A Case of Multi-Resource Fairness for Serverless Workflows (Work In Progress Paper)

Amit Samanta, Ryan Stutsman

Abstract: Serverless platforms have exploded in popularity in recent years, but, today, these platforms are still unsuitable for large classes of applications. They perform well for batch-oriented workloads that perform coarse transformations over data asynchronously, but their lack of clear service level agreements (SLAs), high per-invocation overheads, and interference make deploying online applications with stringent response time demands impractical.Our assertion is that beyond the glaring issues like cold start costs, a more fundamental shift is needed in how serverless function invocations are provisioned and scheduled in order to support these more demanding applications. Specifically, we propose a platform that leverages the observability and predictability of serverless functions to enforce multi-resource fairness. We explain why we believe interference across a spectrum of resources (CPU, network, and storage) contributes to lower resource utilization and poor response times for latency-sensitive and high-fanout serverless application patterns. Finally, we propose a new distributed and hierarchical function scheduling architecture that combines lessons from multi-resource fair scheduling, hierarchical scheduling, batch-analytics resource scheduling, and statistics to create an approach that we believe will enable tighter SLAs on serverless platforms than has been possible in the past.