Abstract: Methods and systems for managing execution of inference models across multiple data processing systems are disclosed. To manage execution of inference models across multiple data processing systems, a system may include an inference model manager and any number of data processing systems. The inference model manager may obtain operational capability data for the inference models from the data processing systems. The inference model manager may use the operational capability data to determine whether the data processing systems have access to sufficient computing resources to complete timely execution of the inference models. If the data processing systems do not have access to sufficient computing resources to complete timely execution of the inference models, the inference model manager may re-assign one or more data processing systems to re-balance the computing resource load and support continued operation of at least a portion of the inference models.

Abstract: Methods and systems for managing execution of an inference model hosted by data processing systems are disclosed. To manage execution of the inference model, a system may include an inference model manager and any number of data processing systems. The inference model manager may identify an inference frequency capability of the inference model hosted by the data processing systems and may determine whether the inference frequency capability of the inference model meets an inference frequency requirement of a downstream consumer during a future period of time. If the inference frequency capability does not meet the inference frequency requirement of the downstream consumer, the inference model manager may modify a deployment of the first inference model to meet the inference frequency requirement of the downstream consumer.

Abstract: Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, data processing systems that host the inference models may be monitored for risk of compromise. The data processing systems may be monitored by evaluating the processing results generated by the data processing systems. When a data processing system is identified as being compromised, a self-healing process or other remediation process may be initiated to reduce or eliminate the threat to successful inference generation presented by the compromised data processing system.

Abstract: Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, the conditions to which the data processing systems are likely to be exposed are identified. The identified conditions may be used to select operating modes for inference models hosted by the data processing systems. The operating modes may define when and under which conditions the inference models hosted by the data processing systems are automatically modified. The automatic modifications may adapt the inference models to the conditions to which the data processing systems are exposed.

Abstract: Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, inference models that may share computations with other inference models may be preferentially selected for deployment. Additionally, the inference models may be divided into portions to distribute the computation load across more data processing systems. The location of deployment of the portions of the inference models may be preferentially selected based on the locations of data sources and inference consumers.

Abstract: Methods and systems for managing execution of an inference model hosted by data processing systems are disclosed. To manage execution of the inference model hosted by the data processing systems, a system may include an inference model manager and any number of data processing systems. The inference model manager may monitor the risk of unsuccessful execution of the inference model by the data processing systems and may proactively take action to support inference generation in the event of reduced functionality of one or more of the data processing systems. The inference model manager may distribute multiple redundant instances of the inference model so that each data processing system only hosts one instance of the inference model. Inference model manager may also obtain an execution plan to respond to a failure of one or more data processing systems to ensure no inference model bottlenecks occur during re-deployment of the inference model.

Abstract: Methods and systems for timely execution of inference models hosted by data processing systems are disclosed. To manage inference models hosted by data processing systems, a system may include an inference model manager and any number of data processing systems. The inference models may include inference models with higher complexity topology and inference models with lower complexity topology. The data processing systems may be provided with instructions for when to execute higher complexity topology inference models and when to operate lower complexity topology inference models. The inference model manager may analyze data processing system information and downstream consumer information to determine whether the current deployment of inference models to the data processing systems is capable of meeting the needs of a downstream consumer. Based on the analysis, an execution plan for the inference models and the distribution of inference models may be updated.

Abstract: Methods and systems for managing execution of inference models hosted by data processing systems are disclosed. To manage execution of inference models hosted by data processing systems, a system may include an inference model manager and any number of data processing systems. The inference model manager may communication system data for the communication system linking the data processing systems. The inference model manager may use the communication system data to determine whether the communication system meets inference generation requirements of the downstream consumer. If the communication system does not meet inference generation requirements of the downstream consumer, the inference model manager may obtain an inference generation plan to return to compliance with the inference generation requirements of the downstream consumer.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. IN one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. IN one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: Embodiments for providing automated selection of optimal disk types for virtualized storage by defining a minimum number of backup samples, selecting, if the minimum number of backup samples is not met for a backup operation, a solid state drive (SSD) for a virtual machine (VM) storage for a disaster recovery operation, otherwise selecting a hard disk drive (HDD) for the VM storage. The method further defines a cold HDD threshold (CHT) value and a minimal percentage of backups (PPT) value, and obtains a cold backup count based on the CHT value. It compares a ratio of the cold backup count to an amount of backups (AB) for the disaster recovery operation to the defined PPT value, and if the ratio is greater than the PPT value, it selects the SSD rather than HDD for the VM storage.

Abstract: Embodiments for providing automated selection of optimal disk types for virtualized storage by defining a minimum number of backup samples, selecting, if the minimum number of backup samples is not met for a backup operation, a solid state drive (SSD) for a virtual machine (VM) storage for a disaster recovery operation, otherwise selecting a hard disk drive (HDD) for the VM storage. The method further defines a cold HDD threshold (CHT) value and a minimal percentage of backups (PPT) value, and obtains a cold backup count based on the CHT value. It compares a ratio of the cold backup count to an amount of backups (AB) for the disaster recovery operation to the defined PPT value, and if the ratio is greater than the PPT value, it selects the SSD rather than HDD for the VM storage.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. IN one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: Instances of client applications running independently on different devices are monitored to generate metadata records that are stored in cloud storage. Metadata records in cloud storage that have an aspect in common are grouped and used for comparative analysis. For example, metadata records for instances of the same client application, database, query or database structure may be grouped. Reports may be generated based on the analysis. The reports may indicate comparative performance, irregular activity, patch version and configurations associated with performance degradation, and recommended actions to improve performance.