FaaSBoard: Efficient Graph Processing with a Disaggregated Architecture on Serverless Services

Graph processing workloads are increasingly being migrated to the cloud. With the growing adoption of serverless computing, graph processing gains advantages such as cost-effectiveness and resource elasticity. However, existing graph processing systems with monolithic function architecture struggle to detect intra-job resource elasticity and suffer from significant communication overhead. In this paper, we present FaaSBoard, a graph processing system with a disaggregated serverless architecture powered entirely by serverless cloud services. FaaSBoard features a multi-tier data communication mechanism and an autonomous-elastic computing mechanism. Specifically, these two mechanisms are realized through image-based graph loading for faster starts, proxy-based collective communication leveraging high-bandwidth shared memory, 2D balanced graph partitioning for improved load balance, and a proactive terminate-and-respawn mechanism enabling fine-grained elasticity. Together, these four techniques collectively enhance both resource and overall execution efficiency. Experimental results demonstrate that FaaSBoard delivers up to 3.8× higher compute performance and reduces monetary cost by up to 61.5% compared to FaaSGraph, the current state-of-the-art serverless-based graph processing system. The source code of FaaSBoard is publicly available athttps://github.com/SJTU-Liquid/FaaSBoard.