Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.

A storage system has a plurality of storage nodes having equal non-volatile storage capacity that is subdivided into equal size cells. Host application data that is stored in the cells is protected using RAID or EC protection groups each having members stored in ones of the cells and distributed across the storage nodes such that no more than one member of any single protection group is stored by any one of the storage nodes. Spare cells are maintained for rebuilding protection group members of a failed one of the storage nodes on remaining non-failed storage nodes so full data access is possible before replacement or repair of the failed storage node.

Present invention discloses method and system for generating knowledge graph and sub-graph clusters to perform a root cause analysis. Method comprising extracting at least one of objects, data entities, links between the objects and the data entities, or relationships between the objects and the data entities from input content. Thereafter, method comprising generating a knowledge graph from the extracted data and sub-graphs from the knowledge graph using an unsupervised ML technique and extracting graph data structure information for each sub-graph. Subsequently, method comprising generating root cause model based on the sub-graphs and the graph data structure information and generating at least one sub-graph cluster and corresponding probabilistic graphical model using the root cause model and the knowledge graph. Generated Knowledge graph, root cause model and at least one sub-graph cluster and corresponding probabilistic graphical model are used to determine a root cause for an issue from an issue content.

A system includes a plurality of watchdog components. Each watchdog component is configured to receive a kick signal from its monitored function to determine whether the monitored function is active. Each watchdog component is further configured to receive a respective token from all watchdog components that the each watchdog component is connected to. The respective token determines whether its respective watchdog component has timed out. Each watchdog component is further configured to generate a token responsive to the kick signal and further responsive to the respective token from all watchdog component that the each watchdog component is connected to. Each watchdog component is further configured to transmit the generated token to the all watchdog components that the each watchdog component is connected to.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

An apparatus includes a memory and a hardware processor. The memory stores a plurality of logging rules. Each logging rule assigned to a tier of a multi-tier platform. The processor receives source code for an application configured to execute on a plurality of tiers of the multi-tier platform and detects, within the source code, an entry point and an exit point for a tier of the plurality of tiers. The processor determines, based on the plurality of logging rules, a first attribute that is to be logged during execution in the tier and a second attribute that is not to be logged during execution in the tier and inserts, between the entry point and the exit point in the source code, logging code that, when executed, logs the first attribute and hides the second attribute.

Exemplary embodiments may perform feasibility analysis to determine whether it is possible to generate a lookup table that conforms to an error tolerance given a specification of a function or a set of data points that the lookup table attempts to approximate, an indication of breakpoint positions, and a specification of a data type for table values. Where it is determined that it is feasible to generate the lookup table, the lookup table may be automatically programmatically generated. Suggestions of how to modify the breakpoint positions and/or error tolerance may be provided. In addition, a visualization of approximation error and error tolerance, such as a visualization showing a feasibility margin, may be output. New data points may be processed to update table values for an already generated lookup table.

Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

An electronic element includes: a module for storing reference data; a module for receiving data from a processor; a module for verifying the received data by comparison by way of reference data; and a module for transmitting an instruction to cut off supply of the processor, the supply cutoff instruction being transmitted after occurrence of a failure event, the failure event being an absence of reception of data or a failure in verifying the data. A system including such an electronic element and a method for monitoring a processor by the electronic element are also described.

An electronic element includes: a module for storing reference data; a module for receiving data from a processor; a module for verifying the received data by comparison by way of reference data; and a module for transmitting an instruction to cut off supply of the processor, the supply cutoff instruction being transmitted after occurrence of a failure event, the failure event being an absence of reception of data or a failure in verifying the data. A system including such an electronic element and a method for monitoring a processor by the electronic element are also described.