Abstract: Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.

Abstract: A method includes monitoring a temperature of a memory component of a memory sub-system to determine that the temperature of the memory component corresponds to a first monitored temperature value; writing data to the memory component of the memory sub-system while the temperature of the memory component corresponds to the first monitored temperature value; determining that the first monitored temperature value exceeds a threshold temperature range; monitoring the temperature of the memory component of the memory sub-system to determine that the temperature of the memory component corresponds to a second monitored temperature value that is within the threshold temperature range; and rewriting the data to the memory component of the memory sub-system while the temperature of the memory component corresponds to the second monitored temperature value.

Abstract: An apparatus comprises a processing device configured to initiate, in response to a designated event, monitoring of memory allocation and de-allocation operations associated with a given application, to maintain a memory allocation operation data structure comprising entries each corresponding to a memory allocation operation not having an associated memory de-allocation operation, and to determine whether memory usage of the given application corresponds to any of a set of anomalous memory usage pattern rules based on the memory allocation operation data structure. The processing device is also configured to identify the given application as being a cause of a memory leak responsive to determining that the memory usage of the given application corresponds to one or more of the anomalous memory usage pattern rules, and to perform remedial action for preventing or resolving the memory leak responsive to identifying the given application as being the cause of the memory leak.

Abstract: Embodiments may generally be directed to systems and techniques to detect failure events in data pipelines, determine one or more remedial actions to perform, and perform the one or more remedial actions.

Abstract: Embodiments of the present disclosure provide a system for continuous and real-time management and monitoring of robotic process automation bots. In particular, the architecture of the system may comprise a centralized hub which provides various features and functions for bot management and monitoring, such as real-time health status updates, granular logging and notification functions, failure detection and reporting for debugging, bot inventory systems, or the like. Through the use of the components and/or features as described herein, the system may provide an efficient way to manage and monitor robotic process automation bots within a computing environment.

Abstract: An apparatus and a method for optimizing a satellite system considering a hard error stability and a soft error stability are disclosed. The satellite system optimizing method which considers a hard error stability and a soft error stability according to an exemplary embodiment of the present disclosure includes acquiring hardware information of a processor which is loaded in the satellite system; acquiring workload information including a task which is performed by the processor; establishing a scheduling policy for the task based on the hardware information and the workload information; and quantifying a soft error stability and a hard error stability in accordance with the scheduling policy.

Abstract: An average inter-pulse delay of a data unit of the memory device is calculated. An average temperature of the data unit is calculated. A first scaling factor based on the average inter-pulse delay and a second scaling factor based on the average temperature is obtained. A media management metric based on the first scaling factor and the second scaling factor is calculated. Responsive to determining that the media management metric satisfies a media management criterion, a media management operation on the data unit at a predetermined cycle count is performed.

Abstract: 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.

Abstract: Detecting resource errors in a heterogeneous computing environment is provided. A plurality of individual resource dependency graphs corresponding to a plurality of computing systems that comprise the heterogeneous computing environment is consolidated to form a consolidated resource dependency graph. An analysis of respective nodes representing respective resources of the heterogeneous computing environment in the consolidated resource dependency graph is performed to identify a resource error caused by a new resource being added to a computing system of the plurality of computing systems based on defined rule sets. It is determined whether the new resource causes an error to sibling resources at a same level under a parent resource in the consolidated resource dependency graph based on the analysis. In response to determining that the new resource does not cause an error to the sibling resources, the new resource is deployed in the computing system of the heterogeneous computing environment.

Abstract: Systems and methods are provided that may be implemented to perform failure analysis and/or self-healing of information handling system storage. In one example, an information handling system may perform self-recovery actions to self-heal system storage issues when there is a OS boot failure due to a failure to detect a system storage drive by determining one or more possible recovery actions based on a current system storage drive status retrieved by an embedded controller (EC) or other programmable integrated circuit of the information handling system. In another example, manufacturing quality control analysis may be performed on boot failure information that is collected at a remote server from multiple failed information handling systems.

