RadSched: A Latency Optimizing Scheduler for Stateful Serverless Edge Computing

Abstract

This thesis presents and evaluates RadSched, a latency-optimizing scheduler designed for stateful, per function execution in a serverless edge computing environment. In distributed systems, where data consistency and latency vary by time and location, selecting the optimal edge location for function execution becomes a complex decision. Built on top of the Radical framework, RadSched maintains records of network conditions and learns from past data consistency outcomes to automate routing function requests to the optimal edge location. The system employs an ϵ-greedy exploration strategy to adapt to shifting network conditions and data availability, thereby ensuring responsiveness. Through empirical evaluation across multiple AWS regions, this thesis demonstrates that RadSched maintains comparable median latency to baseline systems in a stable environment, though with higher tail latency – a tradeoff that allows the system to route functions to a shifting optimal edge in volatile environments. Ultimately, by abstracting edge selection away from the client, RadSched both improves performance and simplifies developer interaction with stateful server-less functions on the edge.