Cloud-to-Cloud Integration Migration Accelerator: A Schema-Agnostic Translation Engine for Workload Migration Across Heterogeneous iPaaS Platforms

Abstract

Enterprises that move an integration workload from one iPaaS platform to another routinely discover that the effort resembles a rewrite more than a configuration change. Workflow structure, connector behavior, transformation syntax, and error-handling semantics rarely line up between platforms such as Dell Boomi, MuleSoft, and AWS Step Functions, and a migration team is left reconstructing flows by hand, one at a time. This paper proposes a schema-agnostic migration accelerator built around an Integration Intermediate Representation (IR) that records a flow's structure, data contracts, and failure behavior in a platform-neutral graph before target-platform artifacts are generated from it. The approach deliberately separates predictable structural and transformation mappings from constructs that require engineering review, custom connectors, embedded code, and platform-specific runtime behavior and produces a remediation report for the latter instead of forcing a silent, possibly incorrect conversion. We evaluate the approach using a reproducible, explicitly simulated benchmark on a representative synthetic corpus of 419 flow components across seven categories, achieving an aggregate automation rate of 68.5% and an estimated effort reduction of 69.9% compared to fully manual reconstruction, with automation focused on routing, simple transformation, and subprocess-reference constructs, and concentrated failure in custom connectors and parallel branches. We report these findings as an illustrative proof of methodology, not a production result, and outline the real-world pilot required before the accelerator can be deployed against a live migration.