Network Flow Orchestration for Time-Constrained Route Optimization
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Solution Overview
Problem
Large communication networks face computational complexity in determining optimal network setups and restoration schemes, with NP-hard optimization problems making it difficult to verify the optimality of solutions, leading to inefficient resource allocation and high operational costs.
Innovation Solution
A network optimization technique using a subset of optimization functions selected based on a decision tree, combined with iterative parameter adjustments within a time constraint, to derive an optimal solution for service link establishment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to determine optimal network setups, then solution optimality can be verified, but computational complexity becomes unmanageable and resource allocation efficiency deteriorates
Solution Approach 1:
The patent segments the optimization problem into multiple phases: initial solution generation using heuristics, followed by iterative local search improvements. This divides the complex NP-hard problem into manageable stages that can be executed within time constraints while achieving near-optimal solutions.
Solution Approach 2:
The patent changes parameters dynamically during the optimization process, including adjusting search depth, iteration counts, and cost function weights based on problem characteristics and time remaining. This allows the system to adapt its computational effort to achieve the best possible solution quality within available resources.
2Loss of energy
If comprehensive optimization is performed to minimize network costs, then operational costs are reduced, but computational time and complexity increase
Solution Approach 1:
The patent performs preliminary actions by generating an initial feasible solution using heuristic methods before applying more computationally intensive local search optimizations. This preliminary solution provides a baseline that guides subsequent optimization efforts, reducing the search space and computational time required to achieve cost minimization.
Solution Approach 2:
The optimization process is made dynamic by adjusting the intensity and duration of optimization phases based on time constraints and problem characteristics. The system dynamically balances exploration vs. exploitation, intensifying search when time permits and accepting good-enough solutions when time is limited, thereby managing the trade-off between cost minimization and computational time.
3Reliability
If exact optimization algorithms are used to solve NP-hard problems, then optimal solutions can be guaranteed, but the complexity of verifying and computing solutions becomes prohibitive
Solution Approach 1:
The patent incorporates feedback mechanisms where the quality of solutions is continuously evaluated and used to guide further optimization. The system monitors solution quality metrics and computational progress, using this feedback to adjust optimization parameters and determine when to terminate the search process, achieving a practical balance between solution quality and computational complexity.
Solution Approach 2:
The patent employs approximation algorithms and heuristics that provide 'good enough' solutions without guaranteeing optimality, trading off exactness for computational tractability. These simplified approaches are sufficient for practical network optimization where near-optimal solutions are acceptable and computational resources are limited.
Data Source
AI summary
According to one example, a method performed by a computing system includes receiving a request for optimizing a communication network, the request defining a set of demands for the communication network, each demand of the set of demands having two or more endpoints, a diversity policy, a number of routes, and a set of constraints. The method further includes selecting a subset of optimization functions from a set of optimization functions, the selecting being based on characteristics of the communication network and a decision tree. The method further includes using the selected subset of optimization functions, determining an initial solution for the communication network, the initial solution comprising assignment of routes within demands of the set of demands. The method further includes, within a time constraint, iteratively applying an updated subset of optimization functions with modified parameters to reduce a cost of the initial solution to produce an optimal solution.