A function-as-a-service middleware for decentralized collaborative edge computing

• FaaS@Edge is a decentralized Function-as-a-Service framework that leverages volunteer computing resources at the network edge, by combining Apache OpenWhisk and IPFS technologies. • The system achieves robust operational reliability across both function submissions and invocations while efficiently managing distributed edge node resources. • While introducing some initial submission overhead, the system maintains competitive execution speeds compared to local deployments and demonstrates efficient bandwidth usage that scales well with increasing nodes, also validated in scenarios with variable network conditions, and on actual edge devices. • The framework implements a flexible prosumer model where edge nodes can simultaneously provide and consume resources, enabling effective operation within edge device constraints. Function-as-a-Service (FaaS) emerges as a sophisticated cloud computing paradigm critically suited to processing the exponentially increasing data volumes generated by Internet of Things (IoT) infrastructures. Deploying computational models proximal to data generation sources addresses critical latency and bandwidth constraints inherent in edge-distributed applications. Edge computing environments present complex architectural challenges characterized by large-scale decentralized infrastructures and resource-constrained devices, which substantially impede contemporary Function-as-a-Service implementation strategies. This research introduces FaaS@Edge, a novel framework that leverages volunteered edge node resources discovered through the InterPlanetary File System (IPFS) network and deployed via Apache OpenWhisk. The proposed system addresses computational resource distribution challenges by enabling FaaS runtime deployments across heterogeneous edge infrastructure. Comprehensive experimental evaluation shows that FaaS@Edge introduces marginal latency during function submission while maintaining performance comparable to local OpenWhisk implementations. Empirical results demonstrate request success rates that approximate 98 % for function submission and invocation processes. These findings shows FaaS@Edge’s potential as an efficient computational model for edge computing environments, characterized by low-latency performance and optimized resource allocation.