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Istio

Istio Multi-Cluster Scale Test — Clean Pass Report

Date: 2026-06-04 Istio: v1.28.5 · Kubernetes: v1.34.6 (all spokes) · Harness: a4b8a18 Topology: 1 hub (rosa-001, no mesh) + 10 mesh spokes (rosa-002 … rosa-011), multi-primary / multi-network. rosa-002 is the source/primary; mesh size N = rosa-002 plus N−1 remotes. istiod: pinned 3 replicas/spoke (autoscaleEnabled=false), no istiod HPA. CPU request 250m (O5 fix), mem req 2Gi / limit 8Gi.

Status: 4 of 5 suites complete and clean. The 5th (churn-dataplane) is re-running on a harness fix (see §6); partial results through mesh-size 3 are clean. This report will be finalized when that sweep completes.


1. Scale envelope (what was exercised)

Dimension Range exercised
Mesh size (clusters) 1 → 10
istiod replicas / spoke 3 (pinned)
Connected proxies (control-plane suite, max) 20
Connected proxies (churn suite, source / remote max) 36 / 216
Services in registry 10 per cluster
Sidecar scoping modes none, namespace, explicit (Sidecar API)
Data-plane load 10 / 100 / 500 / 1000 QPS, 8 connections
Churn intensity scale 1↔5 replicas across 5 deployments; rates 1 / 5 / 10 ops/s
Pods scheduled (control-plane suite, max) 136 / 750 allocatable (~18%)

Headline reads:

  • Control plane is comfortable at 10 clusters — istiod CPU peaks ~0.4 cores, heap ~110–190 Mi, 0 restarts across all 30 combinations.
  • Sidecar scoping cuts per-proxy config ~88–89% (3.8 MB → 0.4–0.5 MB), stable across mesh size.
  • Cross-cluster data-plane overhead is ~1 ms at p50 (local ~2.4 ms vs remote ~3.5 ms) and 100% success (pct_200 = 1.0) at every QPS level up to 1000.
  • Config/endpoint propagation to remote sidecars is the dominant latency (tens of seconds at mesh 10) — expected for full-mesh EDS fan-out, and the main scaling signal.

2. Configuration / Endpoint Propagation

Measures time for a new endpoint to propagate: P1 (local xDS push), P2 (remote istiod EDS), P3 (remote sidecar apply). Pre-warm + label-flip topology (O1 fix) so P3 measures propagation, not pod boot.

Parameters: source rosa-002; mesh 1→10; iterations 10/size; poll 250 ms; timeout 120 s; settle 5 s; backer hashicorp/http-echo:1.0. Sweep 20260604T024318Z-108594510/10 sizes, 0 errors (~1h47m).

Mesh P1 local xDS p50 / p99 (ms) P2 remote istiod EDS p50 / p99 (ms) P3 remote sidecar p50 / p99 (ms)
1 2027 / 2106 — (no remote) — (no remote)
2 2013 / 2072 2012 / 2079 18215 / 46424
10 2043 / 2296 2106 / 2533 13243 / 62909

Read: local xDS push is flat ~2.0–2.3 s regardless of mesh size; remote istiod EDS ~2.1 s; the long pole is remote sidecar application (p50 ~13–18 s, p99 up to ~63 s at mesh 10) as the endpoint fans out across all clusters. P1 histogram-convergence rows report 0-100 ms where valid. No TIMEOUT_P3, no skew outliers (improvement over the prior workaround pass).

On the P3 magnitude: these seconds-to-tens-of-seconds values are real (raw per-iteration P3 at mesh 10 ranges ~2 s to ~40 s, varying by remote cluster within a single iteration), not an aggregation artifact — verified against the raw TSVs. P3 is the dominant scaling signal and the high variance is the thing most worth scrutinizing in deeper analysis; the harness is reporting it faithfully. The non-monotonicity between mesh sizes (e.g. mesh-2 avg > mesh-10 avg) is sample-size driven — mesh 2 has n=10 vs mesh 10's n=90.


3. Service / Endpoint Churn Convergence

Measures istiod convergence and push amplification while 5 deployments scale 1↔5. Parameters: mesh 1→10; 5 deployments; scale 1→5; 5 iterations/size; churn intensity 5; istiod 3 replicas. Sweep 20260604T043213Z-146575410/10 stages, 0 errors.

