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Event processing is the cornerstone of the dynamic and responsive Internet ofThings (IoT). Recent approaches in this area are based on representationalstate transfer (REST) principles, which allow event processing tasks to beplaced at any device that follows the same principles. However, the tasksshould be properly distributed among edge devices to ensure fair resourcesutilization and guarantee seamless execution. This article investigates the useof deep learning to fairly distribute the tasks. An attention-based neuralnetwork model is proposed to generate efficient load balancing solutions underdifferent scenarios. The proposed model is based on the Transformer and PointerNetwork architectures, and is trained by an advantage actor-criticreinforcement learning algorithm. The model is designed to scale to the numberof event processing tasks and the number of edge devices, with no need forhyperparameters re-tuning or even retraining. Extensive experimental resultsshow that the proposed model outperforms conventional heuristics in many keyperformance indicators. The generic design and the obtained results show thatthe proposed model can potentially be applied to several other load balancingproblem variations, which makes the proposal an attractive option to be used inreal-world scenarios due to its scalability and efficiency.

Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks