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To meet next-generation IoT application demands, edge computing movesprocessing power and storage closer to the network edge to minimise latency andbandwidth utilisation. Edge computing is becoming popular as a result of thesebenefits, but resource management is still challenging. Researchers areutilising AI models to solve the challenge of resource management in edgecomputing systems. However, existing simulation tools are only concerned withtypical resource management policies, not the adoption and implementation of AImodels for resource management, especially. Consequently, researchers continueto face significant challenges, making it hard and time-consuming to use AImodels when designing novel resource management policies for edge computingwith existing simulation tools. To overcome these issues, we propose alightweight Python-based toolkit called EdgeAISim for the simulation andmodelling of AI models for designing resource management policies in edgecomputing environments. In EdgeAISim, we extended the basic components of theEdgeSimPy framework and developed new AI-based simulation models for taskscheduling, energy management, service migration, network flow scheduling, andmobility support for edge computing environments. In EdgeAISim, we haveutilised advanced AI models such as Multi-Armed Bandit with Upper ConfidenceBound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, andActorCritic Network to optimize power usage while efficiently managing taskmigration within the edge computing environment. The performance of theseproposed models of EdgeAISim is compared with the baseline, which uses aworst-fit algorithm-based resource management policy in different settings.Experimental results indicate that EdgeAISim exhibits a substantial reductionin power consumption, highlighting the compelling success of power optimizationstrategies in EdgeAISim.

EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments