A trace network for debugging integrated circuits is disclosed. At least one functional network includes a plurality of components interconnected by a number of network switches, implemented on at least one integrated circuit. A trace network is also implemented on the at least one integrated circuit, and includes a plurality of trace circuits configured to generate trace data based on transactions between ones of the plurality of components. The plurality of trace circuits are coupled to one another by a plurality of trace network switches. The trace circuits are configured to convey the generated trace data to an interface, via the trace network, without using the at least one functional network.

An approach to identifying a corrective action for a data storage device (DSD), such as one implemented in a fleet of DSDs in a data center, involves receiving error data about excursions from normal operational behavior of the DSD, inputting data representing a particular excursion into a probabilistic decision network which characterizes a set of DSD operational metrics and certain DSD controller rules that represent internal controls of the DSD and corresponding conditional relationships among the operational metrics, determining from the decision network the likelihood that one or more possible causes was a contributing factor to the particular excursion, and determining a corrective action for the particular excursion based on the determined likelihood of a particular cause of the one or more possible causes. The corrective action may then be shared with the DSD for in-situ execution of corresponding self-repair operations.

Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

A distributed network system and method includes a processing unit configured to manage safety data for a plurality of medical devices, a database software component in communication with the processing unit, and a monitoring software component in communication with the processing unit. The monitoring software component is configured to monitor a number of messages between a number of medical devices and the processing unit, to process performance parameters to generate an overall performance index, and to generate an output that is viewable by a user. The output includes relative contributions of each of the performance parameters to the overall performance index, where the overall performance index is generated using a weighting factor associated with each of the performance parameters. The performance parameters include the number of messages waiting to be processed, which has the largest weighting factor, and a disk queue length, which has the smallest weighting factor.

A method includes determining, by a field instrument in an industrial process and control system, a Highway Addressable Remote Transducer (HART) mode of the field instrument. The method also includes, upon a determination, by the field instrument, that the HART mode is a HART On Demand mode, listening for a HART data signal from a HART master device; when the HART data signal is detected, communicating with the HART master device according to a HART protocol; and when the HART data signal is not detected, diverting a current supply allocated for HART communication to a BLUETOOTH Low Energy (BLE) transceiver for use in BLE communication, and communicating according to a BLE protocol.

A policy generation agent automatically generates a security policy for an application and a security manager. The agent runs the application in a development environment, causing the application to request permissions from the security manager. The agent passes the permissions request to the security manager. The security manger determines whether to approve or deny the request based on a permissions policy. Responsive to a determination to deny the request, the agent generates an updated permissions policy by updating the permissions policy to approve subsequent requests for the permissions. The agent also associates the updated permissions policy with the application, and suppresses any exceptions generated by the security manager in denying the request before approving the request for the permissions in the development environment.

A system maintains a knowledge layout to support the analysis of active events and determination of epicenter and aftershock nodes via an event reach stack. At an input layer of the event reach stack, the system may receive active event data. At a semantic layer, the system may parse the active event data to determine event phrases. Based on the event phrases, the system may identify epicenter nodes directly affected by the active event. At an analytic model layer, the system may successively determine aftershock nodes by traversing the knowledge layout outward from the epicenter nodes. The system then directs the response to the active event to the aftershock and epicenter nodes, via action at a focus response layer of the event reach stack.

An illustrative report server interoperates with one or more enhanced storage managers to evaluate whether backup operations and restore operations meet their recovery point objectives (RPO) and recovery time objectives (RTO), respectively. RTO is evaluated using a tiered approach based on past performance of restore and/or backup operations. The illustrative storage manager executes pre-defined queries that extract relevant information from an associated database that houses information about storage operations. The report server recommends alternative kinds of backup operations for data that fails to meet its RTO using traditional backups. The report server is configured to analyze and report RPO and RTO readiness for several levels of data entities, including multiple systems, single system, groups of clients, single clients, and subclients.

Disclosed herein are methods, systems, and processes for automated log entry identification and alert management. A log statement that includes a log format string and is part of program code associated with a computer program is accessed at a log management server. The execution of the log statement generates a log string that is associated with a trigger pattern of an alert configuration. A fixed part of the log format string that remains unchanged during execution of the log statement when the program code associated with the computer program is executed is extracted and a template is generated for the log statement to track changes to the fixed part of the log format string that causes a mismatch between the trigger pattern of the alert configuration and the log string. The template is then stored. A modification to the fixed part of the log format string is tracked using the template and the trigger pattern of the alert configuration that corresponds to the log string is updated based on the modification.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.