标题： ARRC：面向边缘-云连续体的可解释、工作流集成可持续资源优化推荐器 显示英文标题

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

摘要： 在边缘-云连续体中实现可持续、可解释和可维护的资源优化是核心挑战。 持续的过度配置和操作复杂性通常源于异构平台和分层抽象，而缺乏可解释性和可维护性的系统变得脆弱，阻碍安全恢复并积累技术债务。 现有解决方案通常是被动的，仅限于单一抽象层，或需要侵入性平台更改，导致效率和可维护性收益无法实现。 本文解决了动态、多租户边缘-云系统中的安全、透明且低努力的资源优化问题，而不会干扰操作员工作流程或增加技术债务。 我们引入了ARRC，这是一个基于软件工程设计原则的推荐系统，可将可解释的跨层资源建议直接集成到操作员工作流程中（如工单和GitOps拉取请求）。 ARRC通过共享接口协调的专业化、可审计代理封装优化逻辑，通过透明度以及检查建议及其依据的能力来支持可维护性和可扩展性。 在多区域工业部署中的实证评估表明，ARRC使操作员工作量减少了50%以上，计算利用率提高了7.7倍，并将错误率保持在5%以下，大部分好处通过渐进的、操作员批准的更改实现。 这表明，基于推荐的可解释架构可以在生产规模上实现可持续的效率和可维护性改进。 ARRC提供了一个经过实证评估的框架，用于将可解释的、工作流驱动的自动化集成到资源管理中，旨在推动稳健、可维护和透明的边缘-云连续体平台的最佳实践。 显示英文摘要

摘要： 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.