在使用机器学习的雾计算环境中部署经验意识应用程序的质量
P Jenifer1, J Angela Jennifa Sujana2
1Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India.
PeerJ. Computer science
|September 24, 2025
概括
能源智能组件配置 (ESCP) 算法优化了边缘设备上的人工智能 (AI) 工作负载,提高了效率并降低了能源消耗. 这个框架确保了实时应用程序的服务质量和体验.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 边缘计算 边缘计算
背景情况:
- 边缘智能对于实时传感器数据处理至关重要,但面临带宽,延迟和数据隐私方面的挑战.
- 现有的解决方案难以有效地在资源有限的边缘设备上部署人工智能 (AI) 工作负载.
- 对于在云雾边缘环境中保证服务质量 (QoS) 和体验质量 (QoE) 的动态架构的需求正在增长.
研究的目的:
- 引入能源智能组件配置 (ESCP) 算法,以优化AI在雾边缘设备上的工作负载部署.
- 为雾边设备作为服务 (FEdaaS) 开发可靠和动态的架构,确保QoS和QoE.
- 提高人工智能边缘系统的能源效率和性能.
主要方法:
- 开发了用于雾设备 (FCMN,FN) 的能源智能组件放置 (ESCP) 算法,用于分配模块和禁用不活跃的设备.
- 实现了一个元启发式调度器,将 eXtreme Gradient Boosting (XGB) 结合起来,用于即时的 QoS 评分和长短期内存 (LSTM) 用于节点拥堵预测.
- 设计了一个框架,通过无服务器云,雾和极端边缘层透明地分配压缩的神经工作负载.
主要成果:
- 与仅使用云计算的基线相比,ESCP使带宽利用率提高了5.2%,可扩展性提高了3.2%,能源消耗提高了3.8%,响应时间提高了2.1%.
- 保持了0.4%的预测准确度,同时满足了QoE目标,例如为低资源AI边缘设备提供250ms的延迟和24小时的电池寿命.
- 通过自适应性框架演示了编排AI边缘设备的可行性,以满足严格的应用程序级 QoS 和 QoE 需求.
结论:
- ESCP算法和自适应框架有效优化边缘设备上的AI工作负载,提高性能和能源效率.
- 拟议的FEdaaS架构为跨云,雾和边缘层部署AI服务提供了可靠的解决方案.
- 未来的工作包括探索隐私的联合学习,并在实时重症监护和智能城市应用中验证架构.
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