Abstract: Techniques for performing cache operations are provided. The techniques include tracking performance events for a plurality of test sets of a cache, detecting a replacement policy change trigger event associated with a test set of the plurality of test sets, and in response to the replacement policy change trigger event, operating non-test sets of the cache according to a replacement policy associated with the test set.

Abstract: A vehicular device includes a control unit, a non-volatile first storage unit, a second storage unit, a notification unit, and a backup unit. Multiple operating systems operate in the control unit. The backup unit is provided in each operating system and is configured to back up, on the first storage unit, data temporarily stored in the second storage unit. The backup unit is configured to back up the data on the first storage unit upon being notified of the change in the power supply state by the notification unit. The backup unit running on one operating system is configured to pass, to the backup unit provided in another operating system, target data or information identifying the target data. When a failure occurred in the one operating system, the backup unit provided in the other operating system backs up the target data or the information identifying the target data.

Abstract: During an execution of a first executable entity, a computer system determines that a target chunk of executable instructions is to be recorded during the execution. Prior to executing the target chunk of executable instructions, the computer system identifies one or more input(s) of the target chunk of executable instructions, and adjusts a page table entry to cause a page fault based on an access by a second executable entity to a first memory page that corresponds to an identified input, and/or an access by the first executable entity to a second memory page that does not correspond to any identified input. Based on detecting an occurrence of a page fault during an execution of the target chunk of executable instructions, the computer system detects that there an interference with an identified input by the second executable entity and/or an incompleteness of the identified inputs.

Abstract: Systems and methods are disclosed for monitoring features of a computing device of a distributed computing system using a self-monitoring module. The self-monitoring module can include multiple feature-specific monitoring modules and one or more parent nodes for the feature-specific monitoring modules. A feature-specific monitoring module can identify or detect a fault status change, such as a fault condition or fault resolution, for one or more features. Based on the identified fault conditions or fault resolutions, the feature-specific monitoring module can determine an internal status and communicate an updated status to a parent node.

Abstract: In various embodiments, a technique can be provided to address debug efficiency for failures found on an operational system. The approach can make use of an existing pin on a memory device with added logic to respond to a trigger signal structured different from a signal that is normally sent to the existing pin on the memory device such that the memory device performs a normal or routine function of the memory device in response to the signal. In response to detecting one or more error conditions associated with the memory device, a system that interfaces with the memory device can generate the trigger signal to the memory device. In response to receiving the trigger signal, the memory device can dump an error log of the memory device to a memory component in the memory device. The error log can later be retrieved from the memory component for failure analysis.

Abstract: A system is described herein for mitigating slow process instances in a streaming application. The system includes a slow process instance candidate identifier configured to identify, based on a relative watermark latency, a set of slow process instance candidates from among a plurality of process instances that comprise the streaming application. The system further includes a set of filters configured to remove false positives from the set of slow process instance candidates. The filters account for window operations performed by the process instances as well as stabilization time needed for downstream process instances to stabilize after a slow upstream process instance is mitigated by a mitigation implementer, which may also be included in the system.

Abstract: In an example embodiment, a solution is provided that detects performance degradation of a particular functionality or an overall system problem using machine learning, Mann-Kendall tests, and correlation tests. After a problem has been automatically detected, the first steps of a root cause analysis may be automatically performed, indicating whether, for example non-optimal ABAP coding, a database issue, or hardware or software bottleneck, or some combination thereof. This approach allows a system to rapidly identify a system performance problem and its root cause by combing several data sources. One can see immediately, for example, whether an ABAP code change, an expensive SQL statement, or the combination of both led to an increase in the average response times of a transaction over time.

Abstract: Artificial intelligence (AI) model monitoring and ranking includes obtaining metric values indicative of performance of AI model deployments, the metric values including respective metric values measured across metrics, determining violation statuses of the metrics for each of the AI model deployments, the violation statuses indicating, for each AI model deployment, which of the metrics are violated by the AI model deployment as reflected by respective metric values for that AI model deployment, ranking the AI model deployments against each other according to a ranking model and based on the determined violation statuses for each of the AI model deployments, and providing a rank of at least some of the AI model deployments to a user.

Abstract: Described herein is a system and method for determining whether a detected issue in a computing system is a bug introduced by a developer or an intermittent issue. When an issue is detected, information about the issue is received. A determination is then made as to whether the issue is a new issue or whether it has been previously detected. When it is determined that the issue is a new issue, information about the issue is stored in a storage device. A feature vector is generated for the issue and is analyzed to determine a probability that the issue is a bug. When it is determined that the issue is a bug, the occurrence of the issue is reported to a user of the system that can correct the bug. Once the bug is corrected, the correction is provided back to the system and is used to train the system.

Abstract: Telematics-based device troubleshooting, self-repair, and optimization are provided. A system, method, and computer readable storage device collect telemetry data associated with the health of networked CPE devices, service provider data, and service and repair data associated with CPE devices, analyze the collected information, and detect and predict issues that can affect the functioning and operation of CPE devices. Analysis of collected data can be based on self-check rules. In some implementations, troubleshooting steps or rules can be executed by a server device or communicated to a CPE device for enabling self-troubleshooting and self-correction of an issue. Self-check and troubleshooting rules can be configured according to insights derived from machine learning techniques applied to collected data (e.g.

