With the advancement of Internet of Things (IoT) applications and the substantial surge in associated traffic, coupled with the rise in demand for low-latency mobile applications, the utilization of remote cloud computing infrastructure has encountered notable challenges. As a solution, edge computing has emerged, decentralizing computational resources closer to data sources. This results in quicker processing and diminished reliance on distant servers. To address resource limitations and prevent congestion in edge servers, applications are now being developed as smaller, modular components, referred to as the Function as a Service (FaaS) pattern. FaaS brings advantages such as distributing the workload across multiple edge servers and offering improved flexibility in resource management. However, the FaaS model introduces new challenges when compared to the traditional edge computing environment. These include issues like function placement and minimizing cold start delays. Assignment mechanisms play a crucial role in characterizing the interactions between edge servers and user devices. They excel at devising optimal allocation strategies and ensuring mutual satisfaction between edge servers and user devices, especially in scenarios with limited resources. This article introduces the Edge FaaS-Top Trading Cycles (EF-TTC), an efficient mechanism for implementing FaaS-based applications within the edge platform. The proposed approach models the problem of function placement and scheduling in a manner akin to the well-known School Choice problem. It subsequently employs the Top Trading Cycle mechanism to address this challenge. Experimental results demonstrate that the proposed approach significantly improves the average completion time for users’ requests, outperforming other baseline algorithm in this domain.

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