LLM-Driven Intent-Based Privacy-Aware Orchestration Across the Cloud - Edge Continuum

Abstract

The rapid growth of edge-to-cloud applications has intensified the need to guarantee that sensitive data remains within trusted jurisdictions while still meeting performance targets. Existing orchestration stacks - Kubernetes for compute and software-defined networking (SDN) controllers such as ONOS for traffic steering - expose rich policy knobs, yet they still require experts to translate high-level privacy goals into dozens of low-level rules. This paper proposes and experimentally validates an LLM-driven, intent-based framework that enforces privacy requirements across both computing and networking layers of the cloud-edge continuum. A GPT-4o model interprets natural-language privacy int..