In recent years, edge computing has emerged as a promising solution for deploying IoT applications that demand minimal latency. By leveraging Function as a Service (FaaS) at the edge, it is possible to achieve efficient and scalable computing capabilities. However, implementing serverless deployment at the edge presents challenges such as auto-scaling, resource management, and mitigating cold-start delays, particularly due to the limited resources available. These challenges are even more significant in workflow-based applications, where tasks are interdependent. This article introduces a dynamic approach for executing serverless workflows at the edge, consisting of three key components: initial function placement, request scheduling, and dynamic adjustment. The initial placement leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to deploy function instances across edge nodes. Request scheduling, on the other hand, distributes requests among these instances using a pattern graph matching algorithm. Finally, the dynamic adjustment component periodically refines placement and scheduling strategies to adapt to changing demands, utilizing a local search technique known as simulated annealing. Evaluation results indicate that the proposed solution reduces the average makespan of workflows by up to 86% compared to state-of-the-art methods.

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