Collaborative Uploading in Heterogeneous Networks: Optimal and Adaptive Strategies

TL;DR

This paper models collaborative uploading in heterogeneous networks using queuing theory, deriving optimal data allocation strategies and proposing an adaptive method that minimizes upload delays under varying network conditions.

Contribution

It introduces a queuing theoretic framework for collaborative uploading, providing closed-form optimal strategies and an online adaptive algorithm based on statistical inference.

Findings

Adaptive strategy outperforms proportional allocation in bursty conditions

Closed-form expressions identify optimal data-path mapping

Online adaptation reduces waiting time effectively

Abstract

Collaborative uploading describes a type of crowdsourcing scenario in networked environments where a device utilizes multiple paths over neighboring devices to upload content to a centralized processing entity such as a cloud service. Intermediate devices may aggregate and preprocess this data stream. Such scenarios arise in the composition and aggregation of information, e.g., from smartphones or sensors. We use a queuing theoretic description of the collaborative uploading scenario, capturing the ability to split data into chunks that are then transmitted over multiple paths, and finally merged at the destination. We analyze replication and allocation strategies that control the mapping of data to paths and provide closed-form expressions that pinpoint the optimal strategy given a description of the paths' service distributions. Finally, we provide an online path-aware adaptation of the allocation strategy that uses statistical inference to sequentially minimize the expected waiting time for the uploaded data. Numerical results show the effectiveness of the adaptive approach compared to the proportional allocation and a variant of the join-the-shortest-queue allocation, especially for bursty path conditions.