{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","cell_id":"invariant_mode_decomposition_design__computer_science","arm":"BREADTH_PROBE_ONE_SHOT","candidate_id":"invariant_mode_decomposition_design__computer_science__P1","proposal_index":1,"version":0,"title":"Modal Damping of Cross-Service Retry Storms","problem":"In a bounded microservice dependency chain, queue depth, retry rate, worker saturation, and tail latency can reinforce one another across services. Per-service dashboards may show tolerable values while a coupled, weakly damped or growing state direction is already forming, so responders adjust whichever component looks worst without identifying the combination that the system dynamics amplify.","actors":["Site-reliability engineers responsible for the dependency chain","Service owners controlling retry, timeout, concurrency, and backpressure settings","Incident commander authorizing production mitigations","Telemetry and staging-platform owners supporting offline analysis and replay"],"observable_state":"At fixed sampling intervals, the state vector contains normalized queue depth, retry-attempt rate, worker or connection-pool saturation, rejection rate, and tail latency for each service in one declared dependency subgraph. The observable signature is repeated co-movement after load fluctuations: individual metrics cross thresholds inconsistently, yet a similar weighted combination persists or grows over successive intervals.","consequence":"A latent amplification direction can develop into synchronized queue growth, retry multiplication, and latency escalation before coordinate-level alerts produce a coherent diagnosis, increasing the risk of availability loss and indiscriminate manual configuration changes.","affected_objective":"Maintain bounded latency and request completion in the selected dependency chain while avoiding retry-driven amplification and unnecessary loss of useful capacity.","intervention":"For one stable operating regime, estimate a local state-transition map from state at interval t to state at t+1, extract its approximately invariant modes, and rank them by modal gain and availability consequence. For any persistent or growing retry-saturation mode, run a modal sensitivity sweep over only reversible controls—retry budgets, backoff, admission limits, concurrency caps, and timeout relationships—to identify a coordinated configuration that reduces excitation or gain of that mode. Test the configuration with recorded-traffic replay or shadow execution before any separately approved canary. Retain reconstruction residuals, cross-mode coupling, and mode-drift triggers as explicit reasons not to act on the modal model.","structural_mapping":[{"archetype_element":"Transformation Scope","domain_realization":"The transformation is the one-sampling-interval evolution of telemetry for a declared microservice dependency subgraph under a fixed software version, topology, routing policy, and traffic regime."},{"archetype_element":"State-Vector Definition","domain_realization":"Coordinates are service-level queue, retry, saturation, rejection, and tail-latency measurements with documented normalization and direct links back to raw telemetry."},{"archetype_element":"Invariant Mode Basis","domain_realization":"Eigenvector-like directions represent recurring cross-service combinations, such as upstream retries increasing downstream saturation while downstream delay induces further upstream retries."},{"archetype_element":"Modal Gain Spectrum","domain_realization":"Each mode receives a scalar response estimate indicating decay, persistence, oscillation, or growth over one sampling interval, with uncertainty retained."},{"archetype_element":"Dominant Mode Selection Rule","domain_realization":"A mode becomes action-relevant only if it is reproducible across adjacent fitting windows, materially represented in observed incident trajectories, and associated with retry, saturation, or latency consequences."},{"archetype_element":"Stable/Unstable Mode Partition","domain_realization":"Modes are classified as damped, near-neutral, growing, oscillatory, or unresolved rather than treating overall system averages as the stability verdict."},{"archetype_element":"Modal Intervention Map","domain_realization":"A sensitivity matrix links reversible service controls to their projected influence on the selected mode, permitting coordinated changes across implicated services."},{"archetype_element":"Reconstruction Residual Check","domain_realization":"Held-out telemetry is reconstructed from retained modes; unexplained bursts, nonlinear transitions, and service-local anomalies remain visible as residuals."},{"archetype_element":"Mode Drift Monitor","domain_realization":"Topology, release, routing, workload-mix, and residual changes trigger re-estimation or retirement of the fitted modes."},{"archetype_element":"Interpretation Scope Contract","domain_realization":"The model is valid only for the named subgraph and measured operating window and cannot authorize changes under a different topology, release, or overload regime."},{"archetype_element":"Mode-Coupling Register","domain_realization":"Observed interactions among retained modes are recorded so damping one direction is not assumed to leave other failure directions unchanged."},{"archetype_element":"Spectral Gap Threshold","domain_realization":"Mode-specific action is withheld when the target mode is not sufficiently separated from neighboring estimated modes to support stable interpretation."}],"mechanism_mapping":[{"mechanism_slug":"eigendecomposition_workflow","role":"Decomposes the fitted state-transition map into cross-service directions and their interval-to-interval gains.","counterfactual_removal":"Without decomposition, the intervention reverts to adjusting named service metrics independently and cannot identify the coupled direction being amplified."},{"mechanism_slug":"modal_sensitivity_sweep","role":"Projects small, reversible changes in retry, backoff, concurrency, timeout, and admission controls onto the target mode and screens candidate coordinated configurations.","counterfactual_removal":"Without the sweep, detecting a growing mode would not identify which controllable combination is capable of damping it."},{"mechanism_slug":"residual_reconstruction_test","role":"Tests whether selected modes reproduce held-out transitions and exposes behavior outside the local modal approximation.","counterfactual_removal":"Without residual testing, a mathematically convenient fit could govern production despite omitting bursts, nonlinearities, or unmodeled dependencies."},{"mechanism_slug":"spectral_gap_monitor","role":"Checks whether the selected mode remains distinguishable enough from neighboring modes for its direction and gain to be operationally interpretable.","counterfactual_removal":"Without the gap check, small data changes could rotate an apparently precise target mode and produce unstable configuration advice."