The present disclosure provides computing systems and methods that optimize the execution of workflows that include computational tasks (e.g., which may take the form of functions or containers). In general, the proposed systems and methods can be referred as to or embodied within a serverless workflow enablement and execution platform (also referred to herein as a workflow management system). The serverless workflow platform can facilitate performance of a large-scale computational workflow. In particular, the serverless workflow platform can facilitate performance of serverless workflows that are executed on serverless execution platforms.

A computer-implemented method may include availing a computer platform to a client system. The computer platform may host data and perform data processing. The computer platform may receive a data set from the client system. Computational resources may be determined for the client system, based at least in part on the size of the data set and a size of other data received from the client system, and a category assigned to the client system. A weight may be determined for the client system based on the category, indicating a degree to which resources may be preferentially allocated to process requests associated with the client system. The client system may request a data operation be performed using the data set. Allocation of one or more resources may be prioritized for the data operation, based on the weight. The prioritization may influence how quickly the data operation is performed.

Methods and systems are provided for the deployment of machine learning based processes to public clouds. For example, a method for deploying a machine learning based process may include developing and training the machine learning based process to perform an activity, performing at least one of identifying and receiving an identification of a set of one or more public clouds that comply with a set of regulatory criteria used to regulate the activity, selecting a first public cloud of the set of one or more public clouds that complies with the set of regulatory criteria used to regulate the activity, and deploying the machine learning based process to the first public cloud of the set of one or more public clouds.

Existing techniques of managing Electronic Design Automation (EDA) on cloud are based on pre-defined policies which result in costly burst patterns and server farm tilt. Embodiments of present disclosure overcomes these drawbacks by a method and system for managing EDA on cloud which employ machine learning to predict optimal resource configurations for deploying EDA jobs and configuration circuit on cloud that holds resources required by the optimal resource configuration. Further, different Cloud Service Providers (CSP) are evaluated to determine least cost CSP which has the desired configuration circuit. Completion time of jobs and time required to burst the jobs on cloud are calculated based on which a wait time is determined. The jobs are retained in the queue for corresponding wait time before deploying them on the cloud. The jobs are deployed on the on-prem infrastructure if resources are freed up before the wait time.

An information processing apparatus communicably connected with an intermediary device capable of communicating with at least one device, the information processing apparatus including: circuitry configured to receive, from a terminal operated by a user, identification information that the terminal has acquired from the extraneous source; and transmit a request for execution of a process associated with the acquired identification information to the intermediary device, the request for execution causing the intermediary device to execute the process according to the request for execution to control the device.

Methods, systems and computer program products for bringing-up a computing cluster on a public cloud infrastructure. The method includes using a multicloud management system which is configured to bring-up a computing cluster on any one of a plurality of different public cloud infrastructures to bring-up the cluster in a user's account on the public cloud infrastructure, allowing the user to directly utilize tools and features of the public cloud infrastructure and/or computer security of the user's choice.

An electronic device may map a target application to a machine learning model matched to a request of the target application among a plurality of machine learning models, may generate an inference result for sensing data corresponding to the machine learning model based on the sensing data being sensed by the at least one sensor, and may transfer the generated inference result to at least one of the target application and another application.

Systems, computer program products, and methods are described herein for dynamically reconfiguring electronic applications based on user requests. The present invention may be configured to analyze multiple applications to determine configurations, programming interfaces, functions, and data formats of each application of the applications and receive payload data, where the payload data is based on a user request, and where the user request includes a user identifier associated with a user that provided the user request and information identifying an engineering request. The present invention may be further configured to determine, based on the payload data, an application, of the applications, for performing the engineering request and convert the payload data to a data format, of the data formats, for the application to obtain converted data. The present invention may be further configured to perform, on the application and based on the converted data, the engineering request.

In one embodiment, a device receives application experience metrics for a software-as-a-service application. The device generates, based on the application experience metrics, a predictive model that predicts application experience scores for a plurality of network service providers that provide connectivity to the software-as-a-service application. The device selects a particular network service provider for use by a location, based on an application experience score predicted by the predictive model. The device sends an indication of the particular network service provider to the location.

A method for managing systems with interrelated microservices with self-assembling and self-configuring microservices includes receiving at a first micro service a service request from a client. A determination is the made whether the first micro service is capable of processing the service request. If the first micro service is capable of processing the service requests, then processing the service request; if the first micro service cannot process the service request then routing the service request to a first stem service. The first stem service determines whether there is a second micro service that can process the service request. If the second micro service that can process the service requests exists, then forwarding the service request to the second micro service for processing. If there is no second micro service that can service the service requests then morphing the first stem service into a micro service that can service the service request.