feat(rank): add TOPSIS-based node ranking for VNE - #44
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Adds TOPSISNodeRank to virne/solver/rank/node_rank.py and the corresponding TOPSISRankSolver to virne/solver/heuristic/node_rank.py. The ranker treats each substrate node as a TOPSIS alternative whose criteria are its available resource attributes (CPU, memory, etc.) concatenated with the aggregate available bandwidth of its incident links. The TOPSIS closeness coefficient — proximity to the ideal resource profile — becomes the node ranking score used by the two-stage mapping pipeline. Algorithm: 1. Build decision matrix: node resources || aggregate link resources 2. Euclidean-normalise each criterion column 3. Apply equal weights across all criteria (all benefit-type) 4. Compute positive and negative ideal solutions 5. Score each node by dist_neg / (dist_pos + dist_neg) Usage: python main.py solver.solver_name=topsis_rank Docs: adds topsis_rank row to docs/source/solver/overview.md.
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Summary
Adds a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) node ranking algorithm and its corresponding two-stage solver to Virne.
What changes
virne/solver/rank/node_rank.py— newTOPSISNodeRankclass registered as'topsis':dist_neg / (dist_pos + dist_neg). Nodes closest to the ideal resource profile receive the highest score and are placed first during VNE.virne/solver/heuristic/node_rank.py— newTOPSISRankSolverregistered as'topsis_rank':TOPSISNodeRankinside the existingBaseNodeRankSolvertwo-stage pipeline.python main.py solver.solver_name=topsis_rank.docs/source/solver/overview.md— addstopsis_rankrow to the heuristics solver table.Motivation
TOPSIS is a well-established MCDM method for multi-criteria node selection that provides a principled distance-to-ideal scoring without requiring iterative convergence (unlike GRC or RW). It naturally handles heterogeneous resource dimensions and produces scores in
[0, 1]that are directly interpretable as embedding priority.