}],"causal_chain":["Fixed retry, timeout, concurrency, and backpressure rules couple the interval-to-interval evolution of queues, retries, saturation, and latency across services.","That coupled evolution can preserve and amplify a weighted cross-service direction even while no single coordinate consistently appears dominant.","Per-service alerting and remediation therefore select visible components rather than the active amplification direction.","A locally fitted transition map and eigendecomposition make the direction, scalar response, original-coordinate loadings, and uncertainty inspectable.","A modal sensitivity sweep identifies a reversible combination of controls projected to oppose the target retry-saturation mode.","Recorded-traffic replay or shadow execution tests whether that configuration reduces the mode's persistence without unacceptable residual growth or excitation of another mode.","Residual, spectral-gap, and drift checks determine whether the modal interpretation remains valid; failure of any check halts escalation to production."],"baseline":"Current handling uses per-service thresholds, distributed traces, and incident judgment: responders locate conspicuous latency, retry, or saturation metrics and alter one service at a time. This can diagnose discrete faults but provides no explicit estimate of a coupled state direction or its interval-to-interval gain.","nearest_rivals":["Per-service anomaly alerts: detect unusual coordinates but do not determine whether a weighted cross-service combination is invariant, damped, or growing under the transition.","Distributed tracing and dependency root-cause analysis: identify request paths and initiating faults but do not by themselves estimate the repeated system response or select controls by modal leverage.","Principal-component monitoring: finds high-variance combinations but, without a transition map and modal gain, does not distinguish variance from persistence or amplification.","Single-loop autoscaling or queue control: regulates a chosen metric directly but may miss a failure direction distributed across several services and controls."],"remaining_contrastive_claim":"Conditional on a locally predictive transition map and a separated, reproducible mode, the design selects coordinated service-control changes according to their projected effect on a cross-service amplification direction; the baseline and nearest rivals select changes from coordinate excursions, causal paths, variance, or predefined single-variable loops instead.","authority_safety":{"decision_authority":"Telemetry owners may approve offline fitting, and staging-platform owners may approve recorded-traffic replay. Only the incident commander together with affected service owners may authorize a production canary or configuration rollout through the existing change process.","authorized_first_step":"Read-only analysis of already captured telemetry from one declared dependency subgraph, followed only if permitted by a non-production recorded-traffic replay; no live traffic or production configuration is changed.","excluded_actions":["Automatic production configuration changes from modal estimates","Unapproved live perturbation or fault injection","Disabling retries, timeouts, admission controls, or safety limits globally","Applying modes fitted under one topology, release, or workload regime to another","Suppressing existing coordinate-level alerts or incident procedures","Using the model to override a service owner's rollback decision"],"halt_rollback":"Halt if reconstruction error exceeds the preset budget, the target lacks the preset spectral separation, its direction is unstable across adjacent windows, replay worsens request completion or tail latency, another mode becomes less stable, or topology or software changes. Discard the candidate configuration and return staging to its recorded baseline; any later canary must use the existing immediate configuration rollback path."},"negative_tests":{"strongest_counterevidence":"A service-local fault or exogenous traffic step explains the incident transitions, while a sparse single-service model predicts held-out telemetry as well as the coupled transition model and no reproducible growing or weakly damped cross-service mode appears.","problem_falsifier":"Across the selected incident and control windows, estimated cross-service modes fail reproducibility, do not precede or accompany retry-saturation escalation, or add no held-out predictive information beyond coordinate-wise persistence and known exogenous inputs.","intervention_falsifier":"In recorded-traffic replay, the sensitivity-selected configuration fails to reduce the target mode's persistence, or any apparent reduction is accompanied by larger residuals, worse request completion or latency, or increased gain in another safety-relevant mode.","risks":["A nonstationary workload or deployment change may make the local linear transition misleading.","Correlated telemetry may be mistaken for a controllable dynamical mode.","Hidden services or missing signals may shift important behavior into the residual.","Normalization and sampling choices may change estimated mode direction or gain.","A retry configuration that damps one mode may reduce legitimate recovery capacity or excite another mode.","Operators may over-trust mathematically precise outputs despite weak spectral separation or uncertain control sensitivity.","Replay fidelity may be insufficient to represent production feedback and saturation."]},"next_evidence_step":"Using one previously captured retry-escalation episode and one matched nonincident window from a single dependency subgraph, predeclare the state variables, sampling interval, local-validity window, residual budget, and spectral-gap rule. Fit the transition map on the first portion of each window and evaluate one-step reconstruction, mode direction, and modal gain on held-out portions and adjacent subwindows against coordinate-wise persistence and a sparse service-local model. If and only if one retry-saturation mode is reproducible and passes the residual and separation rules, derive one reversible configuration through a modal sensitivity sweep and run a bounded recorded-traffic replay comparing it with the unchanged configuration. Record request completion, tail latency, target-mode gain, other safety-relevant gains, and residuals; do not proceed to production in this evidence step.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"No comparison with prior proposals was performed because runtime isolation prohibits inspecting other experiment candidates; this one-shot candidate is independently grounded in cross-service retry dynamics.","revision_record":{"parent_version":null,"progress_targets_addressed":["Initial one-shot construction from the supplied archetype and domain card"],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}