Data Replication Meets Function Scheduling in the Edge-Cloud Continuum
2026-06-29 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors look at how to best place both small programs (functions) and their needed data close to users in serverless edge computing, where fast responses matter. They show that deciding where to put data is even more important than where functions run, especially when data needs to stay consistent. They provide an exact mathematical solution but it only works for small systems, so they create faster methods that still work well, even for very large setups with many devices. Their simpler method helps keep delays low, especially when users move around, unlike traditional centralized methods.
serverless computingedge computingfunction schedulingdata placementdata replicationconsistency modelsstrong consistencyeventual consistencylatencyclient mobility
Authors
Matteo Cenzato, Dario d'Abate, Arianna Dragoni, Matteo Briscini, Alessandro Margara
Abstract
Serverless computing is an appealing model for the edge-cloud continuum, but its stateless assumption breaks down once functions need persistent data: fetching state from a distant cloud store erases the latency benefit of running at the edge. Keeping data close means replicating it, and replication forces a placement decision that is coupled with where functions execute and with the consistency each application demands. We study this joint problem of function scheduling and data placement under two consistency models, strong and eventual replication. We first formulate it as a Binary Linear Program that yields the optimal placement for a given system snapshot, and use it as a reference point. Because the solver does not scale past a few hundred nodes, we add two heuristics with progressively less information: a Global-View greedy method that works from the same complete snapshot, and an Aggregated-View heuristic in which each node decides from locally observed demand alone. Across a range of system sizes the Global-View heuristic stays within a few percent of the optimum while scaling to over $10^4$ nodes. The Aggregated-View heuristic sacrifices some solution quality, but adapts continuously to each invocation. Under client mobility, centralized policies suffer from stale snapshots and recurring latency spikes, while the Aggregated-View maintains low and stable client-observed latency. Across all experiments, data placement proves more influential than function scheduling in determining the outcome.