Abstract: A storage device, and a method for operating a storage device. In some embodiments, the storage device includes storage media, and the method includes: determining, by the storage device, that the storage device is in a first fault state from which recovery is possible by power cycling the storage device or by formatting the storage media; determining, by the storage device, that the storage device is in a second fault state from which partial recovery is possible by operating the storage device with reduced performance, with reduced capacity, or in a read-only mode; and operating the storage device with reduced performance, with reduced capacity, or in the read-only mode.

Abstract: A system to implement debugging for a multi-threaded processor is provided. The system includes a hardware thread scheduler configured to schedule processing of data, and a plurality of schedulers, each configured to schedule a given pipeline for processing instructions. The system further includes a debug control configured to control at least one of the plurality of schedulers to halt, step, or resume the given pipeline of the at least one of the plurality of schedulers for the data to enable debugging thereof. The system further includes a plurality of hardware accelerators configured to implement a series of tasks in accordance with a schedule provided by a respective scheduler in accordance with a command from the debug control. Each of the plurality of hardware accelerators is coupled to at least one of the plurality of schedulers to execute the instructions for the given pipeline and to a shared memory.

Abstract: The present disclosure discloses a client device having a digital signature. The client device includes processing circuitry configured to send a message to be stored in nodes after the nodes reach a consensus on the message. The message includes the digital signature of the client device. The processing circuitry obtains results from a subset of the nodes that receive the message. The results have respective digital signatures of the subset of the nodes. The nodes are in a first consensus mode for reaching the consensus on the message. After verifying the digital signatures, the processing circuitry determines whether one or more of the nodes has malfunctioned based on the results. Based on a determination that the one or more of the nodes has malfunctioned, the processing circuitry triggers the nodes to switch from the first consensus mode to a second consensus mode for reaching the consensus on the message.

Abstract: The system determines data which indicate memory usage by a process and information related to a number of instances of the process. The system determines for the process: a first average amount of memory; a first standard deviation; a first value indicating a difference between a current amount of memory used by the process and the first average amount; a second average number of process instances; a second standard deviation; and a second value indicating a difference between a current number of process instances and the second average number. The system performs a corrective action to address a memory spike associated with the process responsive to comparing at least one of: the current amount of memory used by the process with a watermark; the first value with the first standard deviation scaled; the current number of instances with a watermark; and the second value with the second standard deviation scaled.

Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.

Abstract: Described herein is a system and method for determining whether a detected issue in a computing system is a bug introduced by a developer or an intermittent issue. When an issue is detected, information about the issue is received. A determination is then made as to whether the issue is a new issue or whether it has been previously detected. When it is determined that the issue is a new issue, information about the issue is stored in a storage device. A feature vector is generated for the issue and is analyzed to determine a probability that the issue is a bug. When it is determined that the issue is a bug, the occurrence of the issue is reported to a user of the system that can correct the bug. Once the bug is corrected, the correction is provided back to the system and is used to train the system.

Abstract: The present disclosure is for systems and methods for connecting offline machine learning training systems with online near-real time machine learning scoring systems. It is not trivial to connect an offline training environment with an online scoring environment. For example, offline training environments are usually static and contain large amounts of historical data that is needed for the initial training of models. Once trained, the model algorithms are then migrated into an online scoring environment for transactional or event based scoring. This migration effectively breaks the connection between the data in the offline environment and the model now running in the online environment. When new or shifting data occurs in the online environment, the static model running in the online environment goes unaltered to the changing inputs. The present disclosure solves the issues that are caused by the break in the offline and online environments.

Abstract: Systems and methods for updating software in a hazard detection system are described herein. Software updates may be received by, stored within, and executed by a hazard detection system, without disturbing the system's ability to monitor for alarm events and sound an alarm in response to a monitored hazard event. The software updates may be received as part of a periodic over-the-air communication with a remote server or as part of a physical connection with a data source such as a computer. The software updates may include several portions of code designed to operate with different processors and/or devices within the hazard detection system. The software updates may also include language specific audio files that can be accessed by the hazard detection system to play back language specific media files via a speaker.

Abstract: A computer system determines stack usage. An intercept function is executed to store a stack marker in a stack, wherein the intercept function is invoked when a program enters or exits each function of a plurality of functions of the program. A plurality of stack markers are identified in the stack and a memory address is determined for each stack marker during execution of the program to obtain a plurality of memory addresses. The plurality of memory addresses are analyzed to identify a particular memory address associated with a greatest stack depth. A stack usage of the program is determined based on the greatest stack depth. Embodiments of the present invention further include a method and program product for determining stack usage in substantially the same manner described above.

Abstract: Systems and methods are described for optimizing logging of decision outcomes in distributed transaction protocols. An example method may comprise: executing, by a processing device, a transaction manager to coordinate a distributed transaction for a plurality of participants; transmitting, by the processing device via the transaction manager, prepare messages to the plurality of participants; serializing a transaction log record of the distributed transaction in parallel with the transmitting of the prepare messages; speculatively writing the serialized transaction log record in persistent memory while the transaction manager performs the serializing of the transaction log record; and updating a speculative indicator associated with the distributed transaction to indicate validity of the transaction log record.