413results about "Reliability/availability analysis" patented technology

The invention relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be provided for interactively managing data processing system(s) failures in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

Some embodiments provide a method for a first data compute node (DCN) operating in a public datacenter. The method receives an encryption rule from a centralized network controller. The method determines that the network encryption rule requires encryption of packets between second and third DCNs operating in the public datacenter. The method requests a first key from a secure key storage. Upon receipt of the first key, the method uses the first key and additional parameters to generate second and third keys. The method distributes the second key to the second DCN and the third key to the third DCN in the public datacenter.

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.

The invention discloses a communication data verification method and device, electronic equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: after executing a write-in operation on a target terminal, immediately initiating a read-in operation, and through the design of writing first and reading second, immediately checking whether the written-in communication data is correctly received and processed after the data is sent, thereby improving the verification efficiency. According to the method and the device, the communication verification format is improved, and the write operation is seamlessly switched to the read operation under the condition of keeping the communication continuity, so that the reliability of data transmission is improved, and the response speed and the performance of the server are optimized. Therefore, the problems that a verification mechanism is incomplete during data transmission, and the reliability and accuracy of data transmission are poor in the prior art are solved.

The disclosure herein describes training and using an uncorrectable error (UE) state prediction model based on telemetry error data. Sets of UE state labels and non-UE state labels are generated from a first set of collected telemetry data, wherein the UE state labels each reference a UE and telemetry data of an interval prior to the referenced UE. Statistical features are extracted from telemetry data of the sets of UE state labels and non-UE state labels, and the extracted statistical features are used to train a UE state prediction model. A second set of collected telemetry data is obtained, and a UE event is predicted based on the second set of collected telemetry data using the trained UE state prediction model. A preventative operation is performed on a memory page of the system based on the predicted UE event, whereby the predicted UE event is prevented from occurring.

The invention discloses an intelligent fault prediction system for a solid state disk, relates to the technical field of solid state disks, solves the problems that only the test condition of a single chip can be identified, and the test effect of comprehensive radiation cannot be achieved, and is based on the number of threads corresponding to the solid state disk, and different processing processes are executed. The method comprises the following steps: processing the solid state disk in different combination states, confirming numerical value characteristics associated with different processing processes, analyzing reading speed fluctuation states of the solid state disk in different combination states according to the confirmed numerical value characteristics, carrying out multidirectional characteristic testing according to a specific analysis process, and identifying the use performance of the solid state disk. According to the multi-azimuth and multi-chip testing method, the multi-azimuth and multi-chip testing method and the multi-azimuth and multi-chip testing system, the characteristic data associated in the use performance is subjected to interval range confirmation, whether the fault hidden danger exists or not is confirmed according to the change amplitude of the corresponding interval range, the comprehensiveness in the fault hidden danger testing process can be effectively guaranteed by adopting the multi-azimuth and multi-chip testing mode, and a better testing processing effect is achieved.

Embodiments of the present disclosure provide a context based health visualization system and method for a computing environment that indicates context-based health information for the computing resources of a computing environment. According to one embodiment, an Information Handling System (IHS) includes computer-executable instructions to identify a maintenance task that needs to be performed on a computing resource, classify the maintenance task according to one of a plurality of health contexts, and generate, using information associated with the maintenance task, a health context score for the one health context. The maintenance task may be one that impacts an overall health score of the computing resource. The instructions may then cause the IHS to display the health context score for view by a user.

The invention relates to the technical field of server aging testing, in particular to a server aging testing method and device, electronic equipment and a storage medium. Then convolution processing is carried out on each aging data queue in a sliding mode through a difference operator, the obtained multiple first convolution queues are segmented one by one, queue segments obtained through segmentation are normalized, and multiple queue segments are obtained; performing dot product calculation on each queue segment, each same-time-segment queue segment and each same-part queue segment to obtain a plurality of dot product results, and adding representative values determined according to the plurality of dot product results into a test analysis matrix; and finally, according to the plurality of row vectors of the test analysis matrix, determining the abnormal probability of the plurality of tested parts of the server. According to the method, the state of the detected component is determined based on the cross point product and probability estimation, so that the problem component can be found early.

The present invention extends to methods, systems, and computer program products for predictively addressing hardware component failures. Network packets can be received over time at a platform. Metrics derived from platform hardware components and derived from one or more workloads utilizing the platform hardware components can be monitored. Model training data can be formulated from the metrics. A health check model can be trained using the model training data. The health check model can be executed to compute a probability that a monitored platform hardware component is on a path to failure. It can be determined that the probability exceeds a threshold. A workload can be relocated from a pod containing the monitored platform hardware component to another pod. Additional network packets can be received over time at the platform. The workload can process data contained in the additional network packets at the other pod.

