Context is information that describes the situations in which computing, social and physical interactions take place. The complexity and scope of context information available for utilization by context-consuming applications, such as those executing on smart mobile devices, sensing and tracking platforms, etc. is growing with the increased integration of digital artifacts in smart environments. Similarly, the uptake of Cloud computing has significantly influenced the traditional information processing and infrastructure provision models by offering an agile, scalable and cost effective computing paradigm. This is leading to the adoption of Cloud-based solutions for the pervasive and ubiquitous environments; however, there are significant challenges that need to be overcome before its exploitation by real world applications and users. The context information consumed and produced by the applications and devices needs to be represented, disseminated, processed and consumed by numerous components in a context-aware Cloud system. Significant amount of context consumption, production and processing takes place on devices and there is limited or no support for collaborative modeling, persistence and processing between the device-Cloud ecosystems. In this paper we propose an environment for context processing in a Cloud-based distributed infrastructure that offloads complex context processing from the applications and devices. An experimental analysis of complexity based context-processing categories has been carried out to establish the processing-load boundary. The results demonstrate that the proposed collaborative device-Cloud infrastructure provides significant performance and energy conservation benefits for mobile devices and applications.