Event records from multiple computing devices are received at a managing unit. Individual event records include an event identifier field including an event identifier identifying a first event associated with a particular computing device, a parent event identifier field identifying a parent event that initialized the first event, and an entity identifier field including an entity identifier identifying the particular computing device. The managing unit generates log records associated with event identifiers included in the event records. The log records include state fields indicating a state of a particular event associated with a particular event identifier. Based on a correlation of the event and log records, the managing unit determines at least two computing devices associated with events resulting in an error state, and identifies parent events that initialized the events with errors. The managing unit generates a report linking the parent events to the events having an error state.

The present disclosure relates to systems and methods to conduct a fuzzer test on a device under test and configured for use in an electric power system. In one embodiment, a system may include a configuration subsystem to receive a parameter of the device under test. A fuzzer subsystem in communication with the configuration subsystem may be configured to conduct a fuzzer test on the device under test. The fuzzer subsystem may include a fuzzer state machine to generate input data to deliver to the device under test, a packet buffer to store input data generated by the fuzzer state machine, and a packet regulator to deliver input data generated by the fuzzer state machine based the parameter. A physical interface in communication with the packet regulator may transmit input data to the device under test based on the parameter.

A method may include obtaining at least one dataset that includes information corresponding to periods of usage of a plurality of components within a cloud infrastructure and usage cost for each component of the plurality of components within the cloud infrastructure. The method may include comparing the information corresponding to the periods of usage with at least a portion of the information corresponding to the usage cost for components. The method may include determining a cost for one or more of the components for a period of time. The cost may be determined based on the comparison of the information corresponding to the periods of usage of the components with at least the portion of the information corresponding to the usage cost for the components. The method may include generating a visualization that includes information representative of the cost of the components and displaying the visualization via a display screen.

A method and system for auto live-mounting database golden copies. Specifically, the disclosed method and system entail reactively auto live-mounting golden copy databases on hosts or proxy hosts based on the operational state of one or more database hosts and/or one or more assets (or databases) residing on the database host(s). Should a database host prove to be unresponsive, through periodic monitoring, databases residing on the database host may be brought back online on a proxy database host using stored golden copies respective of the aforementioned databases. Alternatively, should a given database on any database host exhibit an operational abnormality (e.g., an error, failure, etc.), the given database may be brought back online on the database host or a proxy database host using a stored golden copy respective of the given database. Accordingly, through the disclosed method and system, database outages may be minimized.

Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.

Methods, systems and computer program products are provided for automated runtime configuration for dataflows to automatically select or adapt a runtime environment or resources to a dataflow plan prior to execution. Metadata generated for dataflows indicates dataflow information, such as numbers and types of sources, sinks and operations, and the amount of data being consumed, processed and written. Weighted dataflow plans are created from unweighted dataflow plans based on metadata. Weights that indicate operation complexity or resource consumption are generated for data operations. A runtime environment or resources to execute a dataflow plan is/are selected based on the weighted dataflow and/or a maximum flow. Preferences may be provided to influence weighting and runtime selections.

The present disclosure provides a method for information storage and a system thereof, which adapts to a data storage system. A monitoring unit is configured to detecting and monitoring operations of a storage node in the data storage system to generate corresponding one and more monitoring data. A recording processor is configured to receiving the one or the plurality of monitoring data, and rendering one or a plurality of logs according to the difference of content of the one or the plurality of monitoring data. The adjustment mechanism is performed according to the stored logs, thereby the amount of large data generated during monitoring is effectively reduced.

One embodiment provides a computer implemented method of estimating replication completion time. The method includes creating a historical dataset of prior replication data; determining a set of replication parameters to consider; inputting the historical dataset and the set of replication parameters to a replication completion time estimator module; generating a replication completion time prediction based on the historical dataset and the set of replication parameters; and generating a confidence prediction corresponding to the replication completion time prediction.

A system is configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. A first visual includes a radar-based visual that renders an object representing data for a set of metrics being monitored. A second visual includes a tree map visual that includes sections where each section is associated with an attribute used to compose the set of metrics. Via the display of the visuals, the techniques provide an improved way of representing a large number of metrics (e.g., hundreds, thousands, etc.) being monitored for a platform. Moreover, the techniques are configured to expose useful information associated with the platform in a manner that can be effectively interpreted by a user.

Example techniques for adaptive time window-based log message deduplication are described. In an example, message values are obtained from received log messages. Further, the number of log messages received in a time window having a message value is counted. A log message from which the message value is obtained and the counted number are transmitted upon expiry of the time window. A length of a time window in which a subsequent counting of log messages is to be performed is determined based on various parameters.