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.

Summary Edge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three‐tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.