483results about "Error avoidance" patented technology

As described herein, various embodiments of the present invention improve computational efficiency of performing reliable persistent monitoring of a computer system. Persistent monitoring of the operations of a computer system has various operational reliability benefits for various computer systems and is an important part of service level objectives for highly maintenance-critical computer systems. However, doing the noted persistent monitoring operations in a reliable manner requires access to large amounts of labeled training data that are not always available for more customized computer systems with unique operational / behaviorial pattern signatures. In response, various embodiments of the present invention address the noted challenges by generating training data for an anomalous operational state detection machine learning model whose training data may be generated using a ground-truth validation criterion that is defined based at least in part on an inferred outlier score for a decomposed residual component of a given operational monitoring timeseries trend.

A method for quantum error correction includes: trapping and / or manipulating an array of qubits that include encoded neutral atoms or ions and forms a computational space; encoding the array of qubits to a state characterized in that an error during at least one of an idling, gate, or other operation results in a respective qubit transition to a disjointed state where that error can be detected without disturbing the qubits remaining in the computational space; performing a syndrome measurement of a quantum error correcting code; identifying the presence and location of an error by detecting the corresponding neutral atom or ion has left the computational space after performing the operation; identifying a logical error in the quantum error correcting code associated with a loss of qubit information based on the identified error location and results of the syndrome measurement; and adjusting subsequent computational steps based on the identified logical error.

Aspects of the disclosure identify a pattern of features associated with a computing component indicating an increased probability that the component incurs an uncorrectable error. Historical component data, component features, server data, and server services are utilized to identify the patterns that correlate to an increased probability that the component incurs an uncorrectable error. For example, this data is used as input into a machine learning platform. Proactive and / or mitigating actions that reduce the probability of an uncorrectable error or its negative effects are presented and / or implemented to minimize or eliminate disruption in cloud computing services.

Techniques for managing resource constraints of a cloud environment are disclosed. A system receives a request to initiate a provisioning process for provisioning a first service in the cloud environment. The system determines a resource constraint associated with a resource that the first service utilizes. Based on the resource constraint, the system determines a set of candidate services that also utilize the resource as candidates for deprovisioning from the cloud environment. The system identifies respective service features of the set of candidate services and generates a ranking of the set of candidate services based on weighting metrics associated with the respective service features. Based on the ranking, the system selects a second service of the set of candidate services for deprovisioning from the cloud environment. The system deprovisions the second service to alleviate the resource constraint and then provisions the first service by executing the provisioning process.

Techniques for identifying faults in a collaborative computing system including a plurality of disparate, geographically separated computing systems are described herein. An intelligent monitoring (IM) server computing system may receive data from the plurality of computing devices and may monitor the health of the collaborative computing system. The IM server computing system may analyze the data and identify one or more faults associated with a portion of the collaborative system (e.g., an associated computing device, platform, network, etc.). In some examples, the IM server computing system may be configured to identify potential future faults associated with the portion of the collaborative system. Based on the fault, the IM server computing device may determine an action to take to remedy the fault and / or prevent the potential future fault. The IM server computing device may either automatically perform the action or send a notification to the associated computing system to perform the action.

The disclosure relates to systems and methods for automated performance testing in a containerized environment. For example, a system may invoke one or more PTSs (PTSs) that each manage a respective type of performance test, such as a load test, a stress test, and a soak test. Each PTS may instantiate one or more dynamic clusters in a container to execute performance test scripts that execute operations to conduct the performance test. The PTS may estimate a testing capacity of each dynamic cluster so that the load during testing does not exceed those limits. The dynamic clusters may perform client-side load balancing that approximates real-world load profiles to more accurately simulate real-world loads based on user behaviors that would occur throughout a test session. The system may use logging and metrics from a diverse set of services that monitor target systems during the automated performance test.

Systems and methods for diagnosing accidents in Information Handling Systems (IHSs) are described. In some embodiments, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause an Operating System (OS) to: in response an accident, collect fan data and, based at least in part upon a comparison between collected fan data and stored fan data, perform an accident remediation or mitigation operation.

