Automated negotiated user profiling across distributed social mobile clouds for resource optimisation. (28th February 2018)

• Record Type: Journal Article
• Authors: Benkhelifa, Elhadj; Welsh, Thomas; Tawalbeh, Lo'ai; Jararweh, Yaser
• Other Names: Sangaiah Arun Kumar guestEditor.; Pham Hoang guestEditor.; Qiu Tie guestEditor.; Muhammad Khan guestEditor.; Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Hussain Farookh guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-02-28
• Subjects: energy optimisation -- negotiation -- social mobile cloud computing -- user profiling -- social computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4349
• Languages: English
• ISSNs: 1532-0626

Abstract

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.