International Business Machines Corporation

Sowjanya Rao

Sarbajit Kumar Rakshit

Venkata Vara Prasad Karri

Saraswathi Sailaja Perumalla

Shailendra Moyal

Akash U Dhoot

Abhishek Jain

Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

Multi-device processing activity allocation

An augmented reality (AR) container orchestration system includes computer hardware including an AR system and a container orchestration platform. The AR system is configured to perform identifying a container to be deployed, displaying, to a user, a grid having a plurality of cells each of which are configured to receive a representation of the container, and redisplaying, using the AR system and to the user, the grid based upon a movement of the representation of the container to a particular position within the grid. The container orchestration platform is configured to perform identifying possible configurations for the container, and configuring the container based upon the movement of the representation of the container to the particular position. The includes a plurality of axes, and each axis of the plurality of axis represents a different configuration-related parameter of the configurations for the container.

Intelligent container configuring using augmented reality

Computer technology for performing dynamic code branching of self-adapted code upon successful execution of a contextual scenario by artificial intelligence (AI) enabled edge device (for example, an autonomous vehicle or an industrial robotic device). predicting a second contextual scenario where the AI enabled edge device can perform a predetermined activity, and proactively deploying self-adapted code on the AI enabled edge device.

Scenario aware dynamic code branching of self-evolving code

First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

Dynamic selection of parameter threshold values

A processor may receive user data associated with one or more locations of a user in an environment. The processor may receive edge computing data associated with utilization of edge computing resources by the user. The processor may analyze the edge computing data to associate a context with an edge computing resource need. The processor may analyze the user data to associate a context with a location of the user within the environment. The processor may determine a first location of the user in the environment at a first time. The processor may predict a first edge computing need of the user in the first location. The processor may determine an arrangement of one or more edge computing devices configured to meet the first edge computing need of the user at the first time.

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Hyderabad Telangana, India

Sowjanya Rao