Quantum Concept Processor for Network Traffic Routing Optimization
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Solution Overview
Problem
Current data traffic management techniques in communications networks face challenges in optimizing link capacity usage, leading to congestion and overloads, especially when dealing with non-linear conditions and complex constraints such as redundancies and Quality of Service requirements.
Innovation Solution
A computer-implemented method using a quantum concept processor to optimize data traffic routing by splitting traffic demands into sub-demands and calculating fractional capacity usages, formulating these as a quadratic stress function to minimize capacity usage across communication paths, thereby selecting optimal paths that respect capacity limits and distribute load uniformly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If linear optimization techniques are applied to minimize maximal link capacity usage, then link overload is reduced, but the computational complexity becomes NP-hard and cannot handle non-linear conditions
Solution Approach 1:
The patent segments the traffic flow into multiple commodities, each with its own demand and routing requirements. This segmentation allows the complex network optimization problem to be broken down into manageable sub-problems that can be solved more efficiently, avoiding the NP-hard complexity of treating all traffic as a single entity.
Solution Approach 2:
The patent introduces a non-linear cost function that changes parameters based on link utilization levels. When link usage exceeds a threshold, the cost function increases non-linearly, dynamically adjusting routing decisions based on current network conditions rather than using fixed linear weights.
2Productivity
If link weights are manipulated to guide traffic demands, then routing optimization is achieved, but congestion and overloads still occur in high-demand scenarios
Solution Approach 1:
The patent implements dynamic routing where link weights are not fixed but adapt based on real-time network conditions. The cost function dynamically adjusts based on current link utilization, allowing the system to respond to changing traffic patterns and avoid congestion that static weight manipulation cannot prevent.
Solution Approach 2:
The patent incorporates feedback mechanisms where routing decisions are continuously adjusted based on observed link utilization. The non-linear cost function provides feedback about link capacity usage, guiding traffic away from overloaded links and toward underutilized paths, creating a self-regulating system that prevents congestion.
3Productivity
If known optimization techniques are applied, then some capacity distribution is achieved, but unused capacity and overloads coexist in the network
Solution Approach 1:
The patent uses a non-linear cost function that changes parameters based on link utilization thresholds. This causes traffic to be redistributed more uniformly across the network, preventing both unused capacity in some links and overloads in others by dynamically adjusting routing preferences based on current conditions.
Data Source
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AI summary
The invention pertains to a computer-implemented method for optimizing a usage distribution in a communications network (1) by using a quantum concept processor (6). A set of traffic demands for a transfer of determined data volumes between origin nodes (o) and destination nodes (d) among the plurality of communication nodes (2) is captured. The traffic demands (5) are split into sub-demands (7, p). A set of optional communication paths (k) for an individual routing of each sub-demand (7, p) is specified. The edges (e) within the set of optional communication paths (k) are assigned a respective usage capacity limit. Fractional capacity usages of the edges (e) are calculated based on the respective usage capacity limit. Then, the calculated fractional capacity usages are formulated as terms of a quadratic stress function. An optimized routing is determined by using a quantum concept processor (6), thereby selecting for each sub-demand (7, p) one communication path (k) from the set of optional communication paths (k), such that the quadratic stress function is minimized.