Efficient allocation of data mining tasks in mobile environments. (September 2013)

• Record Type: Journal Article
• Authors: Comito, Carmela; Falcone, Deborah; Talia, Domenico; Trunfio, Paolo
• Other Names: Fortino Giancarlo guest-editor.
• Is Part Of: Concurrent engineering, research and applications. Volume 21:Number 3(2013)
• Page Start: 197
• Page End: 207
• Publication Date: 2013-09
• Subjects: Mobile data mining -- energy efficiency -- scheduling strategy; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X13493449
• Languages: English
• ISSNs: 1063-293X

Abstract

Mobile data mining can be a significant added service for nomadic users, enterprises, and organizations that need to perform analysis of data generated either from a mobile device or from remote sources. A key aspect to enable data analysis and mining over mobile devices is ensuring energy efficiency, as mobile devices are battery-power operated. We worked in this direction by defining a distributed architecture in which mobile devices cooperate in a peer-to-peer style to perform a data mining process, tackling the problem of energy capacity shortage by distributing the energy consumption among the available devices. Within this framework, we propose an energy-aware scheduling strategy that assigns data mining tasks over a network of mobile devices optimizing the energy usage. The main design principle of the energy-aware strategy is finding a task allocation that prolongs the lifetime of the entire network of mobile devices by balancing the energy load among the devices. The energy-aware strategy has been evaluated through discrete-event simulation. The experimental results show that significant energy savings can be achieved by using the energy-aware scheduler in a mobile data mining scenario, compared to classical time-based schedulers.