The invention provides a task termination strategy generation method and system based on a dynamic programming algorithm, a storage medium and electronic equipment, and relates to the field of risk control. The method comprises the following steps: firstly, defining that the deterioration state of a system obeys a multi-stage continuous time Markov chain and random task duration; secondly, constructing a value function to obtain expected costs of tasks in all possible execution durations in each deterioration state; and finally, a reverse induction method is combined with a dynamic programming algorithm, forward recursion is carried out from the end point time of the longest task duration, so that an optimal task termination decision under each time step length is determined, and global optimization can be realized. According to the method, the uncertainty of the task execution time in an actual system can be reflected more truly, the dynamic change in the task execution process can be monitored in real time, and the task termination strategy is dynamically adjusted based on real-time data, so that a complex and changeable task environment can be flexibly coped, and the survival rate of the system is maximized.

Techniques for risk management in a supply chain are described. In one aspect, to determine an overall impact of a fault associated with an equipment operating in a facility, multiple operational parameters of the equipment are monitored to detect an anomaly, based on which a fault in the equipment is identified. On identifying the fault, a first impact factor corresponding to an energy impact, a second impact factor corresponding to an emission impact, and a third impact factor corresponding to an economic impact are derived based on the multiple operational parameters. Further, an integrated risk indicator is computed from the impact factors to determine a current overall impact of the fault on the equipment. Accordingly, a risk profile including one or more remedial strategies is generated based on the integrated risk indicator and is issued to personnel of the facility for risk management.

Conventional error detection for integration processes in an integration platform are inefficient and require significant expertise. Accordingly, an error prediction model is disclosed. The error prediction model may be operated to produce error predictions, based on the current design (e.g., lineage) of an integration process, during construction of that integration process (e.g., on a virtual canvas). A generative language model may also be used to provide the error predictions in natural language. This enables the efficient troubleshooting and resolution of errors in an integration process, prior to that integration process being deployed and executed, and without requiring significant expertise.

A maintenance system for maintaining a hardware system is described. The maintenance system includes an acquisition circuit and an analysis circuit. The acquisition circuit can be configured to acquire a plurality of operational parameters of the hardware system, the plurality of operational parameters being relevant to a health state of the hardware system. The acquisition circuit can be configured to forward the plurality of operational parameters to the analysis circuit. The analysis circuit can be configured to determine a device health score for the hardware system, a remaining useful life of the hardware system, and / or a user recommendation for operating the hardware system, each based on the plurality of operational parameters. Further, a method of analyzing and / or creating health information for a hardware system is described.

Embodiments relate to early detection of information technology (IT) failures in a computing system. A technique is executed by one or more processors and includes receiving multiple IT records including past and recent historical data, extracting first and second time series sections from the past and recent historical data, respectively, training a failure detection model to correlate metric patterns in the first time series sections with at least one of other metric patterns and previous IT failures and, in response to the training, using the failure detection model to predict at least one of upcoming metric patterns and upcoming IT failures from metric patterns in the second time series sections.

The invention discloses a memory fault prediction method and device, a chip, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps that error position distribution of an error correction unit associated with a memory is obtained, the memory comprises at least one memory particle, the error correction unit comprises a plurality of data bits obtained from a plurality of storage units of the memory through continuous multiple burst reading, and each data bit corresponds to one storage unit in the memory; the error position distribution is used for indicating the position of an error data bit in a plurality of data bits, and the error position distribution causes the occurrence of CE in the memory; a memory fault model is obtained, the memory fault model comprises at least one reference position distribution, and the reference position distribution is associated with the UCE of the memory; according to the similarity between the error position distribution and the at least one reference position distribution, a prediction result is output, and the prediction result is used for indicating the probability that the UCE occurs in the memory. The method is beneficial to improving the timeliness of memory fault prediction.

The invention discloses a multi-mode induction display screen response method fusing voice interaction, and relates to the technical field of man-machine interaction, and the method comprises the following steps: in a user interaction process, calling a built-in sensor through an interface component real-time state monitoring mechanism, continuously monitoring a rendering process of a target interface component at a millisecond-level time granularity, and displaying the rendering process of the target interface component; collecting multi-dimensional availability state parameter information of the interface component; the method comprises the following steps: preprocessing collected interface component multi-dimensional availability state parameter information, and extracting key indexes reflecting potential asynchronous triggering risks from preprocessed data through a feature engineering method; by sensing the state of the interface component in real time, extracting the asynchronous risk index and combining machine learning recognition and TCN prediction modeling, dynamic regulation and control of the induction triggering window and the animation rhythm are achieved, the problem of mispointing or dislocation of the induction component is avoided, the accuracy and safety of interaction response are improved, and the user experience is improved. The method is suitable for high-precision scenes such as vehicle-mounted, medical and industrial control.

