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Automated negotiated user profiling across distributed social mobile clouds for resource optimisation
Author(s) -
Benkhelifa Elhadj,
Welsh Thomas,
Tawalbeh Lo'ai,
Jararweh Yaser
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4349
Subject(s) - computer science , mobile cloud computing , cloud computing , pooling , mobile device , profiling (computer programming) , mobile computing , distributed computing , resource (disambiguation) , negotiation , data science , world wide web , artificial intelligence , computer network , operating system , political science , law
Summary With mobile computing being the number one user paradigm of choice, and cloud computing becoming the chosen supporting infrastructure, the aggregation of these two areas is inevitable. Yet due to their novelty, they suffer from both inherited and new issues. Social computing theory can apply well to this fusion, such as pooling together in a social model to create ad hoc mobile clouds or by providing a greater functionality for mobile devices through resource augmentation from an external cloud. However, due to the highly contested resources within this setting, these resources must be negotiated within a social context. This paper argues that the application of social models to mobile cloud computing can allow mobile devices to employ cooperative strategies for resource sharing to allow aspects such as energy and costs to be minimised. Social computing is the means of using computing resources to augment human intelligence, mobiles can provide these enhancements to social intelligences but in a peer‐to‐peer manner. This paper proposes the design of a novel system which employs aggregated user and application resource profiling, in order to determine the most optimal place to process data, locally or on a remote cloud. Negotiation with the local cloud will then find a balance between optimum energy and resource utilisation.