Abstract: Methods and systems are provided for rapid failure recovery for a distributed storage system for failures by one or more nodes.

Abstract: To be able to support the efficient handling of problems during maintenance. A management board determines an accomplishment sequence of handling manipulation for failures on a maintenance target. The management board includes a CPU, memory, and a storage device. The memory or the storage device stores: condition information associating the handling manipulation with a condition concerning at least accomplishment or non-accomplishment of the manipulation; and failure handling information that associates a failure occurring on the maintenance target with the handling manipulation. The CPU is configured to determine the accomplishment sequence of the handling manipulations so that multiple handling manipulations for multiple failures occurring on the maintenance target satisfy the condition.

Abstract: A memory device comprises a memory control unit including a processor configured to control operation of the memory array according to a first memory management protocol for memory access operations, the first memory management protocol including boundary conditions for multiple operating conditions comprising program/erase (P/E) cycles, error management operations, drive writes per day (DWPD), and power consumption; monitor operating conditions of the memory array for the P/E cycles, error management operations, DWPD, and power consumption; determine when a boundary condition for one of the multiple operating conditions is met; and in response to determining that a first boundary condition for a first monitored operating condition is met, change one or more operating conditions of the first memory management protocol to establish a second memory management protocol for the memory access operations, the second memory management protocol including a change boundary condition of a second monitored operating con

Abstract: A system of verifying execution sequence integrity of an execution flow includes a monitoring system in communication with one or more sensors of a system being monitored, where the monitoring system includes one or more electronic devices, and a computer-readable storage medium having one or more programming instructions. When executed, the one or more programming instructions cause at least one of the electronic devices to receive from the sensors, a parameter value for each of one or more parameters that pertain to an operational state of the system, combine the received parameters to generate a combination value, apply a hashing algorithm to the combination value to generate a temporary hash value, search a data store for a result code associated with the temporary hash value, and in response to the result code associated with the temporary hash value indicating that the temporary hash value is incorrect, generate a fault notification.

Abstract: An illustrative data storage management system is aware that certain data storage resources for storing/serving primary data operate in a partnered configuration. Illustrative components of the data storage management system analyze the failover status of the partnered primary data storage resources to determine which is currently serving/storing primary data and/or snapshots targeted for backup. When detecting that a first partnered primary data storage resource has failed over to a second primary data storage resource, the example storage manager changes the assignment of backup resources that are pre-administered for the targeted data. Accordingly, the example storage manager assigns backup resources, including at least one media agent, that are associated with the second primary data storage resource, and which are “closer” thereto from a geography and/or network topology perspective, even if the pre-administered backup resources are available for backup.

Abstract: This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

Abstract: A device may receive error data indicating that an error occurred, the error being associated with a data processing job scheduled to be performed by a data processing platform. The device may identify input data for the data processing job associated with the error and determine that the error is based on the data processing platform not receiving the input data. In addition, the device may determine a location of the input data and determine a measure of priority associated with the data processing job. Based on the location of the input data and the measure of priority, the device may perform an action to correct the error.

Abstract: A method for resolving an erred input/output (IO) flow, the method may include (i) sending over a path a remote direct write request associated with a certain address range; wherein the path is formed between a compute node of a storage system to a storage drive of the storage system; (ii) receiving by the compute node an error message related to the remote direct write request; wherein the error message does not indicate whether an execution of the remote direct write request failed or is only temporarily delayed; (iii) responding by the compute node to the error message by (a) preventing from sending one or more IO requests through the path, (b) preventing from sending at least one IO requests aimed to the certain address range; and (c) requesting, using a management communication link, to force an execution of pending IO requests that are related to the path; and (iv) reuse the path, by the compute node, following an indication that there are no pending IO requests that are related to the path.

Abstract: Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.

Abstract: Methods, apparatuses, and systems related to a memory device are described. A controller may be configured to predict a temperature of a memory based on a real-time temperature of the controller. Based on the predicted temperature of the memory, the controller may execute a remedial action to reduce an actual temperature of the memory for executing an upcoming operation.