Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis

Amit Samanta, Ryan Stutsman

Abstract: Although serverless platforms have been extremely popular recently, certain kinds of applications are not well-supported by these platforms. For example, they work well for workloads that transform or aggregate bulk data but not for applications where predictable response times are crucial. These workloads have fine-grained tasks and stringent service level agreements (SLAs). Current serverless platforms’ overheads and resource contention lead to variable performance, making using them for real-time online applications impossible. We demonstrate Anubis, a new platform built on top of OpenWhisk, that helps meet SLAs for response-time-sensitive serverless workloads, even when multiple concurrent workloads compete for various resource types. Anubis’s approach centers on multi-resource fair queuing. Even as stateless functions compete for CPU, storage, and network resources, Anubis ensures each gets its fair share according to its dominant resource needs. We show that when various serverless workloads are intermixed, Anubis’s approach can reduce SLA violations by 15–34%, improving max-min fairness by 2× compared to competing scheduling policies.