After generating a first customized recovery plan to resolve a first error associated with a first error message generated by a first software application, a jobs manager obtains identities of an input system, an output system or a combination thereof associated with the first software application. In response to determining that a second software application is associated with the same or similar input system, the same or similar output system, or the combination thereof as the first software application, the jobs manager determines that the first error associated with the first software application is predicted to occur relating to the second software application. Thereafter, the jobs manager generates a second customized recovery plan for the second software application based at least in part upon the first customized recovery plan generated for the first software application.

In memory reads, an increase in a number of correctable errors increases a likelihood that an uncorrectable error will occur. Thus, tracking of correctable errors per memory page is one technique that can be used and if the number of errors on a memory page exceeds a threshold then the memory page can be replaced through dynamic memory re-mapping. However, having a counter per memory page to track the errors is costly due to the number of memory pages. Accordingly, a system is proposed wherein a group of counters is used that acts as a cache of counters that track memory pages of interest due to a variety of factors. Additionally, an eviction policy can control which memory pages are tracked so that only the most interesting candidate pages can be tracked in accordance with a policy.

Systems and methods for detailed cloud posture remediation recommendations utilizing custom Large Language Models (LLMs). The present systems and methods are configured to perform the steps of scanning a cloud environment for posture control data; generating one or more alerts related to any of risky configurations and risky activities associated with the cloud environment; generating one or more remediation recommendations based on the one or more alerts; and providing the one or more alerts and the one or more remediation recommendations to administrators of the cloud environment.

The embodiment of the invention provides a fan service life prediction method and system, a fan, a storage medium and a program product. The fan service life prediction method comprises the steps that the environment temperature of the fan is obtained; if the environment temperature of the fan meets the first condition, the mechanical factor service life and the environment factor service life of the fan are calculated; the first condition is that the environment temperature is smaller than or equal to first preset temperature or larger than or equal to second preset temperature, and the first preset temperature is smaller than the second preset temperature; and outputting the residual life of the fan after fusing the mechanical factor life and the environmental factor life. By fusing the mechanical factor life and the environmental factor life of the fan, the prediction precision of the fan life is improved, the cost does not need to be increased, and the residual life of the fan can be output in real time; and secondly, by monitoring the residual service life of the fan, the effects of good thermal management performance of the equipment and prolonging of the service life of the equipment can be achieved.

The techniques described herein provide a risk assessment framework that enhances the functionality of software deployment systems in cloud-based platforms. Generally described, the present techniques evaluate and consolidate various risk scores to classify a given computing cluster within a software deployment strategy. In various examples, a deployment system collects node-level feature data from the computing cluster to generate a dataset to train a prediction model to calculate constituent risk scores. In another aspect, the deployment system aggregates constituent risk scores to determine an overall risk of software failure. Likewise, the deployment system considers diverse criteria such as virtual machine size and virtual machine density to determine an overall impact of software deployment failure. The deployment system then calculates a composite risk score for the computing cluster as a function of the risk of software deployment failure and the impact of software deployment failure.

Methods, systems, and products for sustainable Reliability, Availability, and Serviceability (RAS) balancing based on predictive models includes predicting, based on one or more predictive models, a potential failure within a computing system, and provisioning, based on the potential failure, one or more redundant resources for failover.

The invention relates to the technical field of data storage, and discloses a solid state disk compatibility and reliability testing method which comprises the steps of initializing a testing environment, installing an operating system, compiling and executing a testing script and configuring a compiling environment. Basic information of the solid state disk is recognized and classified, an automatic script is written, the classified basic information of the solid state disk is created into file clusters of different sizes according to the automatic script, continuous / random reading and writing are conducted, and file integrity is verified; and performing performance test on the classified basic information of the solid state disk by using a Fio tool and recording performance indexes of the solid state disk, wherein the performance indexes comprise sequential read-write speed, random IOPS and response delay, compiling an automatic script, and simulating a database load to perform small block random read-write operation. According to the invention, a plurality of modules for basic function and compatibility test, performance test, power management and reliability test, safety characteristic test and the like are integrated into a complete and automatic test scheme.

The invention discloses a PCBA aging test method and system, and belongs to the technical field of aging test. The method specifically comprises the following steps: S1, historical data acquisition: acquiring PCBA aging related historical data, including but not limited to voltage parameters, current parameters, core area temperature, operation duration, environment humidity, fault types and fault occurrence time during PCBA operation; s2, preprocessing the historical data: preprocessing the historical data, including removing abnormal values, filling missing values and normalizing; 24-72-hour long-time power-on loading of a traditional aging test is not needed, the test period of a single PCBA is shortened to the minute level, the production takt is greatly improved, the large-scale batch production requirement is met, and aging fault recognition is more comprehensive; and recessive aging precursors such as capacitance attenuation and welding spot microcracks can be captured, and the sudden failure risk after the product leaves the factory is reduced.

