Abstract: Provided is a memory-based reinforcement learning method and system capable of storing optional information in streaming data. A question-answering (QA) method using memory-based reinforcement learning method includes receiving, in an episodic memory reader (EMR), streaming data about an input context that is input from a user; analyzing, in the EMR, the received streaming data and storing preset semantic information used for QA in an external memory; and, in response to an input of a question front the user, determining, in a pretrained QA model, an answer to the input question based on semantic information stored in the external memory.

Abstract: As described herein, a system, method, and computer program are provided for using metric valuations in anomaly scoring. In use, a score calculated for a metric used for anomaly detection is identified. Additionally, the score is weighted, based on a valuation defined for the metric, to form a weighted score. Further, at least one action is caused to be performed, based on the weighted score.

Abstract: A storage device including, a plurality of non-volatile memories configured to include a memory cell region including at least one first metal pad; and a peripheral circuit region including at least one second metal pad and vertically connected to the memory cell region by the at least one first metal pad and the at least one second metal pad, and a controller connected to the plurality of non-volatile memories through a plurality of channels and configured to control the plurality of non-volatile memories, wherein the controller selects one of a first read operation mode and a second read operation mode and transfers a read command corresponding to the selected read operation mode to the plurality of non-volatile memories, wherein one sensing operation is performed to identify one program state among program sates in the first read operation mode, and wherein at least two sensing operations are performed to identify the one program state among the program states in the second read operation mode.

Abstract: Embodiments of systems and methods for exothermic event prediction engine are described. In an embodiment, an Information Handling System (IHS) may include: a processor, a Remote Access Controller (RAC) coupled to the processor, and a memory coupled to the RAC, the memory having program instructions stored thereon that, upon execution by the RAC, cause the RAC to collect telemetry data from the IHS and predict an exothermic failure in the IHS based, at least in part, upon the telemetry data.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to identify anomalous applications. In some implementations, the methods may include obtaining application metric data that includes application level metrics and instance level metrics for each instance of a plurality of instances associated with a respective application of a plurality of applications operating over a distributed computing system, generating a first anomaly detection score based on the instance level metrics; generating a second anomaly detection score based on one or more input metrics associated with the respective application, generating a third anomaly detection score based on seasonal metric data associated with the respective application and identifying at least one application of the plurality of applications as an anomalous application based on the first anomaly detection score, the second anomaly detection score, and the third anomaly detection score.

Abstract: Various embodiments provide an electronic device and method for determining the lifespan and failure of a nonvolatile memory storage device. The electronic device and the method according to various embodiments may be configured to determine whether the storage device has failed in response to a connection with the storage device, output a guidance message for replacing the storage device when the storage device fails, confirm whether the storage device has a function of autonomously identifying its state when the storage device is normal, check the lifespan of the storage device based on state information received from the storage device when the storage device has the function, estimate the lifespan of the storage device when the storage device does not have the function, and output the guidance message for replacing the storage device based on the lifespan.

Abstract: A computerized method is disclosed for generating a prioritized listing of alerts based on scoring by a machine learning model and retraining the model based on user feedback. Operations of the method include receiving a plurality of alerts, generating a score for each of the plurality of alerts through evaluation of each of the plurality of alerts by a machine learning model, generating a prioritized listing of the plurality of alerts based on the generated scores, receiving user feedback on the prioritized listing, retraining the machine learning model based on the user feedback by generating a set of labeled alert pairs, wherein a labeled alert pair includes a first alert, a second alert, and an indication as to which of the first alert or the second alert is a higher priority in accordance with the user feedback, and evaluating subsequently received alerts with the retrained machine learning model.

Abstract: In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning-based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.

Abstract: Various implementations described herein relate to systems and methods for predicting and managing drive hazards for Solid State Drive (SSD) devices in a data center, including receiving telemetry data corresponding to SSDs, determining future hazard of one of those SSDs based on an a-priori model or machine learning, and causing migration of data from that SSD to another SSD.

Abstract: Systems and methods are disclosed to implement a time series anomaly detection system that uses configurable statistical control rules (SCRs) and a forecasting system to detect anomalies in a time series data (e.g. fluctuating values of a network activity metric). In embodiments, the system forecasts future values of the time series data along with a confidence interval based on seasonality characteristics of the data. The time series data is monitored for anomalies by comparing actual observed values in the time series with the predicted values and confidence intervals, according to the SCRs. The SCRs may be defined and tuned via a configuration interface that allows users to visually see how different SCRs perform over real data. Advantageously, the disclosed system allows users to create custom anomaly detection triggers for different types of time series data, without use of a monolithic detection model which can be difficult to tune.

