Analytic Methods for Optimizing Realtime Crowdsourcing

TL;DR

This paper analyzes and optimizes real-time crowdsourcing systems using queueing theory, proposing techniques like push notifications and precruitment to reduce wait times and costs, enabling faster response times suitable for interactive applications.

Contribution

It introduces a queueing theory-based analysis of the retainer model and proposes novel techniques to improve performance and cost-efficiency at large scale.

Findings

Precruited workers start tasks within 500 milliseconds.

Proposed methods reduce wait times below one second.

Algorithm helps requesters minimize costs while meeting performance goals.

Abstract

Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.