Mesh local_avg (ms) remote_reach_avg (ms) remote_eds_avg (ms) src→rmt pushes src / rmt proxies amplification
1 2442 98 / 0 33 / 0 0.1
2 2558 7817 2989 328 / 515 36 / 28 0.5
3 2836 8224 4303 362 / 1049 24 / 48 1.1
4 2855 9360 4839 407 / 1395 24 / 72 0.8
5 3101 9629 4849 391 / 1959 24 / 96 0.4
6 3077 10486 4775 442 / 2291 24 / 120 0.5
7 3270 10531 4915 293 / 1828 24 / 144 0.3
8 3483 11993 4172 358 / 2670 24 / 168 0.4
9 3326 10179 3529 290 / 2632 24 / 192 0.6
10 3682 11154 3913 328 / 2980 24 / 216 0.3

Read: local convergence grows gently (~2.4 → 3.7 s) with mesh size; remote reach plateaus ~10–12 s; push amplification stays ≤ ~1.1 (no push storm). Remote proxy count scales linearly (24 → 216 = 24/cluster × remotes), confirming correct full-mesh wiring. Queue p99 0-100 ms throughout.


4. Control-Plane Resource Scaling

istiod CPU/mem/heap and config-dump size across mesh size × sidecar scoping. Parameters: mesh 1→10; 10 services; 3 replicas; 1 namespace; scopings none/namespace/explicit; 3 config-dump samples; settle 60 s. Sweep 20260604T072535Z-5166530/30 combos, 0 FailedScheduling, 0 restarts (O5 fix validated at full 10×3).

Mesh Scoping istiod CPU avg (m) mem avg (Mi) heap inuse (Mi) conv p99 proxies cfg dump (MB)
1 none / ns / explicit 139 / 95 / 83 354 / 329 / 329 187 / 154 / 169 ≤500 / ≤100 / ≤100 11 3.8 / 0.4 / 0.4
5 none / ns / explicit 271 / 173 / 195 350 / 326 / 332 179 / 175 / 169 ≤500 / ≤100 / ≤100 11 3.8 / 0.5 / 0.5
10 none / ns / explicit 402 / 310 / 313 354 / 340 / 344 158 / 166 / 157 ≤500 / ≤100 / ≤100 11 3.9 / 0.5 / 0.5

Sidecar-scoping effect on per-proxy config size (lower is better):

Mesh none (MB) namespace (MB) explicit (MB) reduction
1 3.8 0.4 0.4 89.3%
5 3.8 0.5 0.5 88.2%
10 3.9 0.5 0.5 87.0%

Read: istiod CPU rises modestly with mesh size and is highest with no scoping (402 m at mesh 10) vs ~310 m with scoping; memory is flat ~330–354 Mi; heap inuse ~150–190 Mi. Scoping sidecars cuts per-proxy config ~87–89% and trims istiod CPU. Convergence p99 stays in the 0–500 ms band; 0 istiod restarts across all 30 combos. (Capacity-percentage fields read unknown for this sweep — a provenance gap fixed under #45/PL36, not a measurement failure.)


5. Data-Plane Latency

fortio request latency, local vs cross-cluster (remote) targets. Parameters: mesh 1→10; QPS 10/100/500/1000; 8 connections; 30 s/level; 5 s warm-up; settle 30 s; fortio/fortio:1.69.5. Sweep 20260604T114908Z-83111610×4 QPS, pct_200 = 100% everywhere.

Representative p50 / p99 (ms), local vs remote:

Mesh QPS local p50 / p99 remote p50 / p99
2 1000 1.97 / 2.99 3.17 / 4.78
5 1000 2.24 / 3.00 2.90 / 9.18
10 10 3.42 / 17.51 3.75 / 6.98
10 100 2.77 / 4.61 3.57 / 6.10
10 500 2.43 / 5.79 3.02 / 5.65
10 1000 2.37 / 3.06 2.77 / 6.36

Read: cross-cluster adds ~0.5–1.0 ms at p50 (local ~2.0–2.8 ms vs remote ~2.8–3.6 ms), flat across mesh 1→10. p99 stays single-digit ms at scale; the occasional higher local p99 at 10 QPS is small-sample tail noise (n=1). Every level achieved 100% success (pct_200 = 1.0) and full target QPS.