Abstract: A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops.

Abstract: Reusable information is extracted from a result of executing a simulation of a plurality of models. A simulation management method for managing data for use in a simulation by a management device having a processor and a memory, the method including: executing a simulation using a model and data, by a simulator; extracting intermediate data indicating that an internal state of the model that is being simulated matches a predetermined state, by the management device; and holding the extracted intermediate data as useful intermediate data, by the management device.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: A system and method for training and executing an adaptive bitrate (ABR) controller can include clustering content into content clusters based on content metadata, clustering network scenarios based on network information, normalizing input measurements, training the ABR controller for each content within at least one of a given content cluster and a given network scenario cluster by determining network information and network metadata, associated with the respective content, determining a content bitrate, determining a reward associated with the content bitrate, and training the ABR controller based on the reward.

Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device determines that no backup tunnel for the first tunnel exists in the SD-WAN that can satisfy one or more service level agreements (SLAs) of traffic on the first tunnel, were the traffic rerouted from the first tunnel onto that tunnel. The device predicts, using a machine learning model, that a backup tunnel for the first tunnel exists in the SD-WAN that can satisfy an SLA of a subset of the traffic on the first tunnel, in response to determining that no backup tunnel exists in the SD-WAN that can satisfy the one or more SLAs of the traffic on the first tunnel. The device proactively reroutes the subset of the traffic on the first tunnel onto the backup tunnel, in advance of the predicted failure of the first tunnel.

Abstract: In one embodiment, a supervisory service for a network obtains a first set of user experience survey data from users of an online application whose traffic was rerouted by a predictive routing engine. The supervisory service obtains a second set of user experience survey data from users of the online application whose traffic was not rerouted by the predictive routing engine. The supervisory service generates, using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application. The supervisory service adjusts the predictive routing engine using predicted user experience scores from the user experience prediction model.

Abstract: A solid state circuit breaker assembly includes a transistor, a transient voltage suppression device, and a circuit board. The transistor and/or the transient voltage suppression device may be electrically connected to the circuit board. The solid state circuit breaker module may be configured to be connected to one or more non-scalable modules to regulate current. The solid state circuit breaker module may be configured to receive one or more scalable modules. The transistor and/or the transient voltage suppression device may be disposed on the circuit board in a substantially symmetrical configuration.

Abstract: A device implementing a system to render user interface timeline views for display of dynamic application content includes a processor configured to retrieve a data structure corresponding to user interfaces of an application associated with respective times, and at least one declaratively defined user interface element. The processor is further configured to determine whether a rendering cost of a plurality of the user interfaces complies with an update budget of the application, where the rendering cost includes interpreting the at least one declaratively defined user interface element for the respective times. When the rendering cost is determined to comply, the processor is further configured to render the plurality of the user interfaces in advance of the respective times associated with the plurality of the user interfaces. The processor is further configured to display at least one of the rendered plurality of the user interfaces based on a current time.

Abstract: This disclosure describes systems, devices, and techniques for detecting and diagnosing anomalies in utilization levels of network-based resources. In an example method, at least one utilization level of one or more computer resources by an account during a time interval may be received. A possible anomaly can be detected if the at least one utilization level is determined to be outside of a predetermined range. The anomaly can be confirmed determining that at least a threshold number of multiple discrimination layers identify an anomaly in the at least one utilization level. An action can be performed in response to confirming the anomaly.

Abstract: Technologies are disclosed herein for cross-correlating metrics for anomaly root cause detection. Primary and secondary metrics associated with an anomaly are cross-correlated by first using the derivative of an interpolant of data points of the primary metric to identify a time window for analysis. Impact scores for the secondary metrics can be then be generated by computing the standard deviation of a derivative of data points of the secondary metrics during the identified time window. The impact scores can be utilized to collect data relating to the secondary metrics most likely to have caused the anomaly. Remedial action can then be taken based upon the collected data in order to address the root cause of the anomaly.

Abstract: Computer system security is often implemented using rules-based systems (e.g., allow traffic to this network port, deny it for those network ports; user A is allowed access to these files, but not those files). In enterprises, multiple such systems may be deployed, but fail to be able to intelligently handle anomalies that may technically be permissible but in reality represents a high possibility that there is an underlying threat or problem. The present disclosure describes the ability to build adaptive models using machine learning techniques that integrate data from multiple different domains (e.g. user identity domain, system device domain) and allow for automated decision making and mitigation actions that can provide greater effectiveness than previous systems allowed.

