Crowdsourcing the cloud: energy-aware computational offloading for pervasive community-based cloud computing

Adem, K, Ryan, C and Abebe, E 2015, 'Crowdsourcing the cloud: energy-aware computational offloading for pervasive community-based cloud computing', in Proceedings of the 21st International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'15), Las Vegas, United States, 27-30 July 2015, pp. 415-423.


Document type: Conference Paper
Collection: Conference Papers

Title Crowdsourcing the cloud: energy-aware computational offloading for pervasive community-based cloud computing
Author(s) Adem, K
Ryan, C
Abebe, E
Year 2015
Conference name PDPTA'15
Conference location Las Vegas, United States
Conference dates 27-30 July 2015
Proceedings title Proceedings of the 21st International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'15)
Publisher Worldcomp
Place of publication Las Vegas, United States
Start page 415
End page 423
Total pages 9
Abstract Adaptive offloading systems achieve context specific optimization on mobile and pervasive devices by offloading computational components to a resource copious remote server or cloud. However, with the recent advancement in computational capacity of mobile and pervasive devices, adaptive offloading could facilitate the formation of ad-hoc cloud-like environments using collections of mobile and pervasive devices, with reduced reliance on centralized infrastructure. Therefore, in this paper, we formulate a decision-making strategy for global adaptive offloading that distributes application components to community-based clouds formed from multiple collaborating peers. The goal was to extend the collaboration and application lifetime by optimizing the Time to Failure (TTF) of devices due to energy depletion, while meeting application specific performance constraints. Specifically, a max-min technique was used to maximise the minimum TTF in order to balance energy consumption across collaborating devices. The efficacy, performance and scalability of the formulated model were evaluated with the proposed algorithm producing an optimal solution to the specified model, using integer linear programming, in affordable time and energy for a range of application and collaboration sizes.
Subjects Mobile Technologies
Ubiquitous Computing
Keyword(s) Adaptive Offloading
Collaborative Application Partitioning
Energy Conservation
Pervasive Community Cloud
Time to Failure
Peer-to-peer Distributed Computing
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