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Emergence-as-Code for Self-Governing Reliable Systems

Anatoly A. Krasnovsky

TL;DR

End-to-end journey reliability in microservice systems is emergent and cannot be guaranteed by per-service SLOs alone. EmaC provides a declarative framework that links journey intent with runtime evidence to synthesize a probabilistic journey model, derives bounded SLOs and budgets, and emits governance artifacts for automated and reviewable control. The approach explicitly handles shared-fate, control-flow, and tail-latency effects by computing intervals $[A^-,A^+]$ and distribution-based tail metrics, while integrating with GitOps, Prometheus, and progressive delivery tools. This enables auditable, constrained automation within a MAPE-K feedback loop, aligning reliability with velocity and supporting a principled evolution of complex cloud-native systems.

Abstract

SLO-as-code has made per-service} reliability declarative, but user experience is defined by journeys whose reliability is an emergent property of microservice topology, routing, redundancy, timeouts/fallbacks, shared failure domains, and tail amplification. As a result, journey objectives (e.g., "checkout p99 < 400 ms") are often maintained outside code and drift as the system evolves, forcing teams to either miss user expectations or over-provision and gate releases with ad-hoc heuristics. We propose Emergence-as-Code (EmaC), a vision for making journey reliability computable and governable via intent plus evidence. An EmaC spec declares journey intent (objective, control-flow operators, allowed actions) and binds it to atomic SLOs and telemetry. A runtime inference component consumes operational artifacts (e.g., tracing and traffic configuration) to synthesize a candidate journey model with provenance and confidence. From the last accepted model, the EmaC compiler/controller derives bounded journey SLOs and budgets under explicit correlation assumptions (optimistic independence vs. pessimistic shared fate), and emits control-plane artifacts (burn-rate alerts, rollout gates, action guards) that are reviewable in a Git workflow. An anonymized artifact repository provides a runnable example specification and generated outputs.

Emergence-as-Code for Self-Governing Reliable Systems

TL;DR

End-to-end journey reliability in microservice systems is emergent and cannot be guaranteed by per-service SLOs alone. EmaC provides a declarative framework that links journey intent with runtime evidence to synthesize a probabilistic journey model, derives bounded SLOs and budgets, and emits governance artifacts for automated and reviewable control. The approach explicitly handles shared-fate, control-flow, and tail-latency effects by computing intervals and distribution-based tail metrics, while integrating with GitOps, Prometheus, and progressive delivery tools. This enables auditable, constrained automation within a MAPE-K feedback loop, aligning reliability with velocity and supporting a principled evolution of complex cloud-native systems.

Abstract

SLO-as-code has made per-service} reliability declarative, but user experience is defined by journeys whose reliability is an emergent property of microservice topology, routing, redundancy, timeouts/fallbacks, shared failure domains, and tail amplification. As a result, journey objectives (e.g., "checkout p99 < 400 ms") are often maintained outside code and drift as the system evolves, forcing teams to either miss user expectations or over-provision and gate releases with ad-hoc heuristics. We propose Emergence-as-Code (EmaC), a vision for making journey reliability computable and governable via intent plus evidence. An EmaC spec declares journey intent (objective, control-flow operators, allowed actions) and binds it to atomic SLOs and telemetry. A runtime inference component consumes operational artifacts (e.g., tracing and traffic configuration) to synthesize a candidate journey model with provenance and confidence. From the last accepted model, the EmaC compiler/controller derives bounded journey SLOs and budgets under explicit correlation assumptions (optimistic independence vs. pessimistic shared fate), and emits control-plane artifacts (burn-rate alerts, rollout gates, action guards) that are reviewable in a Git workflow. An anonymized artifact repository provides a runnable example specification and generated outputs.
Paper Structure (20 sections, 2 equations, 1 figure, 1 table)

This paper contains 20 sections, 2 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Problem: Local SLOs, Global Journeys
  3. The "sync tax" of journey SLOs
  4. The nature of emergence
  5. Vision: Governing Emergence as Code
  6. What is EmaC?
  7. Example.
  8. Design principles
  9. Minimal operator set
  10. Computing Emergent Journey SLOs
  11. Availability with shared fate
  12. Tail-latency composition from distributions
  13. From Computation to Self-Governance
  14. Why computation alone is not enough
  15. Compilation targets
  16. ...and 5 more sections

Figures (1)

  • Figure 1: EmaC compiles versioned intent and an evidence-backed (inferred) journey model into bounded journey SLOs, budgets, and governance artifacts; runtime evidence continuously triggers re-inference and proposed deltas.