The invention relates to the technical field of computer system performance evaluation and fault prediction, and particularly discloses an internet-based performance prediction system for software development. The system comprises a data acquisition module, a feature extraction module, a performance prediction model module, a feedback optimization module and a visual analysis module. The system constructs a high-dimensional feature vector set fusing code complexity, a calling relation, an abnormal mode and the like by collecting source codes, logs and defect data, and outputs response time, a resource occupancy rate and a reliability index based on a multi-target prediction model. And further optimizing the model performance through an error feedback mechanism, and displaying a prediction result and a risk score in a graphic mode. The system supports cloud deployment, has cross-project sharing capability and data security guarantee, and is suitable for large software project performance bottleneck diagnosis and risk early warning.

A method comprises collecting operational data from a plurality of devices, predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data, and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices.

Methods, systems, and products for health signature based load shifting for part longevity may include monitoring one or more variables associated with each of multiple redundant parts of a computing system, where one or more of the redundant parts are experiencing a load, generating, based on the monitored variables, a health signature for each of the redundant parts, and shifting, based on the health signatures, the load between the redundant parts.

Disclosed are a service lifetime monitoring and early warning method, a memory storage device, and a memory control circuit unit. The method includes: reading a history information from a rewritable non-volatile memory module, calculating a remaining lifetime based on the history information and a user habit, generating an early warning signal, and outputting the remaining lifetime and the early warning signal in response to the remaining lifetime being lower than a preset lifetime.

An integrated circuit formed with interconnected electronic elements includes at least:i) functional elements which perform IC operations;ii) at least one countermeasure (CM) for detecting fault injection attacks upon the IC and outputting an alert signal when a FI attack is detected; andiii) at least one attack emulator which emulates FI attacks on the IC by applying stimuli to the plurality of functional elements in accordance with at least one control signal.The IC may include other elements such as power supply elements, oscillators, interface elements and so forth.

The invention provides a data storage reliability testing method and system based on a solid state disk. The method comprises the following steps: executing a data storage reliability test on a target solid state disk, and extracting reliability index data; processing according to the reliability index data to obtain a reliability standard degree coefficient; acquiring environment characteristic data of the test region, and processing to obtain an environment interference coefficient; processing according to the reliability standard degree coefficient in combination with an environment interference coefficient to obtain a corrected reliability standard degree coefficient, and judging whether the data storage reliability reaches the standard or not; and if the data storage reliability of the target solid state disk does not reach the standard, executing an optimization measure on the target solid state disk, thereby realizing a data storage reliability test technology based on the solid state disk.

The techniques described herein reduce the noise when making a health determination for an entity executing within, or supported by, a distributed computing environment. The system is configured to receive health data corresponding to resources upon which the entity depends. The health data can include an indication of whether an individual resource is healthy or unhealthy (e.g., based on values collected for metrics). The system uses hysteresis to reduce the noise when making a health determination for the entity. That is, the system calculates a historic center value (e.g., a historic average value) and a spread value (e.g., standard deviation) for the history center value, and uses the spread value to establish different thresholds for health state transitions.

An apparatus comprises a processing device configured to determine specifications for an information technology asset to be developed, and to identify, utilizing at least one machine learning model, whether at least one of the specifications for the information technology asset is defect-prone, wherein a given specification is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset. The processing device is also configured to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset, and to modify one or more development processes for the information technology asset based at least in part on the established mapping.

A method for fault tolerant implementation of a mapping of at least one input value to at least one output value by a processing assembly with a plurality of processing units is disclosed. The mapping includes a plurality of structures with computational operations which can be evaluated by one or more of the processing units. Each of the structures is associated with criticality information that includes information how critical the error-free evaluation of the structure is for the error-free evaluation of the mapping. If at least one processing unit has a malfunction, at least one structure is selected based on the criticality information of the structures. It is determined whether the at least one structure should be evaluated in a modified manner or not evaluated. The mapping is applied to input values, wherein the selected at least one structure is evaluated in a modified manner or is not evaluated.

A computer-implemented method for flagging risk when handling updates to a computing environment is disclosed. The method includes receiving a change request comprising one or more change features associated with a change in hardware and / or software within the computing environment, and receiving a change record associated with the change request, the change record comprising one or more fields corresponding to the one or more change features. The method further includes calculating a risk score for the change by applying a trained predictive model to the one or more fields, the predictive model having been trained on a dataset comprising historical changes to the computing environment and their associated historical change records. If the calculated risk score is above a predetermined threshold, a flag is raised to the change request to notify a user.