Red Hat, Inc.

Miroslav Jaros

Stefan Bunciak

Martin Vecera

Deployment code that identifies a desired deployment of a computer application onto a computing system comprising one or more compute instances is accessed. The computer application includes a plurality of computing components to be deployed on the one or more compute instances in accordance with the deployment code. Contents of the deployment code are input into a machine learning model (MLM) that has been trained using: a plurality of previously generated deployment codes that identified corresponding desired deployments of corresponding computer applications, and information that identifies successes or failures associated with attempted deployments of the previously generated deployment codes. An output is received from the MLM. Based on the output from the MLM, information indicative of a likelihood of success of deploying the deployment code is sent to a destination.

Infrastructure as code (IaC) pre-deployment analysis via a machine-learning model

Certain aspects and features provide generation or simulation of sensory data that would otherwise come from Internet-of-things (IoT) sensors in reproducible and controllable way. Thus, the response of a system to very large numbers of sensors can be tested without acquiring and deploying a very large number of sensors for test and development purposes. In some examples, a processing device coupled to a network interface identifies a stored function of time describing a locally sensed property for a simulated sensor. The locally sensed property corresponds to at least one event taking place in a virtual environment. The processing device can determine values of an input variable produced by the stored function of time. The values can be wrapped in a communication protocol to produce messages that are transmitted over the network interface.

Sensory data generator

Parameters associated with a distributed network are received. A topology of a virtual network that corresponds to the distributed network is generated in view of the received parameters. The topology of the virtual network is configured to simulate the distributed network and a simulation of the distributed network is executed using the configured virtual network.

Virtual network layer for distributed systems

Responses of software applications to spatiotemporal events can be tested using simulated environments. In one example, a system can generate a simulated environment having simulated distributed devices positioned at various spatial locations in the simulated environment. The system can then simulate a spatiotemporal event propagating through the simulated environment by modifying a device simulation property of each simulated distributed device based on the spatiotemporal event and a respective spatial location of the simulated distributed device in the simulated environment. This can produce simulation outputs impacted by the spatiotemporal event. The system can then provide the simulation outputs as input to a target software application to test a response to the spatiotemporal event by the target software application.

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Brno, Czechia

Miroslav Jaros