6. Churn Under Data-Plane Load (re-running on harness fix — partial)

Measures added data-plane tail latency (Δp99) while the mesh churns under live fortio load. Parameters: mesh 1→10; churn rates 1/5/10 ops/s; 5 deployments; scale 1→5; QPS 200; 8 connections; baseline 60 s + churn 60 s/combo. Sweep 20260604T170554Z-2030208 (in progress).

6a. Harness defect found & fixed (PR #50)

The original run (…1174742) lost ~40% of its matrix to a setup/teardown race introduced by the O8 deploy-once-per-mesh-size refactor: per-mesh-size namespace teardown (fortio + sidecar-injected churn pods) exceeded its 180 s budget → CLEANUP_TIMEOUT; the next mesh-size's setup then collided with the still-Terminating namespace → SETUP_FAILED for all its combos. The failure path absorbed the remaining teardown, so the following mesh-size recovered — a deterministic odd-✓ / even-✗ oscillation (mesh sizes 2, 4, 6 lost entirely).

Fix: (A) setup now waits out any pre-existing Terminating namespace before applying (delay, not destroy); (B) cleanup fast-drains sidecar pods (5 s grace) before deleting the namespace; shared timeout 180 → 240 s. Reviewed by the 7-agent scale-test review — all six reviewers approved, round 1, zero blocking findings; lesson captured as PL37. Validated live: the always-failing even mesh-sizes now pass; fast-drain handled every teardown, with the setup-wait as the safety net for rare slow-finalize cases.

6b. Partial results (clean through mesh-size 3)

Mesh Churn rate baseline p50 / p99 (ms) churn p50 / p99 (ms) Δp99 (ms) xDS pushes Status
1 1 2.33 / 3.54 2.71 / 19.53 15.99 7117 OK
1 5 2.32 / 3.49 2.72 / 19.85 16.36 40282 OK
1 10 2.20 / 3.00 2.94 / 19.80 16.80 84026 OK
2 1 2.33 / 3.95 3.09 / 21.63 17.67 8047 OK
2 5 2.35 / 3.95 2.99 / 19.21 15.26 49886 OK
2 10 2.43 / 3.99 3.05 / 24.93 20.94 101956 OK
3 1 2.35 / 3.98 2.92 / 17.46 13.49 9101 OK
3 5 2.40 / 4.15 3.20 / 19.91 15.77 48347 OK
3 10 2.46 / 3.98 3.24 / 20.07 16.09 99…k OK

(Δp99 = churn p99 − baseline p99, the harness's churn-sensitivity metric; verified = churn_p99 − baseline_p99 on every combo. Mesh-sizes 4–10 pending.)

Read so far: idle p99 ~3–4 ms; under continuous churn p99 rises to ~17–25 ms, i.e. Δp99 ~13–21 ms of added tail latency, flat across mesh size and churn rate; p50 barely moves (~2.3 → 3.2 ms). xDS push volume scales cleanly with churn rate (~7 k at rate 1 → ~100 k at rate 10). 0 failures, 0 restarts across all completed combos — the fix is holding at full scale.


7. Campaign status

# Suite Sweep Result
1 Configuration / endpoint propagation …024318Z-1085945 ✅ 10/10 sizes, 0 errors
2 Service/endpoint churn convergence …043213Z-1465754 ✅ 10/10 stages, 0 errors
3 Control-plane resource scaling …072535Z-51665 ✅ 30/30 combos, 0 restarts
4 Data-plane latency …114908Z-831116 ✅ 10×4 QPS, pct_200 = 100%
5 Churn under data-plane load …170554Z-2030208 🔄 re-running on PR #50 fix — clean through mesh 3

Notable fixes applied this campaign: O5 (istiod CPU request, unblocks full 10×3 control-plane scheduling) · O1 (propagation pre-warm so P3 measures propagation, not boot) · O10 / PR #50 (churn-dataplane cleanup-cascade).


Generated 2026-06-04 from the per-suite sweep summaries under tests/<suite>/results/. Cluster Kubernetes version v1.34.6 on all spokes; Istio v1.28.5; harness commit a4b8a18.

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