Cost-Aware Uplink MPQUIC Scheduling via Multi-Objective Bayesian Optimization

2026-07-20

AI Summary

This work addresses the challenge of balancing upload latency and cellular cost in MPQUIC scheduling over heterogeneous (Wi-Fi/LTE) uplink networks. It introduces, for the first time, multi-objective Bayesian optimization to this domain, formulating path scheduling as a black-box problem that jointly minimizes makespan and LTE usage. Without requiring modifications to the protocol internals or predefined scheduling policies, the approach employs probabilistic path selection to automatically explore the Pareto-optimal front, fully characterizing the latency–cost trade-off spectrum. Experimental results using Mininet-WiFi demonstrate that, under highly congested conditions, the proposed method reduces LTE consumption by up to 80% with only modest and controllable increases in latency, substantially outperforming fixed-policy schedulers.

Abstract

Multipath QUIC (MPQUIC) enables simultaneous uplink transmission over heterogeneous access networks such as Wi-Fi and LTE, improving reliability and performance. However, aggressive LTE utilization increases operational cost, creating an inherent trade-off between upload delay and cellular usage. Existing MPQUIC schedulers typically optimize a single performance objective and operate at fixed points within this trade-off space, without explicitly supporting cost-aware operation. This paper formulates uplink MPQUIC scheduling as a multi-objective optimization problem that jointly considers maximum upload completion time and total LTE usage. We propose a Bayesian Optimization-based framework that treats the MPQUIC system as a black box and systematically explores probabilistic path selection configurations to uncover Pareto-efficient operating points. Rather than committing to a predefined scheduling policy, the framework exposes a spectrum of delay--cost trade-offs without modifying protocol internals. Experiments conducted using the Mininet-WiFi emulator show that the proposed approach characterizes a wide delay--cost region and identifies configurations that achieve substantial LTE savings (up to 80%) with controlled increases in upload time. The results further indicate that, under higher contention levels, systematic multi-objective exploration provides increased flexibility compared to fixed-policy schedulers in cost-aware heterogeneous uplink deployments.

Problem

• MPQUIC
• cost-aware scheduling
• multi-objective optimization
• uplink transmission
• LTE usage

Innovation

• Multi-Objective Bayesian Optimization
• MPQUIC Scheduling
• Cost-Aware Networking
• Pareto-Efficient Trade-offs
• Heterogeneous Uplink