Optimizing Border Gateway Protocol (BGP) traffic using reinforcement learning
AssigneeCiena Corporation
InventorsCengiz Alaettinoglu, Shelley A. Bhalla, Emil Janulewicz, Thomas Triplet, David Côté
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Abstract
Systems, methods, and computer-readable media including software logic are provided for optimizing Border Gateway Protocol (BGP) traffic in a telecommunications network. In one embodiment, systems and methods include, with a current state of one or more inter-Autonomous Systems (AS) links, causing performance of an action in the telecommunication network, determining a metric based on the action to determine an updated current state of the one or more inter-AS links, and utilizing the metric to perform a further action to achieve one or more rewards associated with the one or more inter-AS links.
Claims20
A system configured to optimize Border Gateway Protocol (BGP) traffic in a telecommunications network, the system comprising:
- a network interface configured for communication with the telecommunications network;
- a processing device interconnected with the network interface;
- and
- a memory device configured to store instructions that, when executed, enable the processing device to
- perform an action in the telecommunications network when one or more inter-Autonomous System (AS) links are in a current state, wherein the action in the telecommunications network is configured to have an effect on BGP traffic flow on the one or more inter-AS links,
- define one or more metrics, describing a state of the one or more inter-AS links, based on the effect of the action on the BGP traffic flow,
- determine an updated current state of the one or more inter-AS links based on the defined one or more metrics, and
- utilize the defined one or more metrics to perform a further action to achieve one or more rewards associated with the one or more inter-AS links, wherein an offline simulation is utilized to determine which actions are effective for the one or more rewards based on the current state, the actions and the one or more rewards are based on reinforcement learning where the current state is mapped to some action to maximize the one or more rewards over time, wherein the reinforcement learning seeks to learn what to do given a problem, in the BGP traffic flow, for the current state being mapped to the some action.
A non-transitory computer-readable medium comprising software logic adapted to optimize Border Gateway Protocol (BGP) traffic in a telecommunications network, the software logic, when executed by a processor of a processing device, enabling the processing device to:
- perform an action in the telecommunications network when one or more inter-Autonomous Systems (AS) links are in a current state, wherein the action in the telecommunications network is configured to have an effect on BGP traffic flow on the one or more inter-AS links,
- define one or more metrics, describing a state of the one or more inter-AS links, based on the effect of the action on the BGP traffic flow,
- determine an updated current state of the one or more inter-AS links based on the defined one or more metrics, and
- utilize the defined one or more metrics to perform a further action to achieve one or more rewards associated with the one or more inter-AS links, wherein an offline simulation is utilized to determine which actions are effective for the one or more rewards based on the current state, the actions and the one or more rewards are based on reinforcement learning where the current state is mapped to some action to maximize the one or more rewards over time, wherein the reinforcement learning seeks to learn what to do given a problem, in the BGP traffic flow, for the current state being mapped to the some action.
A method comprising:
- performing an action in a telecommunications network when one or more inter-Autonomous Systems (AS) links are in a current state, wherein action in the telecommunications network is configured to have an effect on BGP traffic flow on the one or more inter-AS links,
- defining one or more metrics, describing a state of the one or more inter-AS links, based on the effect of the action on the BGP traffic flow,
- determining an updated current state of the one or more inter-AS links based on the defined one or more metrics, and
- utilizing the defined one or more metrics to perform a further action to achieve one or more rewards associated with the one or more inter-AS links, wherein an offline simulation is utilized to determine which actions are effective for the one or more rewards based on the current state, the actions and the one or more rewards are based on reinforcement learning where the current state is mapped to some action to maximize the one or more rewards over time, wherein the reinforcement learning seeks to learn what to do given a problem, in the BGP traffic flow, for the current state being mapped to the some action.
Dependencies are inferred from the claim text.