Graph processing is widely used in cloud services; however, current frameworks face challenges in efficiency and cost-effectiveness when deployed under the Infrastructure-as-a-Service model due to its limited elasticity. In this paper, we present FaaSGraph, a serverless-native graph computing scheme that enables efficient and economical graph processing through the co-design of graph processing frameworks and serverless computing systems. Specifically, we design a data-centric serverless execution model to efficiently power heavy computing tasks. Furthermore, we carefully design a graph processing paradigm to seamlessly cooperate with the data-centric model. Our experiments show that FaaS-Graph improves end-to-end performance by up to 8.3X and reduces memory usage by up to 52.4% compared to state-of-the-art IaaS-based methods. Moreover, FaaSGraph delivers steady 99%-ile performance in highly fluctuated workloads and reduces monetary cost by 85.7%.

Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by running functions (or Lambdas) in containers. Data-dependent functions are required to be invoked following a pre-defined logic, which is known as serverless workflows. However, our investigation shows that the traditional master-worker based workflow execution architecture performs poorly in serverless context. One significant overhead results from the master-side workflow schedule pattern, with which the functions are triggered in the master node and assigned to worker nodes for execution. Besides, the data movement between workers also reduces the throughput.