Abstract: Systems, methods, and computer-readable media are provided for recommending actions to be taken in a network for optimizing or improving the operability of the network. A method, according to one implementation, includes a first step of receiving raw, unprocessed data that is obtained directly from one or more network elements of a network. The method includes second step of determining one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions.

Abstract: Systems, methods, and computer-readable media are provided for recommending actions to be taken in a network for optimizing or improving the operability of the network. A method, according to one implementation, includes a first step of receiving raw, unprocessed data that is obtained directly from one or more network elements of a network. The method includes second step of determining one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions.

Abstract: Systems and methods for recommending actions in a closed-loop system are provided. In one embodiment, an Action Recommendation Engine (ARE) may include a processor and memory configured to store computer programs having instructions that cause the processor to obtain input data pertaining to a state of a network and obtain information regarding one or more historical actions performed on the network. Also, the instructions may cause the processor to utilize a Machine Learning (ML) model for imposing one or more current actions on the network, the one or more current actions selected from the group of procedures consisting of: a) suggesting one or more remediation actions that, when performed, transition the network from a problematic state to a normal state, and b) identifying one or more root causes in response to detecting a transition in the network from a normal state to a problematic state.

Abstract: Systems and methods for recommending actions in a closed-loop system are provided. In one embodiment, an Action Recommendation Engine (ARE) may include a processor and memory configured to store computer programs having instructions that cause the processor to obtain input data pertaining to a state of a network and obtain information regarding one or more historical actions performed on the network. Also, the instructions may cause the processor to utilize a Machine Learning (ML) model for imposing one or more current actions on the network, the one or more current actions selected from the group of procedures consisting of: a) suggesting one or more remediation actions that, when performed, transition the network from a problematic state to a normal state, and b) identifying one or more root causes in response to detecting a transition in the network from a normal state to a problematic state.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to select a computing resource from a plurality of computing resources to perform a computing process. A request is received from a remote computing device to perform the computing process. A first set of estimated metrics is accessed that includes an estimated metric for each computing resource and the first remote computing device. The second data is processed using a machine learning algorithm to select a candidate computing resource to perform the process. The machine learning algorithm selects the candidate computing resource based on a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device, and a capacity of each computing resource of the plurality of computing resources.