Mocha: Scalable and Compliant Function Scheduling for Federated Serverless Computing

Abstract

Serverless computing promises on-demand elasticity and simplified deployment, yet today’s production-grade serverless platforms remain tied to a single-provider, centrally scheduled control plane. This centralized scheduling model faces mounting challenges in handling heterogeneous policies, data governance constraints, and dynamic workloads for the modern web, where applications increasingly span multiple geo-distributed autonomous administrative domains. In this paper, we present Mocha, a decentralized, policy-aware framework for scheduling serverless functions across a federated ecosystem. At its core, Mocha proposes a hierarchically structured distributed hash table that embeds geographical and organizational context to facilitate locality-aware scheduling without any central authority. By implementing a formally specified compliance engine at each domain, Mocha guarantees that all regulatory, locality, and resource constraints are honored for function placement decisions. Experiments show that Mocha reduces scheduling tail latency by 4–9× compared to alternatives while maintaining full policy adherence.