtime prediction computer program, an instrument cost for each of the jobs using the machine learning model.

Abstract: Various methods, apparatuses/systems, and media for event-based task processing are disclosed. The system includes a processor and a memory operatively connected to the processor via a communication interface.

Abstract: Systems and methods for affinity-based distribution of work are disclosed. The method may include determining a classification for a plurality of tasks. The method may further include classifying a compute cost and an affinity for each task of the plurality of tasks. The method may further include assigning the plurality of tasks to a work collection. The method may further include selecting one or more tasks from the plurality of tasks in the work collection based on a selection strategy of a worker node. The method may further include performing a compute task associated with the one or more tasks. The method may further include generating a set of results based on performing the compute task.