Described herein are example systems and computer-implemented methods for monitoring changes to an application. For example, information regarding a change made to an aspect of an application may be received by a processor. It, may be determined if a similarity of the change to a cluster of changes related to the aspect is within a change threshold. Further, the change may be associated with the cluster of changes when the similarity of the change is within the change threshold. It may be further determined if a metric based on a number of changes associated with the cluster of changes is within a cluster range. When the metric within the cluster range, a prototype change may be extracted from the cluster of changes. The application may be updated based on the prototype change when the metric is within the cluster range.

In the present invention, a management system has a storage device and a processor. The storage device holds an information processing program for controlling information pertaining to a storage system by utilizing a database. The processor executes an update program that updates the information processing program and the database utilized by the information processing program. The update program calculates an estimated update time needed to update the information processing program and the database on the basis of the size of at least a portion of the database utilized by the information processing program before being updated and the structure of the database utilized by the information processing program after being updated, and outputs the estimated update time thus calculated.

Methods and apparatus to execute a workload in an edge environment are disclosed. An example apparatus includes a node scheduler to accept a task from a workload scheduler, the task including a description of a workload and tokens, a workload executor to execute the workload, the node scheduler to access a result of execution of the workload and provide the result to the workload scheduler, and a controller to access the tokens and distribute at least one of the tokens to at least one provider, the provider to provide a resource to the apparatus to execute the workload.

A method and a gateway system of a mobile asset are described. A voltage produced by a power system of the mobile asset is monitored by a gateway system. In response to determining that a first fluctuation occurs in the voltage produced by the power system of the mobile asset over a first interval of time, the gateway system operates in a first mode. In response to determining that a value of the voltage produced by the power system of the mobile asset is less than a voltage threshold, the gateway system automatically transitions to operating in a second mode, which is different from the first mode and causes the gateway system to consume less power than when it is operating in the first mode.

A disclosed load estimation method includes: collecting run information of a processor being executing a predetermined program; specifying execution status of the processor based on the collected run information; and estimating a load of the predetermined program based on a result of comparison between the execution status of the processor and execution characteristics of the processor. Each of the execution characteristics is stored in association with a load level of the predetermined program.

A method, a system, and a computer program product for analyzing error messages. A first error log generated as a result of an execution of at least one task of a computing system at a first instance is received. The first error log include a plurality of first error messages. A first association rules model is generated using the first error messages. The first association rules model includes a plurality of association rules defining one or more relationships. A second error log, including a plurality of second error messages, generated as a result of an execution of the task at a second instance is received and a second association rules model is generated using the second error messages. Based on the first and second association rules models, at least one error message pattern associated with execution of the at least one task is determined.

Techniques are disclosed for capturing diagnostics data in a distributed computing environment comprising a plurality of computing devices executing a plurality of Kubernetes pods. A worker node is configured with a staging area for storing temporary diagnostics data. An agent is configured to upload the temporary diagnostics data. Each container in the worker node is assigned a directory in the staging area for writing the container's temporary diagnostics data. When a container in the worker node has written a temporary diagnostics data file to the container's directory in the staging area, the temporary diagnostics data file is uploaded to the persistent storage.

Systems and methods are described for facilitating operation of a plurality of computing devices. Data indicative of enumerated resources of a computing device is collected. The data is collected without dependency on write permissions to a file system of the one computing device. A condition of the computing device is determined based on historical data associated with enumerated resources of other computing devices. The identified condition can be updated as updated historical data becomes available. A communication to the computing device may be sent based on the identified condition.

To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

A method for managing provenance information associated to one or more interconnected provenance entities in a provenance system for data processing pipelines in a distributed cloud environment over a network interface, wherein each of the data processing pipelines is configured to read in data, transform the data, and output transformed data is disclosed. The method comprises steps being performed by a configuration component of obtaining at least one declarative intent representing a configuration indicative of requirements and levels of priority for storage of provenance information for each of the data processing pipelines, deriving the requirements and levels of priority for storage of provenance information for each of the data processing pipelines based on the obtained at least one declarative intent, wherein one of the levels of priority—first level of priority—is higher than the other levels of priority—second levels of priority, estimating storage capacity for storage of provenance information in the provenance system based on the derived requirements and levels of priority, storing the provenance information according to the derived requirements and levels of priority for storage of provenance information and for each of the data processing pipelines, and when actual storage consumption for storage of provenance information in the provenance system meets a threshold of storage capacity set based on the estimated storage capacity: reducing a data amount for storage of provenance information of the second levels of priority in the provenance system. Corresponding computer program product, arrangement, configuration component, and system are also disclosed.