Abstract: In some implementations, a recovery system may detect a trigger to generate a system resource document for a target system. The recovery system may communicate, based on detecting the trigger to generate the system resource document, with a set of resources of the target system to perform an auto-discovery procedure. The recovery system may generate the system resource document identifying the set of resources of the target system. The recovery system may store the system resource document in a recovery archive. The recovery system may detect an outage event. The recovery system may obtain, based on detecting the outage event, the system resource document from the recovery archive. The recovery system may fail over the set of resources of the target system to a new target system. The recovery system may transmit a set of notifications associated with the new target system.

Abstract: In some embodiments, conserving computational resources by reducing network traffic predictions via machine learning models may be facilitated. In some embodiments, in connection with a first scheduled time for automated triggering of network resource predictions via a first machine learning model at scheduled times, a first resource usage prediction is received, via the first machine learning model, for a service executing within a computing environment. A first resource allocation process is performed for the service based on the first resource usage prediction. In connection with detecting that an amount of actual traffic corresponding to the service satisfies a traffic threshold for a threshold amount of time, a second resource allocation process is performed, before a second scheduled time next in the scheduled times after the first scheduled time.

Abstract: Systems and methods for active-passive cluster configuration. In some aspects, the system receives a first service log from an active server and a second service log from a passive server, wherein the passive server mirrors the active server. The system identifies, in the first service log, a first configuration file and identifies, in the second service log, a second configuration file. The system inputs the first service log and the second service log into a model trained to identify excessive autoscaling and configuration drift by comparing the first configuration file and the second configuration file. The system, upon identifying differences between the first configuration file and the second configuration file indicative of excessive autoscaling and configuration drift, generates an alert and, based on the alert, generates one or more instructions to resolve the differences between the first configuration file and the second configuration file.

Abstract: Systems and methods for error mitigation in a software pipeline. In some aspects, the system receives a request for evaluating a pipeline at a recurrent interval of time. The request comprises a first version of software, a second version of software, evaluation tests to execute on the second version of the software at intermittent intervals, and the recurrent interval of time. The system, in response to determining the recurrent interval of time elapsed, executes the evaluation tests on the second version of the software. The system determines that the output of the evaluation tests does not match one or more target values. The system terminates deployment of the second version of the software. The system generates instructions to replace the second version of the software with the first version of the software and transmits a notification to a remote device.

Abstract: Systems and methods for active-passive cluster configuration. In some aspects, the system receives a first service log from an active server and a second service log from a passive server, wherein the passive server mirrors the active server. The system identifies, in the first service log, a first configuration file and identifies, in the second service log, a second configuration file. The system inputs the first service log and the second service log into a model trained to identify excessive autoscaling and configuration drift by comparing the first configuration file and the second configuration file. The system, upon identifying differences between the first configuration file and the second configuration file indicative of excessive autoscaling and configuration drift, generates an alert and, based on the alert, generates one or more instructions to resolve the differences between the first configuration file and the second configuration file.

Abstract: In some embodiments, conserving computational resources by reducing network traffic predictions via machine learning models may be facilitated. In some embodiments, in connection with a first scheduled time for automated triggering of network resource predictions via a first machine learning model at scheduled times, a first resource usage prediction is received, via the first machine learning model, for a service executing within a computing environment. A first resource allocation process is performed for the service based on the first resource usage prediction. In connection with detecting that an amount of actual traffic corresponding to the service satisfies a traffic threshold for a threshold amount of time, a second resource allocation process is performed, before a second scheduled time next in the scheduled times after the first scheduled time.

Abstract: In some implementations, a recovery system may detect a trigger to generate a system resource document for a target system. The recovery system may communicate, based on detecting the trigger to generate the system resource document, with a set of resources of the target system to perform an auto-discovery procedure. The recovery system may generate the system resource document identifying the set of resources of the target system. The recovery system may store the system resource document in a recovery archive. The recovery system may detect an outage event. The recovery system may obtain, based on detecting the outage event, the system resource document from the recovery archive. The recovery system may fail over the set of resources of the target system to a new target system. The recovery system may transmit a set of notifications associated with the new target system.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automatically failing over all services operating on a primary region to a secondary region upon detection or notification of a fault in the primary region. When a fault exists on the primary region, the method traverses each cluster containing services operating on the primary region and prepares an input file including a list of service names identifying each service operating on the primary region. Referencing the input file, the method fails over each service from the primary region to the secondary region by modifying a service weight corresponding to each service. This failover process of services may be done simultaneously with failing over any databases corresponding to the failed-over services from the primary region to the secondary region. The method may also introduce a sleep time after modifying each service weight to avoid any potential throttling issues.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automatically failing over all services operating on a primary region to a secondary region upon detection or notification of a fault in the primary region. When a fault exists on the primary region, the method traverses each cluster containing services operating on the primary region and prepares an input file including a list of service names identifying each service operating on the primary region. Referencing the input file, the method fails over each service from the primary region to the secondary region by modifying a service weight corresponding to each service. This failover process of services may be done simultaneously with failing over any databases corresponding to the failed-over services from the primary region to the secondary region. The method may also introduce a sleep time after modifying each service weight to avoid any potential throttling issues.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automatically failing over all services operating on a primary region to a secondary region upon detection or notification of a fault in the primary region. When a fault exists on the primary region, the method traverses each cluster containing services operating on the primary region and prepares an input file including a list of service names identifying each service operating on the primary region. Referencing the input file, the method fails over each service from the primary region to the secondary region by modifying a service weight corresponding to each service. This failover process of services may be done simultaneously with failing over any databases corresponding to the failed-over services from the primary region to the secondary region. The method may also introduce a sleep time after modifying each service weight to avoid any potential throttling issues.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automatically failing over all services operating on a primary region to a secondary region upon detection or notification of a fault in the primary region. When a fault exists on the primary region, the method traverses each cluster containing services operating on the primary region and prepares an input file including a list of service names identifying each service operating on the primary region. Referencing the input file, the method fails over each service from the primary region to the secondary region by modifying a service weight corresponding to each service. This failover process of services may be done simultaneously with failing over any databases corresponding to the failed-over services from the primary region to the secondary region. The method may also introduce a sleep time after modifying each service weight to avoid any potential throttling issues.