Action recommendation engine (ARE) for network operations center (NOC) solely from raw un-labeled data
AssigneeCiena Corporation
InventorsDavid Côté, Shervin Shirmohammadi, Shady A. Mohammed, Sa'di Altamimi, Basel Altamimi
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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.
Claims20
A non-transitory computer-readable medium configured to store computer logic having instructions that, when executed, enable a processing device to:
- receive raw, unprocessed data obtained directly from one or more network elements of a network, and
- determine one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions, wherein the direct association is based on offline Reinforcement Learning (RL) that analyzed one or more of historical Network Operations Center (NOC) actions and operations on a network simulator.
A system comprising:
- one or more processing devices;
- and
- a memory device configured to store computer logic having instructions that, when executed, enable the one or more processing devices to
- receive raw, unprocessed data obtained directly from one or more network elements of a network, and
- determine one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions, wherein the direct association is based on offline Reinforcement Learning (RL) that analyzed one or more of historical Network Operations Center (NOC) actions and operations on a network simulator.
A method comprising the steps of:
- receiving raw, unprocessed data obtained directly from one or more network elements of a network;
- and
- determining one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions, wherein the direct association is based on offline Reinforcement Learning (RL) that analyzed one or more of historical Network Operations Center (NOC) actions and operations on a network simulator.
Dependencies are inferred from the claim text.