# Plebiscito: A Decentralized, Bandwidth-Aware Architecture for Distributed Learning Workloads

- **Authors**: Andrea Pinto, Stefano Galantino, Fulvio Risso, and Flavio Esposito
- **Venue**: IFIP Networking, 2026
- **Award**: 🏆 Best Paper Award (Honorable Mention)
- **Paper PDF**: [Read Paper PDF](https://opendl.ifip-tc6.org/db/conf/networking/networking2026/1571261587.pdf)

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## Abstract

As large-scale AI training increasingly relies on distributed GPU clusters, data-center network bandwidth has become a critical bottleneck. Existing systems often overlook real-time link utilization during job placement, leading to suboptimal scheduling decisions that exacerbate congestion and increase Job Completion Time (JCT).

To address this gap, we introduce **Plebiscito**, a policy-based architecture that enables bandwidth-aware job placement in distributed AI training clusters. Using a distributed max-consensus auction protocol, nodes autonomously bid on incoming jobs based on local resource availability and network conditions.

We formulate this as a network utility maximization problem and prove that our decentralized algorithm achieves a (1 - 1/e) optimality bound. Experiments on a Kubernetes-based prototype and through large-scale, trace-driven simulation show that Plebiscito reduces JCT, improves bandwidth utilization, and lowers allocation failure rates compared to bandwidth-agnostic baselines.

**Index Terms** — Distributed Training, Resource Orchestration, Network Management, Data Center Systems, Bandwidth-Aware Allocation, Distributed Auctions.

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## System Architecture

Plebiscito uses a decentralized auction-based mechanism where nodes independently bid on learning workloads. Bids are formulated based on local compute capabilities and network paths connecting candidate nodes, avoiding centralized scheduling bottlenecks.

![Plebiscito System Architecture Diagram](https://andreapinto94.github.io/docs/plebi.png)