feat: distributed FaaS coordination heuristics (FaaS-MADiG / MAPoD / MABR) + coordination rework

[miciav]

Summary

This branch adds a family of price-free distributed heuristics for DiFRALB/DeFRALB and reworks the shared coordination logic across all three for correctness, determinism, and consistency. The heuristics form a spectrum of coordination styles, all built as controlled ablations of the FaaS-MADeA auction:

• FaaS-MADiG — greedy diffusion (removes the price signal; buyer scans its whole one-hop neighbourhood greedily by score).
• FaaS-MAPoD — power-of-d-choices (removes full visibility; buyer probes only d sampled neighbours per step, serves best of sample).
• FaaS-MABR-S / -R / -O — Gauss-Seidel best response (the sequential counterpart to the simultaneous diffusion methods): fixed-order, randomized-order, and capped-reoptimization variants.

All keep the local planning stack and shared helpers, reuse the same seller-side clearing, and isolate exactly one mechanism each (pricing → visibility → sequential-vs-simultaneous coordination).

Validation: uv run pytest -q → 270 passed. FaaS-MABR e2e runs under real Gurobi (smoke + same-seed reproducibility for all three variants).

FaaS-MABR (Gauss-Seidel best response) — decentralized_bestresponse.py

• True best-response sweep: each node releases its current buyer row back to the shared residual-capacity ledger, recomputes its placement greedily by score, and commits the coordinate delta (new_row − previous_row). Later nodes observe earlier nodes' updates through the live ledger — the defining Gauss-Seidel property — and the loop converges to a fixed point.
• Fixed-point termination on allocation_changed (no node revised its row this sweep), the correct signal under release-and-recompute (raw placement volume never settles).
• Three variants: FaaS-MABR-S (fixed order), FaaS-MABR-R (seeded random order; variance via --n_experiments), FaaS-MABR-O (capped local re-optimization via LSP_capped/LSP_capped_fixedr and a per-node re-solve).
• Runtime amortization via compute_sweep_runtime (re-optimization time excluded before amortizing bookkeeping over active nodes); input validation for order/response.
• Additive wiring: CLI keys faas-br-s/-r/-o, method names FaaS-MABR-S/-R/-O (mkey LSPc), compare_results.py palette + default set, planar_comparison.json br_* blocks. Paper-ready LaTeX note under faas-bestresponse-note/ positioning it honestly as the textbook Gauss-Seidel relaxation (not claimed novel).

Cross-method coordination rework (diffusion / powerd / bestresponse)

Landed in lockstep across the three runners so they stay mutually consistent:

• Memory-aware seller eligibility: a node can host a replica only if rho[j] >= memory_requirement[f] (was rho[j] > 0).
• Deterministic seller clearing: explicit (score, index) tie-breaks replace unstable np.argsort; evaluate_assignments reworked — seller_pairs now includes current hosts (so saturated sellers can still be re-evaluated for incumbent replacement), leftover-aware replica start, and lowest-score-incumbent-first reassignment.
• LSPr_fixedr under --fix_r (social-welfare re-solve now fixes replicas consistently with the subproblem); best_centralized_cost initialized to -inf (fixes a latent bug where a negative centralized objective could never set the initial best).
• coordination_rho zeroes memory/replica expansion under --fix_r (no new replicas when replicas are pinned).
• force_memory_bids parity in the block-A memory-bid emission; vectorized rmp_omega/omega/fairness updates.

Shared run_faasmadea.start_additional_replicas: deterministic proportional allocation plus a leftover-memory packing pass (the old per-function floor division left memory unused).

Also on this branch

• FaaS-MADiG and FaaS-MAPoD (greedy diffusion + power-of-d), each with a design spec, plan, and citation-audited LaTeX note (faas-madig-note/, faas-mapod-note/ — 5/5 cited works verified against CrossRef, PDFs committed).
• run.py: method→(mkey, name) mapping flattened into a single METHOD_RESULT_MODELS dict.
• Hierarchical auction, uv migration, and extended test coverage.

Heads-up for reviewers / reproducibility

The determinism rework of define_assignments/evaluate_assignments and the leftover-packing in start_additional_replicas change the numeric outputs of the existing baselines (FaaS-MADeA, FaaS-MADiG, FaaS-MAPoD), not just the new method — the changes are more correct and deterministic, but any benchmark CSVs/figures produced before this rework are now stale and should be regenerated for an apples-to-apples three-way comparison.

🤖 Generated with Claude Code

[miciav]

Added remote_experiments/: dispatches DFaaSOptimizer algorithm comparisons to remote Gurobi VMs via ray-dispatcher, with a two-phase define/run workflow, a pluggable experiment-suite registry, manifest-based stop/resume, and a rich TUI for live per-experiment/per-VM progress.

13 tasks, TDD throughout, individually reviewed + a final whole-branch review (one fix-wave addressed: added cmd_run integration test coverage and README prerequisites). 320/320 tests passing.

Depends on a small prerequisite addition to ray-dispatcher itself (Dispatcher.running_hosts(), already merged on that repo's main) for live per-VM job attribution.

See docs/superpowers/specs/2026-06-30-remote-experiments-design.md and docs/superpowers/plans/2026-06-30-remote-experiments-implementation.md.