Abstract: Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems for determination of anomalous data are provided. In a computer-implemented method, a plurality of data packets is received within a predetermined time period, the plurality of data packets comprising a data structure. A historical distribution of historical data including the data structure as the data packets is determined. The plurality of data packets is compared to the historical distribution to generate a comparison result. If it is determined that data anomaly exists in the plurality of data packets according to the comparison result, an alert indicating the data anomaly is generated.

Abstract: A method may include receiving instructions to process a first application in response to a user request. The method also includes determining whether to store the first application in a first processing queue or a second processing queue based on a comparison between a CPU processing cost associated with the first application and a GPU processing cost associated with the first application. Further, the method includes grouping a first set of applications stored in the first processing queue according to CPU grouping criteria and grouping a second set of applications stored in the second processing queue according to GPU batching criteria. The method also includes causing a CPU to process the grouped first set of applications and a plurality of GPUs to process the grouped second set of applications.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating a self-adaptive system for large scale online machine learning computations. In one embodiment, a system is introduced that can generate and execute an optimization plan for combining nodes based on relationship information. The execution of the optimization plan can occur at both a static and dynamic state to determine how to best execute node combining. In another embodiment, pool isolation is executed base on the processing information associated with each node.