CRAWO：用于自适应工作负载编排的定制资源

中文摘要 AI 辅助

边缘智能通过将计算从集中式云数据中心转移到网络边缘，已成为在智慧城市中实现实时应用的关键范例，可减少延迟和带宽消耗。然而，在异构边缘基础设施上部署人工智能（AI）管道仍具挑战性。现有边缘编排平台主要关注部署自动化和基础设施管理，但效率低下且限制动态条件下自适应分配资源的能力。本文介绍了CRAWO，一种用于在分布式边缘环境中协调AI管道的架构框架。CRAWO采用基于控制回路的模型，通过管理放置决策、状态管理和阶段间数据流，同时在边缘节点上实例化服务，将分配智能与执行分离。该框架结合了硬件感知分配器和可插拔多标准决策层，利用实时基础设施指标实现自适应工作负载放置。在车辆监控场景中使用车牌识别进行评估，结果表明在对延迟敏感的环境中，工作负载分布得到改善，对集中式云处理的依赖减少。

英文摘要

Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data centers to the network edge, thereby reducing latency and bandwidth consumption. However, deploying Artificial Intelligence (AI) pipelines across heterogeneous edge infrastructures remains challenging due to the wide range of device capabilities, from low-power microcontrollers to accelerator-equipped systems. Existing edge orchestration platforms primarily focus on deployment automation and infrastructure management, but these approaches are often inefficient and limit the ability to adaptively allocate resources under dynamic conditions. To tackle these issues, this paper introduces CRAWO (Custom Resources for Adaptive Workload Orchestration), an architectural framework for coordinating AI pipelines across distributed edge environments. CRAWO follows a control-loop-based model that separates allocation intelligence from execution by managing placement decisions, state management, and inter-stage data flows while instantiating services on edge nodes. The framework incorporates a hardware-aware allocator with a pluggable multi-criteria decision layer that leverages real-time infrastructure metrics to enable adaptive workload placement. The reference implementation adopts a microservices architecture deployed on a lightweight Kubernetes distribution (K3s), using Custom Resource Definitions (CRDs) for domain modeling and a dedicated operator for state reconciliation. Evaluation in a vehicle surveillance scenario using license plate recognition demonstrates improved workload distribution and reduced reliance on centralized cloud processing in latency-sensitive environments.

发表机构

• Federal University of Rio Grande do Norte（巴西里约热内卢州联邦大学）
• IMD（信息管理与决策研究所）
• DIMAP（应用数学与统计系）

机构由 AI 辅助整理，请以论文原文为准。