The proliferation of portable devices (PDAs, smartphones, digital multimedia players, and so forth) allows mobile users to carry around a pool of computing, storage and communication resources. Sharing these resources with other users (“Digital Organisms” – DOs) opens the door to novel interesting scenarios, where people trade resources to allow the execution, anytime and anywhere, of applications that require a mix of capabilities. In this paper we present a fully distributed approach for resource sharing among multiple devices owned by different mobile users. Our scheme enables DOs to trade computing/networking facilities through an auction-based mechanism, without the need of a central control. We use a set of numerical experiments to compare our approach with an optimal (centralized) allocation strategy that, given the set of resource demands and offers, maximizes the number of matches. Results confirm the effectiveness of our approach since it produces a fair allocation of resources with low computational cost, providing DOs with the means to form an altruistic digital ecosystem.
M. Marzolla, S. Ferretti, G. D'Angelo (2013). Auction-Based Resource Allocation in Digital Ecosystems. Los Alamitos, CA : IEEE [10.1109/Mobilware.2013.16].
Auction-Based Resource Allocation in Digital Ecosystems
MARZOLLA, MORENO;FERRETTI, STEFANO;D'ANGELO, GABRIELE
2013
Abstract
The proliferation of portable devices (PDAs, smartphones, digital multimedia players, and so forth) allows mobile users to carry around a pool of computing, storage and communication resources. Sharing these resources with other users (“Digital Organisms” – DOs) opens the door to novel interesting scenarios, where people trade resources to allow the execution, anytime and anywhere, of applications that require a mix of capabilities. In this paper we present a fully distributed approach for resource sharing among multiple devices owned by different mobile users. Our scheme enables DOs to trade computing/networking facilities through an auction-based mechanism, without the need of a central control. We use a set of numerical experiments to compare our approach with an optimal (centralized) allocation strategy that, given the set of resource demands and offers, maximizes the number of matches. Results confirm the effectiveness of our approach since it produces a fair allocation of resources with low computational cost, providing DOs with the means to form an altruistic digital ecosystem.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.