A Crowd-Cloud architecture for Big Data analytics

The 3 V's defining big data raises the need for non-conventional computing and communication architectures for its analytics and processing. In this paper we evolve a new architecture based on amalgamating benefits of Crowdsourcing and Cloud Computing. The 'Crowd-Cloud' architecture so formed presents newer domains for efficient analysis and processing of big data. By integrating the sensing and processing capabilities of the mobile dynamic cloud, the 'Crowd-Cloud' architecture provides an efficient and increased functionality in handling Big Data. The proposed architecture furthermore has two-fold benefits, minimizing the overall energy consumption and efficiently utilizing the available resources. In the context of energy constraints and dynamic nature of a mobile cloud, a job assignment problem is formulated and an algorithm is proposed to reach a priority based energy efficient solution. Simulations performed over several different scenarios using realistic energy consumption models, applicable for some of the commercially available mobile devices, favors practical implementation of the 'Crowd-Cloud' concept.