Abstract: Methods and systems for evaluating metrics (e.g., quality of service metrics) corresponding to a monitored computer, detecting metric anomalies, and issuing alerts, are disclosed. A metrics collecting agent, operating on a monitored computer, collects metrics corresponding to the monitored computer and/or one or more monitored services. These metrics are transmitted to a monitoring server that dynamically determines metric thresholds corresponding to normal metrics and anomalous metrics. Using these metric thresholds, along with a machine learning model, the monitoring server can determine whether one or more metrics are anomalous, automatically issue alerts to security and operations teams, and/or transmit a control instruction to the monitored computer in order to fix the issue causing the anomalous metrics.

Abstract: Embodiments provide for prediction and mitigation of network faults. Information associated network nodes may be compiled and used to generate network analytical records (NARs). A first model may be executed against the NARs to predict faults associated with one or more nodes of the network. Tickets are generated for predicted faults and stored in a ticket database. The tickets may be analyzed to predict executable actions to mitigate the faults associated with each ticket. To analyze the tickets, ticket data may be compiled and used to generate ticket analytical records (TARs). A second model may be executed against the TARs predict actions to resolve the predicted faults. The predicted actions may be executed to mitigate the impact that the faults have on the network, which may include preventing the faults entirely (e.g., via preventative maintenance) or minimizing the impact of the faults via use of the predicted actions.

Abstract: Systems and methods for zero-footprint email and browser lifecycle, dependency, and configuration management are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for implementing zero-footprint email and browser lifecycle management may include: (1) launching a restricted sandbox with no dependencies installed; (2) executing an email client or browser session with no dependencies in the restricted sandbox; (3) receiving a request for a dependency from the email client or browser; (4) halting initialization of the request; (5) validating the request; (6) retrieving an approved configuration for the dependency; (7) resuming initialization of the request; and (8) destroying contents of the restricted sandbox when the email client or browser session is complete.

Abstract: A method of facilitating access to a promotional offer, the method comprising: receiving at a server system a page request from a client device; and sending program code executable in a browser application to the client device in response to the page request, the program code being executable to display at least one promotional offer and a promotional code, to provide a flash object at a display position of the promotional code and, in response to selection of the flash object, to cause the promotional code to be copied to a user-accessible memory of the client device and to open a new browser display of the browser application.

Abstract: Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Abstract: Systems, devices, media, and methods are presented for determining a level of abusive network behavior suspicion for groups of entities and for identifying suspicious entity groups. A suspiciousness metric is developed and used to evaluate a multi-view graph across multiple views where entities are associated with nodes of the graph and attributes of the entities are associated with levels of the graph.

Abstract: A security client can be configured to operate on the one or more computing systems and record all events occurring on the one or more computing systems. The security client can operate as a “security camera” for the computing systems by identifying and retaining data and information that describes and details different events that occur on the computing systems. The security client can be configured to generate event records for the events that are uniquely associated with the process that requested or performed event. Likewise, the security client can be configured to uniquely associate the event records with the specific computing system associated with the event.

Abstract: Novel tools and techniques are provided for implementing data and source validation for equipment output data and/or for equipment failure predict. In various embodiments, in response to receiving a first request for first data that is output by first equipment, a computing system might retrieve and analyze the first data to determine whether the first data can be trusted. If so, the computing system might send the first data to the requesting device. If not, the computing system might send a second request for identifying a blockchain containing a block containing a copy of the first data. In response to the blockchain system identifying such a blockchain, the computing system might receive the identified blockchain; might abstract the block containing the copy of the first data from the identified blockchain; might abstract the first data from the block; and might send the first data to the requesting device.

Abstract: A system and computer-implemented method for managing service request is provided. The system comprises a collaboration engine configured to receive one or more service requests from one or more service management tools. The system further comprises a classification engine configured to identify a configuration item and problem type corresponding to each of the one or more received service requests. Furthermore, the system comprises a policy engine configured to determine one or more pre-stored rules associated with the identified configuration item and problem type. The system also comprises a recommendation engine configured to recommend at least one of: one or more diagnostic actions and one or more resolution actions corresponding to the one or more determined pre-stored rules. In addition, the system comprises an automation engine configured to perform at least one of: the one or more recommended diagnostic actions and the one or more recommended resolution actions.

