In a method of molecular scaffold hopping an interface of a scheduler computer sends instructions, prepared by the scheduler computer, to a job runner computer to perform a plurality of separate computational tasks. Each of the separate computational tasks includes calculating one or more chemical properties for a query molecule or molecules in a library of molecules. One or more of the plurality of separate computational tasks performed on the job runner computer are preemptible computing instances. Status indicators sent from the job runner computer are received by the interface for each of the plurality of separate computational tasks. The indicators are one of: incomplete, completed, or failed computing instances. The interface resends the instructions to the job runner computer that correspond to the separate computational tasks having the failed computing instance indicator to increase fault-tolerance against the separate computational tasks not attaining the completed computing instance indicator.

Methods for improving operation of a user device executing an application. The methods include collecting a first set of data corresponding to a run time environment of the application, collecting a second set of data corresponding to a crash of the application, identifying a cause of the crash based on the first set of data and a second set of data and determining the cause of the crash is associated with an application feature corresponding to a feature flag.

An electronic computing system preserves a pre-error state of a processing unit by receiving a first stream of inputs; buffering the first stream of inputs to generate a buffered stream of inputs identical to the first stream of inputs; conveying the first stream to a primary instance of a first program; conveying the buffered stream to a secondary instance of the first program; executing the primary instance on the first stream in real time; executing the secondary instance on the buffered stream with a predefined time delay with respect to execution of the primary instance on the first stream; detecting an error state resulting from execution of the primary instance; and in response to detecting the error state, pausing the secondary instance and preserving a current state of the secondary instance, wherein the current state of the secondary instance corresponds to a pre-error state of the primary instance.

Techniques are disclosed for executing a data processing pipeline. The techniques may include receiving a job at a data pipeline queue, setting up one or more distributed processing environments, and allocating the job to one of the distributed processing environments. The techniques may further include receiving the allocated job at a job queue within the distributed processing environment, increasing a priority level of the job, and executing the job within the distributed processing environment. The techniques can further include providing a retry pipeline at the data processing pipeline, and re-executing the job at a stage following a failure of at least one of its components. The techniques may decrement the retry budget as the job is re-executed.

When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Systems and methods are provided for generating a flow control plan for a plurality of components in a flow control system. A decentralized multi-agent control framework is used to plan and schedule for each agent independently without a central processor. Each agent of the multi-agent control framework separately optimizes a local portion of the system as a function of values for one or more parameters. Agents communicate with other connected agents, sharing values for parameters. The communication provides a negotiation and consensus for values of the shared parameters that are used by the agent to recalculate optimized parameters values for the local portion of the system.

Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to determine a state of the plurality of data processing requests, determine, using an event state decision machine learning model, remedial actions to resolve an error state, and provide instructions to the processing applications to perform the remedial actions.

A system and method for detecting and fixing robotic process automation failures, including collecting tasks from at least one client computerized device, processing the tasks via robotic process automation, collecting tasks that failed to complete per task type, recording successful execution steps per each of the failed tasks, evaluating the recorded successful execution steps with respect to the failed task types, and providing selected execution steps that best fix the failed tasks, thereby fixing the robotic process automation failures.

The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.

A quality score for a computer application release is determined using a first number of unique users who have launched the computer application release on user devices and a second number of unique users who have encountered at least once an abnormal termination with the computer application release on user devices. Additionally or optionally, an application quality score can be computed for a computer application based on quality scores of computer application releases that represent different versions of the computer application. Additionally or optionally, a weighted application quality score can be computed for a computer application by further taking into consideration the average application quality score and popularity of a plurality of computer applications.