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Uncorrelated churn alone was incomplete: real outages take out a datacentre, AS or region as a unit. Adds failure domains and a correlated churn mode, plus the metric needed to tell the two apart. - n_regions / region_locality: nodes belong to equal-sized failure domains, and a configurable share of each node peers inside its own domain. Locality is what makes a failure domain a connectivity domain -- with region-blind peering, dropping whole regions removes a uniformly random set of nodes and is indistinguishable from uniform churn. The locality matchings keep the graph exactly d-regular (they change where peers are, never how many). - churn_mode = uniform | regional, swept per topology so both modes are compared on the same graph at an identical dead-node count. - frac_reached_live: coverage of the *responsive* network, alongside coverage of all nodes. The two move in opposite directions under correlated failure, so one number could not express the result. Measured (degree 4, 20 domains, 75% locality, half the network dead): clustered failure leaves the survivors fully connected -- live coverage 1.000 and delivery equal to the live-relay rate, i.e. nothing lost to routing -- where the same number of scattered failures gives 0.857 live coverage and loses delivery to broken routes. Correlated outages are gentler on the survivors than uniform churn, while stranding the dead domains. Verify check 8 anchors this. Also, per review of the caveats: exact d-regularity is a protocol requirement rather than a modelling simplification, and the timing-correlation adversary is deferred because it is only meaningful once the network emits cover traffic, which this simulator does not yet do. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
import dataclasses
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import pytest
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from pd.config import SimConfig, SweepConfig
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def test_key_covers_every_field():
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fields = [f.name for f in dataclasses.fields(SimConfig)]
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assert len(SimConfig().key()) == len(fields)
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# n_regions=2 in the base so the region/churn fields can each be varied on their own
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# (region_locality and churn_mode="regional" both require n_regions >= 2)
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base = SimConfig(n_regions=2)
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for name in fields:
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cur = getattr(base, name)
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alt = {"n_nodes": 2000, "degree": 4, "n_regions": 4, "region_locality": 0.5,
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"blend_hops": 2, "max_blend_delay": 5,
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"unresponsive_frac": 0.2, "churn_mode": "regional", "redundancy": 2,
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"n_rounds": 10, "transport_jitter_mean_ms": 1.0,
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"processing_lags_ms": (11.0, 51.0, 101.0), "processing_lag_probs": (0.6, 0.3, 0.1),
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"link_latency_dist": "fixed", "link_latency_mean_ms": 1.0,
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"coverage_pcts": (25.0,), "f_adv": 0.1, "adversary_mode": "worstcase_coverage",
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"n_placements": 1, "worstcase_max_n": 5, "graph_seed": 99, "replicate": 1,
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"root_seed": 7}[name]
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assert alt != cur
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assert dataclasses.replace(base, **{name: alt}).key() != base.key(), name
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@pytest.mark.parametrize("kw", [
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{"n_nodes": 999}, # odd
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{"degree": 1000}, # >= n
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{"blend_hops": 0}, # < 1
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{"f_adv": 1.0}, # >= 1
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{"max_blend_delay": -1},
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{"processing_lag_probs": (0.5, 0.4)}, # doesn't sum to 1 (with default 3 lags -> len mismatch)
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{"link_latency_dist": "bogus"},
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{"adversary_mode": "bogus"},
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])
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def test_validation_rejects(kw):
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with pytest.raises(ValueError):
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SimConfig(**kw)
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def test_sweep_grids_and_collapse():
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sw = SweepConfig(n_nodes=[1000, 10000], degree=[4, 8], blend_hops=[2, 3],
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max_blend_delay=[0, 3], f_adv=[0.0, 0.2],
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adversary_mode=["random", "worstcase_coverage"], seeds=3)
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assert len(sw.graph_cells()) == 2 * 2 * 3
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assert len(sw.prop_grid()) == 2 * 2
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# f_adv=0 collapses to a single (mode-irrelevant) row; f_adv=0.2 keeps both modes
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assert sw.adv_grid() == [(0.0, "random"), (0.2, "random"), (0.2, "worstcase_coverage")]
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def test_from_dict_rejects_unknown():
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with pytest.raises(ValueError):
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SweepConfig.from_dict({"nonsense": [1]})
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def test_base_config_coerces_tuples():
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sw = SweepConfig(base={"processing_lags_ms": [10.0, 90.0], "processing_lag_probs": [0.3, 0.7]})
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cfg = sw.base_config(1000, 8, 0)
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assert cfg.processing_lags_ms == (10.0, 90.0)
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assert isinstance(cfg.key(), tuple)
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