Disclosed is a method for detecting a disappearance of a task in an environment including at least one recurring parent task that triggers, in nominal mode, on each occurrence, at least one child task, including the following steps: evaluating a parent duration elapsed between the last occurrence and the penultimate occurrence of the parent task, evaluating a child duration elapsed between the last occurrence and the penultimate occurrence of the child task, comparing the parent duration and the child duration, it being concluded that an occurrence of the child task has disappeared if the child duration, preferably with a margin, is longer than the parent duration.

Methods that use marking, leveling and linking (“MLL”) processes to identify problems and dynamically correlate events recorded in various log files generated for a use case of an application are described. The marking process determines fact objects associated with the use-case from events recorded in the various log files, database dumps, captured user actions, network traffic, and third-party component logs in order to identify non-predefined problems with running the application in a distributed computing environment. The MLL methods do not assume a predefined input format and may be used with any data structure and plain log files. The MLL methods present results in a use-case trace in a graphical user interface. The use-case trace enables human users to monitor and troubleshoot execution of the application. The use-case trace identifies the types of non-predefined problems that have occurred and points in time when the problems occurred.

A method includes receiving a first progress request from a first state machine associated with execution of a first thread on a processor. The method includes updating a current state of a temporal relationship state machine based on the current state, the first progress request, and a predetermined temporal relationship between progress of the first state machine to a first state machine state and progress to a second state. The predetermined temporal relationship may require the first state machine to progress to the first state machine state before the progress to the second state. The current state of the temporal relationship state machine may be one of a first temporal relationship state and a second temporal relationship state. The second state may be a second state machine state of the first state machine. The second state may be a second state machine state of a second state machine.

A protection method and device for application data are provided. The method includes: acquiring a data request sent by a monitored application, wherein the data request is used for requesting data in a first data source in which data needing protection is stored (S302); and redirecting the data request from the first data source to a second data source, wherein the second data source is used to store false data of the data needing protection (S304).

A computerized method for identification of suspicious processes executing on an end-point device communicatively connected to network, the network communicatively connected to a server, the method comprising receiving, by the server, a record of at least one process, initiated by and executing on by the end-point device. One or more parameters associated with the at least one process are identified. A first time pointer is identified corresponding to the identified one or more parameters, a first time pointer. A second time pointer at which a user associated with the end-point device initiated a user dependent process is identified. Whether the second time pointer occurred before the first time pointer is identified. It is determined whether the at least one process was initiated by the user based on identification of user dependent processes and corresponding attribution. An action is performed based on the above determination.

Machine learning is utilized to analyze respective execution times of a plurality of tasks in a job performed in a distributed computing system to determine that a subset of the plurality of tasks are straggler tasks in the job, where the distributed computing system includes a plurality of computing devices. A supervised machine-learning algorithm is performed using a set of inputs including performance attributes of the plurality of tasks, where the supervised machine learning algorithm uses labels generated from determination of the set of straggler tasks, the performance attributes include respective attributes of the plurality of tasks observed during performance of the job, and applying the supervised learning algorithm results in identification of a set of rules defining conditions, based on the performance attributes of the plurality of tasks, indicative of which tasks will be straggler tasks in a job. Rule data is generated to describe the set of rules.

A server computer. The server computer comprises a processor, a non-transitory memory, a application comprising JavaScript instructions stored in the non-transitory memory, a runtime stored in the non-transitory memory, and a native agent module stored in the non-transitory memory. When executed by the processor, the runtime provides a JavaScript execution environment for executing the application and an instrumentation application programming interface (API). When executed by the processor outside of the runtime, the native agent module monitors memory buffers allocated to the application based on accessing the instrumentation API of the runtime, executes an event loop that sends an interrupt to the runtime, and provides reporting based on monitoring the memory buffers and the interrupt sent to the runtime to a management layer external to the server computer.

A computer-implemented method, system, and computer device for modifying an appearance of an object in an electronic display of a computer device based on a progress of a task is provided. The method includes monitoring a progress of a first task of a first application. The method also includes identifying a first object of a second application, and modifying an appearance of the first object of the second application based on the progress of the first task, wherein the second application is distinct from the first application.

This disclosure relates generally to anomaly detection, and more particularly to system and method for detecting anomalies. In one embodiment, the method includes executing at least one thread associated with the application. Executing the at least one thread results in invoking one or more methods associated with the at least one thread. During the execution metrics associated with the one or more methods are captured. The metrics are systematically arranged in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs. One or more anomalies associated with the one or more methods are identified from the data structure based on a detection of at least one predetermined condition in the data structure. An anomaly of the one or more anomalies includes one of un-exited anomaly, an exception anomaly and a user-defined anomaly.

A processing system is configured to dynamically carry out processes. A method for monitoring the processing system includes steps of determining a number of processes running on the processing system; of determining a maximum expected number of processes; of determining that more processes than expected are running; and of deactivating the processing system.