MOBOS: Co-Optimizing Cost and Execution Time in Serverless Workflow with Multi-Objective Bayesian Optimization

Serverless computing has established itself as a prominent paradigm in cloud computing by enabling developers to focus exclusively on application development without the burden of infrastructure management. In the serverless environment, resource configuration of Function-as-a-Service (FaaS) is a critical factor that directly impacts both execution time and cost; however, determining the optimal configuration remains challenging. This challenge is particularly pronounced in workflows where multiple functions execute sequentially, as individual function configurations influence the overall workflow execution time, thereby increasing the complexity of resource configuration and making it more difficult to identify optimal settings. We propose MOBOS, a method that simultaneously optimizes both execution time and cost of serverless workflows using multi-objective optimization techniques. Our proposed approach is based on Bayesian Optimization, which effectively explores the trade-off between two objectives, execution times and cost, to derive Pareto-optimal memory configurations. Experimental results demonstrate that MOBOS outperforms state-of-the-art single-objective optimization methods and achieves up to 33.83 % faster execution time while simultaneously reducing costs by 13.54 % in real-world application workflows. Moreover, MOBOS provides additional Pareto-optimal configurations with equivalent trade-offs, enabling flexible resource allocation based on specific performance requirements.