Customizable function scheduling for serverless computing

Serverless computing, with its advantages of elasticity, efficiency, and low cost, has become a key component of next-generation cloud computing. As the complexity and diversity of serverless applications increase, existing serverless platforms typically deploy function instances from different applications on the same server and apply a unified CPU scheduling policy, such as Linux's completely fair scheduler (CFS). However, due to the varying requirements of different applications in terms of performance, latency, and scheduling fairness, a single global scheduling policy struggles to meet diverse scheduling demands. Moreover, our research reveals that function instances exhibit a wide range of execution times, with a high proportion of short tasks, and that cold starts significantly impact task completion times. These characteristics make current scheduling policies ineffective in balancing efficiency and fairness, especially increasing the scheduling latency for short tasks. To address these issues, we propose and implement a scheduling isolation mechanism that allows single or multiple applications with similar requirements to adopt independent CPU scheduling policies on the same server. Additionally, we design FaaSchedule, a novel customizable scheduling policy that takes into account function execution time, startup time, and waiting time, and supports dynamic customization of scheduling strategies based on the needs of different applications. Experiments on the OpenWhisk platform show that FaaSchedule significantly reduces the average completion time of short tasks with minimal impact on the performance of long tasks, while effectively supporting scheduling isolation and policy customization.