Instead of incurring interface errors caused by power drops, continuously track all the channels between the controller and the storage device (both in-band and side-band channels). The tracking will include extracting relevant indications and use a prediction model that correlates between the tracked indications to later occurrence of power drop events. In response to the prediction results, the system may perform different rehabilitation operations (countermeasures). The controller will monitor different indications from the storage element and power supply to predict a signal integrity degradation marginality event on the interface. The monitoring and mitigation strategy relies on an open-ended system. The controller does not employ a real-time continuous return channel from the storage device that may signal pass / fail conditions. Given the nature of an open-ended system and tolerance of calibration for the data gathering, processing and inference system, the controller will have to cope also with false positive events.

According to one aspect, a method includes: receiving, by a node of a computer cluster, a command for a health status report, the node having a plurality applications and being one of a plurality of nodes of the cluster, the command indicating a class of a health checks to be executed and a scope; identifying, from a configuration, one or more health checks to be executed based on the indicated class; executing the one or more health checks on one or more of the applications and on one or more of the nodes of the cluster according to the indicated scope, where different ones of the health checks are configured to determine state of different ones of the applications; aggregating, by the node, results of the executed health checks; and outputting the aggregated results.

Techniques are provided for maintaining and recomputing reference counts in a persistent memory file system of a node. Primary reference counts are maintained for pages within persistent memory of the node. In response to receiving a first operation to link a page into a persistent memory file system of the persistent memory, a primary reference count of the page is incremented before linking the page into the persistent memory file system. In response to receiving a second operation to unlink the page from the persistent memory file system, the page is unlinked from the persistent memory file system before the primary reference count is decremented. Upon the node recovering from a crash, the persistent memory file system is traversed in order to update shadow reference counts for the pages with correct reference count values, which are used to overwrite the primary reference counts with the correct reference count values.

Systems and methods of loopback control in multi-active database synchronization are provided. A first database instance and a second database instance are configured with two-way synchronization. A synchronization tool detects a first log change in a first operation log associated with the first database instance. The first log change includes a first operation entry associated with a first data operation on a first data record in the first database instance. The first data record includes a first synchronization tag field comprising a first source tag representing a first data source associated with the first data operation on the first data record. If the source tag contains the identification of the second database instance, the synchronization tool skips the first operation entry, without synchronizing the first data operation to the second database instance to avoid data loopback.

Processing systems and associated methods are provided for managing tasks and handling errors based on criticality levels assigned to individual processing circuitry blocks. The system includes a plurality of processing circuitry blocks, each assigned a level of criticality by a critical domain manager, such that errors arising in relation to these blocks are handled in accordance with their assigned level of criticality. The system features timers assigned to tasks initiated by the processing circuitry blocks, with timeout durations determined by the criticality levels respectively assigned to the processing circuitry blocks and tasks initiated by them. Upon timeout expiration without task completion, various error handling procedures are selected and executed based on the assigned criticality levels.

Systems and methods for real-time mapping and visualization generation of system components. The system may receive a first user request to generate a first visualization of a first configuration of a first subset of components in a first software system. The system may, in response to the first user request, retrieve a first software applications lineage log, wherein the first software applications lineage log comprises a log of event data of current processes being performed in the first software system. The system may generate a first feature input based on the first software applications lineage log. The system may input the first feature input into a first artificial intelligence model to generate a first output.

Forecasting industrial asset failures is described. A system determines a data start value associated with an industrial asset at a data start time. The system determines a data end value associated with the industrial asset at a data end time. The system estimates a failure time when a trend projected from the data start value through the data end value will reach a failure limit value. The system determines a distance to failure based on the failure limit value and the data end value. The system outputs a failure forecast, associated with the failure time and the distance to failure, for the industrial asset.

A system and method for assessing, broadcasting and remediating a health of a cloud computing infrastructure. A system includes a health assessment platform interfaced with components of the cloud computing infrastructure, and configured to perform an evaluation loop of the cloud computing infrastructure to produce a health assessment, the evaluation loop assessing one or more of a drift between desired and actual states, resource utilization, cost or spend controls, policy compliance, and planning or refactoring of the components of the cloud computing infrastructure. The health assessment platform is further configured to report the health assessment via notifications to a user interface associated with the cloud computing infrastructure. The system further includes a remediation engine that is interfaced with the user interface, the remediation engine configured to provide a user of the user interface with remediation recommendations and optimizations based on the reported health assessment.

