ARRC: Explainable, Workflow-Integrated Recommender for Sustainable Resource Optimization Across the Edge-Cloud Continuum

TL;DR

This paper introduces ARRC, an explainable recommender system that integrates into operator workflows to optimize resources across edge-cloud systems, significantly reducing workload and improving utilization while maintaining transparency and ease of maintenance.

Contribution

The paper presents ARRC, a novel, explainable, workflow-integrated recommender system for resource optimization that enhances maintainability and transparency in multi-tenant edge-cloud environments.

Findings

Reduces operator workload by over 50%

Improves compute utilization up to 7.7x

Maintains error rates below 5%

Abstract

Achieving sustainable, explainable, and maintainable automation for resource optimization is a core challenge across the edge-cloud continuum. Persistent overprovisioning and operational complexity often stem from heterogeneous platforms and layered abstractions, while systems lacking explainability and maintainability become fragile, impede safe recovery, and accumulate technical debt. Existing solutions are frequently reactive, limited to single abstraction layers, or require intrusive platform changes, leaving efficiency and maintainability gains unrealized. This paper addresses safe, transparent, and low-effort resource optimization in dynamic, multi-tenant edge-cloud systems, without disrupting operator workflows or increasing technical debt. We introduce ARRC, a recommender system rooted in software engineering design principles, which delivers explainable, cross-layer resource recommendations directly into operator workflows (such as tickets and GitOps pull requests). ARRC encapsulates optimization logic in specialized, auditable agents coordinated via a shared interface, supporting maintainability and extensibility through transparency and the ability to inspect both recommendations and their rationale. Empirical evaluation in a multi-region industrial deployment shows that ARRC reduces operator workload by over 50%, improves compute utilization by up to 7.7x, and maintains error rates below 5%, with most benefits achieved through incremental, operator-approved changes. This demonstrates that explainable, recommendation-based architectures can achieve sustainable efficiency and maintainability improvements at production scale. ARRC provides an empirically evaluated framework for integrating explainable, workflow-driven automation into resource management, intended to advance best practices for robust, maintainable, and transparent edge-cloud continuum platforms.