🤖 AI Summary
This work addresses the challenge of modeling and governing user journey reliability in microservice systems, an emergent property that existing SLO-as-code approaches fail to capture effectively, leading to misalignment between intended objectives and actual system behavior. To bridge this gap, the paper introduces Emergence-as-Code (EmaC), a novel framework that formalizes journey reliability as declarative, verifiable, and executable code specifications. By integrating distributed tracing, traffic analysis, and runtime model synthesis, EmaC automatically infers journey-level SLOs and their error budgets, then leverages an SLO compiler to generate corresponding control-plane artifacts. Embedded within GitOps workflows, EmaC enables auditable, autonomous governance mechanisms—such as burn rate alerts and release gates—to achieve computable, closed-loop management of end-to-end reliability.
📝 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.