Method and system for optimization of network traffic based on reinforcement learning

A method for optimizing network traffic distribution across an equal-cost multi-path (ECMP) set of interfaces, comprising:

• in a control plane, optimizing network traffic distribution across an equal-cost multi-path (ECMP) set of interfaces by:
• implementing reinforcement learning (RL) operations, including:
• modeling a network state space comprising interface characteristics including bandwidth capacity per interface representing bins having capacity constraints, and traffic characteristics including size of each traffic flow representing items of varying sizes to be packed into the bins;
• defining an action space for allocating specific flows to specific interfaces, wherein each action represents a decision to assign a specific flow to a specific interface to pack the flow into the interface while respecting the capacity constraints;
• establishinga reward function that encourages efficient utilization of bandwidth while penalizing packet loss, latency, and jitter, wherein the reward function comprises a utilization reward based on a ratio of used bandwidth to total available bandwidth and a penalty that is a weighted combination of packet loss rate, latency, and litter;
• learning optimal flow-to-interface allocations based on the modeled state space, action space, and reward function;
• and
• implementing flow allocation operations, including:
• generating flow allocation policies based on the learned optimal flow-to-interface allocations;
• and
• dynamically updating the flow allocation policies in response to changes in network conditions detected based on collected network statistics, wherein the updated flow allocation policies reassign one or more flows from a first interface to a second interface based on available bandwidth;
• and
• implementing, in a data pane, the flow allocation policies generated by the flow allocation operations;
• collecting, by at least one network device of the data plane, network statistics including bandwidth utilization, packet loss rates, latency, and jitter for each interface;
• transmitting the collected network statistics to the control plane in real time;
• and
• routing network traffic flows based on the implemented flow allocation policies.

A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• in a control plane, optimizing network traffic distribution across an equal-cost multi-path (ECMP) set of interfaces by:
• implementing reinforcement learning (RL);
• operations, including:
• modeling a network state space comprising interface characteristics including bandwidth capacity per interface representing bins having capacity constraints, and and traffic characteristics including size of each traffic flow representing items of varying sizes to be packed into the bins;
• defining an action space for allocating specific flows to specific interfaces, wherein each action represents a decision to assign a specific flow to a specific interface to pack the flow into the interface while respecting the capacity constraints;
• establishing a reward function that encourages efficient utilization of bandwidth while penalizing packet loss, latency, and jitter, wherein the reward function comprises a utilization reward based on a ratio of used bandwidth to total available bandwidth and a Penalty that is a weighted combination of packet loss rate, latency, and litter;
• learning optimal flow-to-interface allocations based on the modeled state space, action space, and reward function;
• and
• implementing flow allocation operations, including:
• generating flow allocation policies based on the learned optimal flow-to-interface allocations;
• dynamically updating the flow allocation policies in response to changes in network conditions detected based on collected network statistics, wherein the updated flow allocation policies reassign one or more flows from a first interface to a second interface based on available bandwidth;
• and
• implementing, in a data pane, the flow allocation policies generated by the flow allocation operations;
• collecting, by at least one network device of the data plane, network statistics including bandwidth utilization, packet loss rates, latency, and jitter for each interface;
• transmitting the collected network statistics to the control plane in real time;
• and
• routing network traffic flows based on the implemented flow allocation policies.