According to one embodiment of the present invention, a computer-implemented method for dynamically altering a frequency at which data scrubbing is performed on a memory device is disclosed. The computer-implemented method includes monitoring at least one of a temperature and a magnetic field of the memory device. The computer-implemented method further includes, responsive to determining that at least one of the temperature and the magnetic field of the memory device reaches and / or exceeds a predetermined threshold, respectively, increasing the frequency at which data scrubbing is performed on the memory device.

A system and method for detecting code intent of code objects in a computing environment for assessing cybersecurity risk is presented. The method includes detecting a plurality of code objects in a computing environment; statically analyzing each code object of the plurality of code objects to determine a plurality of potential intents, each potential intent corresponding to an action; generating a cybersecurity signal in the computing environment based on a plurality of event records; detecting in the cybersecurity signal a first event corresponding to a potential intent; determining that the potential intent is a code intent based on the detection; detecting in the cybersecurity signal a second event which does not correspond to any potential intent; and initiating a remediation action in the computing environment based on the detected second event.

A computing device comprising at least one processing module carrying multiple execution units each with a local memory unit; and a local network of the processing module linking at least some of the execution units to each other by respective communication paths; wherein the execution units are configured to execute a binary code segment at least temporarily stored in their respective associated local memory unit and derived from a computer executable program code application containing application code sections; and wherein at least one of the execution units is designated for executing a binary code segment as an assigned binary code segment based on a pre-determined runtime schedule with fixed time slots during which the execution unit is granted exclusive access to at least one of the communication paths when executing the assigned binary code segment in line with a pre-defined running order of the application code sections.

Systems, methods, and other embodiments associated with automatic configuration of a circular buffer for ingesting a stream and generating ML estimates in real-time are described. In one embodiment, an example method includes loading a stream of multivariate time series observations into a circular buffer at a real-time pace of input from a target asset. The circular buffer is configured with a buffer configuration that specifies buffer length and choice of arrangement as a single-buffer or dual-buffer. The method then adjusts the buffer configuration until generation of machine learning estimates of the multivariate time series observations that are in the circular buffer satisfies a threshold test for generation at the real-time pace. And, at the real time pace, the method loads additional multivariate time series observations into the circular buffer that is in the adjusted configuration and generates additional machine learning estimates of the additional multivariate time series observations.

A radiation tolerant data storage apparatus and method. The radiation tolerant data storage apparatus and method can direct and store redundant copies of data in different physical locations to reduce the likelihood that a single radiation particle damages corresponding portions of the multiple copies of data. The radiation tolerant data storage apparatus and method includes a controller that arranges for multiple copies of data to be stored in a manner that reduces collinearity and detect if one copy has been damaged. The copies are preferably stored at physical locations that minimize or otherwise completely avoid the ability for a single radiation particle to damage or flip the same bit in each of the multiple copies of data. Most preferably, numerous other radiation hardening techniques can also be used to provide further radiation tolerance to the system and method.

Systems and methods for accident detection and handling are described. In some embodiments, an Information Handling System (IHS) may include an Embedded Controller (EC), and a memory coupled to, or integrated into, the EC, where the memory comprises program instructions that, upon execution by the EC, cause the EC to receive data from a sensor and, in response to the data being indicative of an accident, perform an action.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various checks and combinations of checks including installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.

A system and method for fail-operational mission continuity in a multi-agent autonomous system. Each autonomous agent includes an onboard Prognostic Health Management (PHM) module that monitors health using sensor data. Upon detecting an incipient fault, the PHM module calculates a prognostic Remaining Useful Life (RUL). This RUL is transformed into a quantitative Operational Risk Cost (Ω) and communicated to a multi-agent control system. The control system's dynamic task allocation algorithm uses the Ω values as key inputs in a multi-objective optimization process. This enables the system to proactively and autonomously re-allocate a task from a degrading agent to a healthy agent before a failure occurs. The degrading agent is simultaneously commanded to perform a safe contingency maneuver. This integration of real-time prognostics and multi-agent control creates a resilient, self-healing system capable of completing missions despite hardware degradation.