Abstract: In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.

Abstract: A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Abstract: A method for automatically detecting and evaluating experience data associated with an experience journey to identify and adjust an action that is taken to improve customer experience is provided. In some embodiments, the method includes generating performance data associated with the experience journey from the experience data using a machine learning model. The method further includes determining the action to be taken based on analyzing the performance data. The method further includes collecting new experience data responsive to the action having been taken and training the machine learning model using the new experience data. The method further includes updating the performance data and the action to be taken based on training the machine learning model.

Abstract: Systems and methods for scheduling storage management tasks over predicted user tasks in a distributed storage system. A method commences upon receiving a set of historical stimulus records that characterize management tasks that are run in the storage system. A corresponding set of historical response records comprising system metrics associated with execution of the system tasks is also received. A learning model is formed from the stimulus records and the response records and formatted to be used as a predictor. A set of forecasted user tasks is input as new stimulus records to the predictor to determine a set of forecasted system metrics that would result from running the forecasted user tasks. Management tasks are selected so as not to impact the forecasted user tasks. Management tasks can be selected based on non-contentions resource usage between historical management task resource usage and predictions of resource usage by the user tasks.

Abstract: Systems and methods for scheduling storage management tasks over predicted user tasks in a distributed storage system. A method commences upon receiving a set of historical stimulus records that characterize management tasks that are run in the storage system. A corresponding set of historical response records comprising system metrics associated with execution of the system tasks is also received. A learning model is formed from the stimulus records and the response records and formatted to be used as a predictor. A set of forecasted user tasks is input as new stimulus records to the predictor to determine a set of forecasted system metrics that would result from running the forecasted user tasks. Management tasks are selected so as not to impact the forecasted user tasks. Management tasks can be selected based on non-contentions resource usage between historical management task resource usage and predictions of resource usage by the user tasks.

Abstract: An apparatus includes a drive; a movable arm connected to the drive and having a first link rotatable about the drive at a first rotary joint, a first actuator configured to cause a rotation of the first link about the first rotary joint, at least one second link connected to the first link at a second rotary joint, at least one second actuator configured to cause a rotation of the second link about the second rotary joint, and at least one gripper on the second link, the gripper being configured to carry a payload. The gripper includes a dielectric substrate, at least one electrode disposed on the dielectric substrate, the electrode being configured to produce an attractive force on a surface of the electrode to attract the payload, and a main electronic module configured to apply a voltage to the electrode from a source of current.

Abstract: In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.

Abstract: An apparatus including a robot drive; and a robot arm connected to the robot drive, where the robot arm includes a first link connected to the robot drive, a second link rotatably connected to the first link at a first rotatable connection, and an end effector rotatably connected to the second link at a second rotatable connection. The end effector includes a heat choke located between a substrate support area of the end effector and the second rotatable connection. At least one of the first rotatable connector or the second rotatable connection includes a rotary thermal coupling having interleaved members which are rotatable relative to each other.

Abstract: An apparatus includes a drive; a movable arm connected to the drive, the movable arm comprising a first link connected to the drive at a shoulder, a second link connected to the first link at an elbow, a third link connected to the second link at a wrist, and a fourth link connected to the second link at the wrist; at least one first actuator located in the second link configured to cause a rotation of the third link about the wrist; and at least one second actuator located in the second link configured to cause a rotation of the fourth link about the wrist. One or more of a thermal management, a power distribution, or a communication is effected through the second link.

Abstract: A system and method include receiving, by a troubleshooting system of a virtual computing system, a search query for troubleshooting a problem associated with a component of the virtual computing system. The search query is received via a troubleshooting interface of the troubleshooting system. The system and method also include parsing the search query, including associating a troubleshooting category with the parsed search query, determining possible causes of the problem from the troubleshooting category, and ranking the possible causes based on a pre-determined criteria. The system and method additionally include displaying a subset of highest ranked possible causes of the problem on the troubleshooting interface.

