A cloud resource management model for the creation and orchestration of social communities

Managing resources, context and data in mobile clouds is a challenging task. Specific aspects of spontaneity, large interaction space and dynamic interaction share a metaphorical resemblance to chemistry, chemical reactions and solutions. In this paper, it is argued that by adopting a nature-inspired chemical computing model, a mobile cloud resource management model can be evolved to serve as the basis for novel service modelling and social computing in mobile clouds. To support the argument, a chemistry inspired computation model, Chemistry for Context Awareness (C2A), is extended with Higher Order Chemical Language (HOCL) and High Level Petri-net Graph (HLPNG) formalisms. A scenario and simulation-based evaluation of the proposed model, focusing on two applications dynamic service composition and social communities identification, is also presented in this paper. The formal encoding of C2A validates its assumptions, enabling formal execution and analysis of context-based interactions that are derived using C2A principles

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work