P2MP LSP Path Optimization via Simulated Annealing
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Solution Overview
Problem
Existing network technologies face challenges in efficiently computing and optimizing point-to-multipoint (P2MP) label switched paths (LSPs) in computer networks, particularly in managing resource costs, avoiding routing loops, and ensuring diversity and failure resilience.
Innovation Solution
A controller uses network topology information to determine and optimize P2MP LSP paths by iteratively applying link penalties and simulated annealing techniques, modifying the network topology to find the lowest total cost solution that accounts for resource usage, diversity, and constraint satisfaction, thereby configuring the network with robust and efficient path configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional path computation methods are used for P2MP LSPs, then the computation is simpler and faster, but the solution falls into local minima and fails to achieve global optimization
Solution Approach 1:
The patent applies simulated annealing by dynamically changing the temperature parameter to control the exploration-exploitation tradeoff. High temperature enables broad exploration of the solution space to escape local minima, while gradual cooling concentrates the search around promising regions, achieving global optimization without excessive computational complexity
Solution Approach 2:
The path computation process is made dynamic through iterative optimization. The algorithm dynamically adjusts link penalties and explores different path configurations over multiple iterations, transitioning from static shortest-path computation to dynamic global optimization that adapts to network conditions
2Reliability
If link penalties are applied to avoid routing loops and ensure diversity, then path reliability improves, but the total resource cost increases
Solution Approach 1:
The algorithm incorporates feedback mechanisms where the computed paths are evaluated against constraints and resource usage. Link penalties are adjusted based on the impact on path diversity and loop avoidance, creating a feedback loop that balances reliability requirements with resource efficiency
Solution Approach 2:
The penalty values for links are dynamically adjusted during the optimization process. Links that cause routing loops or reduce path diversity receive higher penalties, while the algorithm explores different penalty configurations to find the optimal balance between avoiding harmful routing patterns and minimizing overall resource consumption
3Reliability
If multiple paths are computed for diversity and failure resilience, then network robustness improves, but the computation time and complexity increase
Solution Approach 1:
The computation process is made dynamic through iterative optimization. The algorithm dynamically adjusts link penalties and explores different path configurations over multiple iterations, transitioning from static shortest-path computation to dynamic global optimization that adapts to network conditions
Solution Approach 2:
The simulated annealing process uses temperature parameter changes to control computation time. At high temperatures, the algorithm performs broader exploration to ensure path diversity, while gradual cooling reduces the search space and computation time while maintaining solution quality
Data Source
AI summary
In some examples, a controller for a network includes a path computation module configured for execution by one or more processors to obtain configuration information for at least one point-to-multipoint label switched path (P2MP LSP); obtain, from the network via at least one protocol, network topology information defining a network topology for the network; determine, based on the network topology, a first solution comprising first respective paths through the network for the at least one P2MP LSP; determine, after generating a modified network topology based on the network topology, a second solution comprising second respective paths through the network for the at least one P2MP LSP. The controller also includes a path provisioning module configured for execution by the one or more processors to configure the network with the solution of the first solution and the second solution having the lowest total cost.