Abstract: A service management platform can implement functionality for one or more services, each of which can be independently used by a plurality of clients of the services. To activate the functionality of the one or more of the services, a hub server of the service management platform can assign a set of tasks to individual node servers for execution. The hub server can operate in a “supervisor environment” distinct from the processing environment used to execute the computationally intensive portions of the tasks. A task received at a node server can be managed by a supervisor process within the supervisor environment and executed by a native process within a native operating system environment, where the native process executes the computationally intensive calculations of the task and supervisor process provides communications and data transfer between the native process and rest of the service management platform.

Abstract: Read error mitigation in solid-state memory devices. A solid-state drive (SSD) includes a read error mitigation module that monitors one or more memory regions. In response to detecting uncorrectable read errors, memory regions of the memory device may be identified and preemptively retired. Example approaches include identifying a memory region as being suspect such that upon repeated read failures within the memory region, the memory region is retired. Moreover, memory regions may be compared to peer memory regions to determine when to retire a memory region. The read error mitigation module may trigger a test procedure on a memory region to detect the susceptibility of a memory region to read error failures. By detecting read error failures and retirement of a memory regions, data loss and/or data recovery processes may be limited to improve drive performance and reliability.

Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device makes a prediction as to whether a second tunnel in the SD-WAN will satisfy a service level agreement (SLA) associated with traffic on the first tunnel. The device proactively reroutes the traffic from the first tunnel onto the second tunnel, based on the prediction as to whether that the second tunnel will satisfy the SLA of the traffic. The device monitors one or more quality of service (QoS) metrics for the rerouted traffic, to ensure that the second tunnel satisfies the SLA of the traffic.

Abstract: A method, apparatus, and system for reducing pipeline stalls due to address translation misses is presented. An apparatus comprises a memory access instruction pipeline, a translation lookaside buffer coupled to the memory access instruction pipeline, and a TLB miss queue coupled to both the TLB and the memory access instruction pipeline. The TLB miss queue is configured to selectively store a first memory access instruction that has been removed from the memory access instruction pipeline as a result of the first memory access instruction missing in the TLB along with information associated with the first memory access instruction. The TLB miss queue is further configured to reintroduce the first memory access instruction to the memory access instruction pipeline associated with a return of an address translation related to the first memory access instruction.

Abstract: Applying machine-learning to optimize the operational efficiency of data backup systems is described. A machine-learning system creates a training set of multiple features for each of multiple historical data backup jobs. The machine-learning system trains a prediction model to predict, based on the training set, the probabilities that the corresponding historical data backup jobs failed during the next historical data backup window. The machine-learning system creates an operational set of multiple features for each of multiple scheduled data backup jobs. The trained prediction model predicts, based on the operational set, the probabilities that the corresponding scheduled data backup jobs will fail during the next scheduled data backup window. The predicted probability that a scheduled data backup job will fail during the next scheduled data backup window is output, thereby enabling an operator to remediate the scheduled data backup job prior to the next scheduled data backup window.

Abstract: In one embodiment, a traffic analysis service obtains traffic characteristics of network traffic associated with a device in a network. The traffic analysis service uses a machine learning model to infer resource usage by the device based on the obtained traffic characteristics of the network traffic associated with the device. The traffic analysis service controls traffic flows in the network based on the inferred resource usage by the device.

Abstract: In some embodiments, there may be provided a system. The system may be configured to receive web server data indicating a current state of a web server; determine, by a machine learning model and based on the web server data, whether the web server is in a first failure state; generate a first failure state indication for the web server in response to the determination, by the machine learning model, that the web server is in the first failure state; determine, by a forecaster and based on the web server data, whether the web server is in a second failure state; and generate a second failure state indication for the web server in response to the determination, by the forecaster, that the web server is in the second failure state.

Abstract: A computing resource monitoring service obtains access to aggregated metrics data from computing resources of a computing resource service provider. The computing resource monitoring service may then generate a predictive model based at least in part on the aggregated metrics. The predictive model may then be used to generate a prediction associated with the computing resource and, based at least in part on the prediction, one or more alarms may be triggered. The alarm may be triggered based at least in part on a confidence interval determined based at least in part on the prediction.

