Throughput-Fairness Tradeoffs in Mobility Platforms

TL;DR

This paper introduces Mobius, a system that balances throughput and fairness in task allocation for mobility platforms, effectively handling diverse and dynamic customer demands while demonstrating scalability and versatility.

Contribution

Mobius is a novel guided optimization system that explicitly manages the tradeoff between throughput and fairness in mobility task scheduling.

Findings

Mobius can schedule over 16,000 tasks for 40 customers and 200 vehicles.

It effectively balances fairness and throughput in dynamic, real-world scenarios.

The system demonstrates scalability and versatility across different mobility applications.

Abstract

This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.