SRLG-Disjoint Path Search Using Loss-Bound Pruning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network topologies are manually planned statically, leading to inconsistencies and load imbalances due to instantaneous network changes, affecting customer service experience and requiring real-time adjustments that current methods like KSP and TA algorithms are unable to efficiently handle, especially in large-scale networking scenarios.
Innovation Solution
A method involving depth-first search and pre-estimation of a loss upper bound to efficiently prune the search space, determining K paths with loss values less than the upper bound, and finding a working and protection path that meet SRLG disjointness and other constraints, using an electronic device and computer storage medium to implement this method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual static network planning is used, then network topology can be initially established, but it cannot adapt to instantaneous network changes and leads to load imbalance and service inconsistency
Solution Approach 1:
The patent transforms static manual network planning into dynamic automated path computation by implementing real-time constraint-based routing algorithms that continuously adapt to network changes, enabling the system to dynamically adjust working and protection paths based on current network state and constraints
Solution Approach 2:
The system enables self-service by automating the network path planning and adjustment processes through intelligent algorithms that independently compute optimal routes, manage SRLG constraints, and adapt to network changes without requiring manual intervention for each adjustment
2Reliability
If traditional path search algorithms (KSP, TA) are used, then path computation can be performed, but computational time and storage requirements increase significantly in large-scale networking scenarios
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing critical network parameters, constraint information, and path metrics in advance, allowing the system to quickly retrieve and process this pre-prepared data during real-time path computation, significantly reducing computational time for large-scale networks
Solution Approach 2:
The system extracts and separates critical constraint information (SRLG identifiers, path metrics, network topology data) from the overall path computation process, allowing these elements to be independently processed, stored, and reused, thereby reducing the computational burden during actual path search operations
3Reliability
If comprehensive path search is performed to ensure SRLG disjointness and meet all constraints, then reliable working and protection paths can be found, but search space complexity and computational resources increase
Solution Approach 1:
The patent segments the path search process into distinct phases: working path computation, protection path computation, and constraint verification. By dividing the search space and processing steps, the system can efficiently compute SRLG-disjoint paths while reducing overall computational complexity
Solution Approach 2:
The system performs preliminary computation of SRLG constraints and path metrics before the actual path search, pre-identifying candidate paths and their associated constraints. This preliminary preparation significantly reduces the complexity of the subsequent disjointness verification and path selection processes
Data Source
AI summary
A method for solving shared risk link group disjointness is provided, and includes: obtaining information about a link from a source node to a destination node in a communication network topology, where the information about the link includes constraint information and a loss value of each path from the source node to the destination node; estimating a value of a loss upper bound of a working path; determining K paths having loss values less than the value of the loss upper bound in a search space, where the search space includes N paths from the source node to the destination node, and K<N; searching the K paths to determine M paths that meet the constraint information, where M<K; and searching the M paths to determine a working path and a protection path that meet a requirement.