To meet next-generation IoT application demands, edge computing moves processing power and storage closer to the network edge to minimise latency and bandwidth utilisation. Edge computing is becoming popular as a result of these benefits, but resource management is still challenging. Researchers are utilising AI models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilised advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and ActorCritic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim.

翻译：为满足下一代物联网应用需求，边缘计算将处理能力与存储资源迁移至网络边缘，以降低延迟和带宽利用率。尽管此类优势使边缘计算日益普及，但资源管理仍面临挑战。研究者正利用AI模型解决边缘计算系统中的资源管理难题，然而现有仿真工具仅关注传统资源管理策略，尤其缺乏针对AI模型在资源管理中应用与实现的支持。这使得研究人员在借助现有仿真工具为边缘计算设计新型资源管理策略时，面临重大挑战且耗时费力。为解决上述问题，我们提出名为EdgeAISim的轻量级Python工具包，用于在边缘计算环境中仿真与建模AI驱动的资源管理策略设计。在EdgeAISim中，我们扩展了EdgeSimPy框架的基础组件，开发了面向边缘计算环境的新型AI驱动仿真模型，涵盖任务调度、能耗管理、服务迁移、网络流调度及移动性支持。我们采用多臂赌博机结合置信上界、深度Q网络、图神经网络增强深度Q网络及演员-评论家网络等先进AI模型，在优化功耗的同时高效管理边缘计算环境中的任务迁移。通过不同场景下与基于最差适应算法资源管理策略的基线对比，EdgeAISim所提模型展现出显著的功耗降低效果，有力验证了其功耗优化策略的成功性。