Cloudburst

TLDR

Serverless cloud computing is increasingly popular, yet current FaaS platforms mainly support stateless functions with minimal I/O, making cache consistency a key challenge. The authors propose extending serverless computing to support a broader range of applications and algorithms. Cloudburst implements a stateful FaaS platform using Python, Anna’s autoscaling key‑value store for state sharing, overlay routing, and mutable caches for locality, together with lattice‑encapsulated state and new distributed session‑consistency protocols. Benchmarks demonstrate that Cloudburst makes stateful functions practical, cuts state‑management overheads by orders of magnitude, and advances serverless consistency.

Abstract

Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.