Modeling of crowdsourcing platforms and granularity of work organization in Future Internet

Beside of social media networks, crowdsourcing is one of the emerging new applications and business models in the Future Internet, which can dramatically change the future of work and work organization in the on-line world. The crowdsourcing technology can be viewed as "Human Cloud" technique, in contrast to "Machine Cloud Computing". Using a crowd with a large number of internationally widespread workers and the flexibility of micro-payment services, crowdsourcing platforms like Amazon's MTurk and Microworkers can outsource traditional forms of work organization on a microscopic level of granularity to a large, anonymous crowd of workers, the human cloud. In such platforms work or tasks are organized at a finer granularity and jobs are split into micro-tasks that need to be performed by a human cloud. It is a need of analysis to understand the anatomy of such a platform and of models to describe the time-dependent growth of the human cloud, in order to predict the traffic impact of such novel applications and to forecast the growth dynamics. The purpose of this paper is a measurement-based statistical analysis of a crowdsourcing platform, using the Microworkers.com platform as example. The obtained results are then used to model the growth of such fast-changing environments in the Internet using well-known models from biology. Based on the findings from the population growth, we develop a deterministic fluid model which is an extension of the SIR model of epidemics, in order to investigate the platform dynamics.