FaaSBid: an auction-based model for Function as a Service in edge-fog environments using unallocated resources - Al-Qadhi, Abdulrahman K.; Athauda, Rukshan; Latip, Rohaya; Hussin, Masnida (Elsevier B.V., 2026)
The exponential growth of IoT devices has resulted in a need to process IoT workloads. Processing such workloads near the edge instead of the cloud has a number of advantages including lower latency, improved security and ability to meet many other Quality of Service attributes. Function as a Service (FaaS) is becoming a popular method to process such IoT workloads. In this paper, we propose a novel model, termed FaaSBid, that incentivise users to utilise serverless functions near the edge using unallocated resources. The service provider offers a discount range based on resource utilisation, where users offer bids to execute their functions near the edge resulting in cost savings while the service providers have a new revenue stream and higher resource utilisation near the edge. In this paper, a number of algorithms are proposed and evaluated for FaaSBid model. To initialise function placement, Fitness-Based Swap (FBSW) algorithm is proposed which places functions based on pre-defined information such as function size, function maximum execution time, and storage cost. Next, the Dynamic Demand Replacement Algorithm (DDRA) algorithm is used to place in-demand functions near the edge nodes periodically, while the proposed task scheduling algorithm - Maximum Revenue Bid (MRB) is used to give priority to tasks to maximise revenue near the edge. We have evaluated the FaaSBid model and the proposed algorithms and pricing model by comparing with a number of existing models and algorithms using real-world datasets. The results show that FaaSBid model provides higher resource utilisation, a new revenue stream for service providers while reducing costs for users. On average, in FaasBid, the proposed pricing model saved 12.9% and 6.5% compared to AWS fixed pricing and AuctionWhisk pricing respectively per function execution. Also, the results show that the proposed function placement and scheduling algorithms outperform many well-known function placement and scheduling algorithms in terms of revenue generated, resource utilisation, throughput, and latency with significant improvements near the edge. The results also demonstrated that dynamically placing functions based on demand has a significant impact. Overall, this paper outlines a new paradigm that uses unutilised resources near the edge, improving many QoS attributes from both service providers' and users’ perspectives.