Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.

Abstract: A computerized method determines erasure coding (EC) formats for data extents in storage systems. Traffic history data of a data extent to be stored in the storage system is obtained. Estimated future traffic data is generated using a traffic estimation model, wherein the traffic history data is provided to the model as input. A current storage system constraint is calculated using the obtained traffic history data, and a constraint adjustment value is determined using a proportional, integral, derivative (PID) controller based on the current storage system constraint. An EC format for the data extent is determined using the estimated future traffic data and the determined constraint adjustment value and the data extent is stored in the storage system using the determined EC format. Use of the model and the PID controller balances optimized storage use with efficient data traffic handling.

Abstract: A computing system including one or more processing devices that train an oversubscription reinforcement learner at least in part by receiving computing resource usage trajectories. At the oversubscription reinforcement learner, the training further includes generating prototypes based at least in part on the computing resource usage trajectories. The training further includes, based at least in part on the prototypes, generating an oversubscription rate. The training further includes outputting a prototype feedback query and/or an oversubscription rate feedback query. The training further includes receiving a prototype feedback input and/or an oversubscription rate feedback input.

Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.

Abstract: A system and method that include receiving an oilfield operational plan and converting the oilfield operational plan into pixel-grids that are interpreted as images. The system and method also include inputting the pixel-grids to an image analysis convolution neural network to execute an image interpretation process. The system and method additionally include determining a current state of the oilfield operational plan based on the image interpretation process. The system and method further include determining a next state of the oilfield operational plan.

Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.

Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Abstract: In various examples, techniques for using future trajectory predictions for adaptive cruise control (ACC) are described. For instance, a vehicle may determine a future path(s) of the vehicle and a future path(s) of an object(s). The vehicle may then use a speed profile(s) and the future path(s) to determine a trajectory(ies) for the vehicle. The vehicle may then select a trajectory, such as based on the future path(s) of the object(s). Based on the trajectory, ACC of the vehicle may cause the vehicle to navigate at a speed or a velocity. This way, the vehicle is able to continue using ACC even when the driver makes a maneuver(s) or the system determined to make a maneuver, such as switching lanes or choosing a lane when a road splits.

Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.

Abstract: A system and method that include receiving an oilfield operational plan and converting the oilfield operational plan into pixel-grids that are interpreted as images. The system and method also include inputting the pixel-grids to an image analysis convolution neural network to execute an image interpretation process. The system and method additionally include determining a current state of the oilfield operational plan based on the image interpretation process. The system and method further include determining a next state of the oilfield operational plan.

Abstract: A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.

Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.

Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

Abstract: A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.