Task Scheduling on Crowdsourcing Platforms for Enabling Completion Time SLAs

Today a diverse crowdsourcing economy has established, and service level agreements (SLAs) become a crucial part of this ecosystem. However, the uncertainty introduced by human workers makes it hard to set completion time guarantees. In this work, we analyze an exemplary task scheduling strategy that helps operators of crowdsourcing-based services to meet completion time SLAs. We use the real-world crowdsourcing-based text digitalization platform ScaleHub as the foundation for our system model and a dataset provided by ScaleHub to obtain a realistic parametrization of the model. We derive a simulation model that enables us to illustrate the potential and limitations of scheduling mechanisms to meet SLA constraints and that helps platform providers to optimize their systems.