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Chapter 14 — Runtime and Streaming Corrections (Caching / Fallback / Dashboards)


One-Sentence Goal
On an execution graph G = (V, E), produce T_corr and manifest.path.* in a streaming, low-latency, and auditable manner, using caching / fallback / alerting and dashboard SLIs to uphold business SLOs.


I. Scope and Objects

  1. Inputs
    • Execution graph and operators: V = { geom, env, iono, tropo, fiber, ray, integ, inst, u, compose, emit }; edges E encode data dependencies.
    • Streaming data: obs(t), met_3D(t,x), TEC(t,lat,lon), temp_profile(t), Sparams(f), cfg(ver).
    • Runtime policy: policy = { cache, window, fallback, alarm, SLO }; time base tau_mono and publication clock ts.
  2. Outputs
    • Streaming path-correction results T_corr(t) and components T_parts(t).
    • manifest.path and runtime-derived keys manifest.path.rt.
    • Dashboard metrics and audit logs.
  3. Constraints & Boundaries
    Parallel dual-form evaluation with traceable RefCond; extrapolation is forbidden; every node obeys its invariants (see prior chapters and unified notation).

II. Terms and Variables


III. Axioms P814-*


IV. Minimal Equations S814-*


V. Metrological Workflow M80-14


VI. Contracts and Assertions (C80-141x)


VII. Implementation Bindings I80-*


VIII. Cross-References


IX. Quality and Risk Control

  1. Suggested SLO / SLI Targets
    • SLI_latency = p95(L_rt): free-space links ≤ 1.0 s, indoor fiber ≤ 0.2 s.
    • SLI_freshness = p95( max(age(src)) ) ≤ Delta_t.
    • SLI_delta_form = p99(delta_form) ≤ tol_Tarr.
    • SLI_cache_hit_L1 ≥ 0.8, SLI_cache_hit_total ≥ 0.95.
    • SLI_u = p95(U) ≤ U_target (see Chapter 13).
  2. Risk Scenarios & Actions
    • Source outage: raise fallback_level, shrink W_t, enlarge TTL, tag degraded.source.
    • Integration instability: densify adaptive grids, add step-size guards, or fall back to empirical models.
    • Jitter spikes: weighted smoothing, delayed publish, trigger re-calibration (see Chapter 12).
  3. Audit & Rollback
    Log all alarm/degrade/fallback events; provide trace-level replay scripts and a “shadow publish” channel.

Summary
This chapter defines a streaming mechanism for path corrections on G = (V, E): event-time alignment, tiered caching, a fallback matrix, online dual-form monitoring, and dashboard alerting.
Minimal key set:
manifest.path.rt = { trace_id, ts, L_rt, cache:{ L1,L2,L3,hits,TTL }, wm, lateness, window:{ W_t,W_x }, fallback:{ level,alpha_r,tags }, contracts:{ freshness,cov,delta_form }, SLI:{ latency,hit,freshness }, seeds, inputs.hash }.
Together with Chapters 10–13, the system maintains low-latency, traceable, and compliant publication of T_corr under resource and data perturbations.


Copyright & License (CC BY 4.0)

Copyright: Unless otherwise noted, the copyright of “Energy Filament Theory” (text, charts, illustrations, symbols, and formulas) belongs to the author “Guanglin Tu”.
License: This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You may copy, redistribute, excerpt, adapt, and share for commercial or non‑commercial purposes with proper attribution.
Suggested attribution: Author: “Guanglin Tu”; Work: “Energy Filament Theory”; Source: energyfilament.org; License: CC BY 4.0.

First published: 2025-11-11|Current version:v5.1
License link:https://creativecommons.org/licenses/by/4.0/