Abstract: A system and method for dynamically adjusting the amount of memory allocated to a virtual machine includes generating, by a memory resizing system, a current memory usage profile for the virtual machine. The memory resizing system and the virtual machine are part of a virtual computing system and the current memory usage profile is generated by mapping, as a function of time, memory usage information from the virtual machine. The system and method also include computing an upper baseline based upon a peak memory usage in the current memory profile, updating an initial memory allocation of the virtual machine based upon the upper baseline and a predetermined threshold for obtaining an initial revised memory allocation, determining a moving average of memory usage from a historical memory usage profile, and updating the initial revised memory allocation based upon the moving average of memory usage for obtaining a final revised memory allocation.

Abstract: Systems for distributed data storage. A method embodiment commences upon capturing a history of storage I/O activity over a recent time period. A predictive model is derived from the captured storage I/O activity, and the predictive model is then used for predicting future storage I/O activity. A set of snapshot planning parameters comprising objectives (e.g., to minimize costs or to maximize likelihood completing a snapshot activity by a prescribed time) and/or constraints (e.g., don't wait more than one day to start a snapshot) are applied to the predicted storage I/O characteristics to generate a set of feasible snapshot plans. One of the feasible snapshot plans is selected for scheduling so as to begin the planned snapshot activity at a prescribed time. The snapshot planning parameters are normalized based on the predicted storage I/O characteristics.

Abstract: Systems for alerting in computing systems. A method commences by defining a plurality of analysis zones bounded by respective ranges of system metric values, which ranges in turn correspond a plurality of system behavior classifications. System observations are taken while the computing system is running. A system observation comprising a measured metric value is classified into one or more of the behavior classifications. Based on the classification, one or more alert analysis processes are invoked to analyze the system observation and make a remediation recommendation. An alert or remediation is raised or suppressed based on one or more zone-based analysis outcomes. An alert is raised when anomalous behavior is detected. The system makes ongoing observations to learn how and when to classify a measured metric value into normal or anomalous behaviors. As changes occur in the system configuration, the analysis zones are adjusted to reflect changing bounds of the zones.

Abstract: Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured.

Abstract: In one embodiment, a method may comprise receiving a query to search for one or more entities of the data center, the query including one or more query tokens. The method may then identify one or more entity-type nodes corresponding to the query, and rank the entity-type nodes based on a score determined for each of the identified entity-type nodes. For at least a portion of the ranked entity-type nodes, the method may generate a structured query associated with each of the entity-type nodes based at least in part of one or more activity-type nodes connected to each of the entity-type nodes. Then, the method may retrieve, based on the structured queries, results from one or more databases.

Abstract: Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured.

Abstract: A system for implementing seasonal time series analysis and forecasting using a distributed tournament selection process. Time series analysis is initiated by a prediction or runway event trigger. Prediction events include a determination of the availability of one or more resources at a given point in time or over a given time period. A runway event may include a determination of when a resource is below a minimum threshold level of availability. Training of the prediction models is based data taken from different time periods, accounting for any combination of time periods and/or for differing sampling frequencies or ranges. Processes for prosecuting a tournament to identify winning models are parallelized to achieve low latency tournament results. Ranking of each model and/or some combination of models is based on how precisely and/or conclusively the models match a determined set of training data.

Abstract: Systems for distributed data storage. A method embodiment commences upon capturing a history of storage I/O activity over a recent time period. A predictive model is derived from the captured storage I/O activity, and the predictive model is then used for predicting future storage I/O activity. A set of snapshot planning parameters comprising objectives (e.g., to minimize costs or to maximize likelihood completing a snapshot activity by a prescribed time) and/or constraints (e.g., don't wait more than one day to start a snapshot) are applied to the predicted storage I/O characteristics to generate a set of feasible snapshot plans. One of the feasible snapshot plans is selected for scheduling so as to begin the planned snapshot activity at a prescribed time. The snapshot planning parameters are normalized based on the predicted storage I/O characteristics.

Abstract: A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.