Abstract: Methods, systems, and apparatus, including computer-readable storage devices, for predictive models for electric power grid maintenance. In some implementations, one or more computers receive data indicating a maintenance action to be performed for a utility system. The one or more computers generate outcome scores for the maintenance action using a model generated from records for maintenance performed by multiple utilities. The one or more computers select one or more maintenance plan elements based on the outcome scores, provide an indication of the selected one or more maintenance plan elements for display, and store a record corresponding to the maintenance action. In some examples, the selected one or more maintenance plan elements can include recommended plan elements to improve the safety and efficiency of the maintenance action.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: A storage form searching device includes a calculator configured to calculate a failure rate of each physical link constituting a physical network based on information representing a disaster-affected area; and a searcher configured to search for a storage form for one or more stored networks stored in a logical network configured by using the physical network, wherein the storage form makes a cut-off rate calculated based on the failure rate of said each physical link smaller, so as to improve controllability of the stored network with respect to a disaster.

Abstract: A method for determining behavior information corresponding to a dangerous file in a computer device includes running the dangerous file in a virtual environment of the computer device when detecting existence of the dangerous file, wherein the virtual environment comprises at least one virtual API identical to at least one real API in a real environment of the computer device; monitoring behavior(s) of the dangerous file in the virtual environment to obtain the behavior information corresponding to the dangerous file. According to the solution of the present disclosure, it does not need to analyze disruptive behaviors of a dangerous file manually, the behavior information of the dangerous file can be quickly obtained in a virtual environment, thereby quickly and comprehensively repair the real system of the computer device.

Abstract: According to certain aspects, a method of assessing users of a data storage system can include receiving configuration information relating to a deployed configuration of a first data storage cell and including number of installed data storage hardware and/or software components, the first data storage cell comprising a plurality of client computing devices and being configured to create and store one or more secondary copies of primary data generated by the plurality of client computing devices. The method may also include receiving information relating to an incident opened by a user of the first data storage cell. The method may further include updating a performance metric associated with the user to a new value to reflect a negative impact on the performance metric resulting from the opening of the incident by the user, wherein the degree of the negative impact is based at least in part on the configuration information.

Abstract: Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Abstract: A state change detection unit obtains the data generation probability on the basis of the values of observation data and the value of a parameter of a prior distribution, obtains, on the basis of the data generation probability, a run length probability distribution of the time-series observation data acquired up to the current time point as a condition, and detects a change in the state of a facility on the basis of the run length probability distribution. Furthermore, an update unit updates the value of the parameter of the prior distribution using the values of the observation data, to generate the prior distribution to be used for calculating the data generation probability at a next time point.

Abstract: An information processing apparatus includes: a processor configured to: acquire message data that indicate a message size and a response time of each response message identified from an encrypted packet group; identify, based on the message data, first peak sizes that satisfy a first condition from a first size distribution of the message size of each response message; determine based on the message data, when the first peak sizes include a second peak size different from third peak sizes, whether a first time distribution satisfies a second condition, the third peak sizes being peak sizes of the message size of each response message in a normal state, the first time distribution being a frequency distribution of the response time of each response message having the second peak size; and determine that a service is abnormal, when the first time distribution satisfies the second condition.

Abstract: A method and device for starting an external application program in a browser are provided. The method comprises: adding a standard HTML label representing a plugin into a webpage of a browser; adding an external application program plugin module corresponding to the label into the external application program; and after the browser receives an event of clicking the label, transmitting the click event to the external application program plugin module added into the external application program by invoking an interface of the external application program, the external application program plugin module of the external application program invoking an application programming interface which is provided by an operating system for starting an application program to start the external application program.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: A cell controller includes a first communication unit which receives a task program and signal setting information stored in each manufacturing machine from this manufacturing machine, a stop detection unit which refers to the task program and the signal setting information to detect whether a production facility has stopped operation, and a stop cause identification unit which analyzes the task program and the signal setting information to identify the manufacturing machine that has caused the operation stop of the production facility, and this cause. Such a cell controller can detect whether a production facility including manufacturing machines has stopped operation, and automatically identify the manufacturing machine that has caused this operation stop, and this cause.

Abstract: A data storage device includes a number of blocks in multiple dies and a controller configured to perform operations. A method of controlling the data storage device includes: translating logical addresses to physical addresses; activating one or more dies; writing data to one or more blocks; determining the number of write operations performed on the storage device; facilitating determination of a lifetime of the storage device based on a number of blocks, a particular number of write operations, and a maximum number of write operations per block; providing the number of write operations that have been performed to a host to facilitate determination of an amount of a fee, where providing the number of write operations facilitates tracking a percentage of the lifetime of the storage device.