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Both algorithms were evaluated under identical synthetic network conditions to measure load distribution, failure isolation, and operational overhead.
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@@ -13,36 +13,37 @@ Both algorithms were evaluated under identical synthetic network conditions to m
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### MOLS (Mutually Orthogonal Latin Squares)
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-**Concept**: Arranges relays and clients into an $N \times N$ discrete grid where $N$ is the candidate pool size. Multipliers $(m_1, m_2)$ coprime to $N$ form two orthogonal Latin squares to assign coordinates to relays.
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-**Design Intent**: Seeks mathematically exact uniform dispersion across both primary and secondary candidate slots, minimizing initial load variance across static topologies.
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-**Hypothesis**: Given a known, relatively stable cluster, combinatorial orthogonality provides provable deterministic spreading without relying on probabilistic hashing balance.
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-**Reshuffle Defense Mechanism**: To prevent the $\approx 81\%$ reshuffle storm when $N$ changes ($N \to N-1$), MOLS relies on **resilient active-connection stickiness** in `applyActiveStickiness`. Established healthy listener connections are retained in priority order, cutting unaffected client churn to 0% as long as nodes remain healthy and unpressured.
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### HRW (Highest Random Weight / Rendezvous Hashing)
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-**Concept**: Evaluates an independent 64-bit pseudo-random weight function $W(c, r) = \text{hash}(c \mathbin{\Vert} r)$ for every client-relay pair and sorts descending.
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-**Design Intent**: Prioritizes monotonicity (minimal disruption) and robustness in asynchronous, gossip-based discovery environments where candidate pools change dynamically.
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-**Hypothesis**: A stateless volunteer network experiences frequent membership changes ($N \to N-1$). Monotonicity ($1/N$ migration) prevents global reconnection storms and provides robust degradation regardless of whether nodes share a consistent view of $N$.
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-**Design Intent**: Prioritizes intrinsic monotonicity (minimal disruption) and robustness in asynchronous, gossip-based discovery environments where candidate pools change dynamically.
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-**Hypothesis**: A stateless volunteer network experiences frequent membership changes ($N \to N-1$). Intrinsic monotonicity ($1/N$ migration) prevents global reconnection storms mathematically without requiring stateful connection-tracking stickiness or identical cluster views across clients.
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---
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## 2. Empirical Benchmark Results
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Evaluated across 700 synthetic clients on 7 relays ($N = 7$), as well as even non-prime orders ($N = 6$):
### A. Stability Under Dynamic Topology ($N \to N \pm 1$)
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-**MOLS**: Because grid coordinates are computed modulo $N$, adding or removing a single relay shifts the entire coordinate space. Over $80\%$ of unaffected clients change their primary relay, causing widespread connection churn.
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-**HRW**: Guarantees the **monotonicity property**. When relay $k$ drops, only clients connected to $k$ migrate to their respective second choices. Unaffected clients experience **0% churn**.
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### A. Churn Storm Mitigation: Intrinsic Monotonicity vs. Stateful Stickiness
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-**MOLS**: Without stateful connection memory, MOLS intrinsically reshuffles $\approx 81\%$ of traffic upon pool size changes. With resilient active stickiness, re-routed traffic drops to 0.0% for existing connections, but newly joining clients or re-dialing clients will still compute disjoint grid placements.
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-**HRW**: Achieves 0.0% churn **intrinsically and statelessly**. Even without passing `ActiveRelayURLs`, the mathematical mapping guarantees minimal disruption ($1/N$).
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### B. Gossip Discovery & Eventual Consistency
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-**MOLS**: Requires all participants to share an identical view of $N$ and relay sorting order. If client A discovers 10 relays and client B discovers 9 relays, their coordinate frames diverge completely.
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