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Petri, Ioan; Punceva, Magdalena; Rana, Omer Farooq; Theodorakopoulos, Georgios; Rezgui, Yacine (2014)
Publisher: Inderscience
Languages: English
Types: Article
Subjects: QA76
The new consumption without ownership paradigm is leading towards a “rental economy” where people can now rent and use various services from third-parties within a market of “shared” resources. The elimination of ownership has increased the marginal utility of consumption and reduced the risks associated with permanent ownership. In the absence of ownership the consumption in the global marketplace has become more dynamic and has positively impacted various economic and social sectors. The concept of “consumption without ownership” can also be used in the area of cloud computing where the interaction between clients and providers generally involves the use of data storage and computational resources. Although a number of commercial providers are currently on the market, it is often beneficial for a user to consider capability from a number of different ones. This would prevent vendor lock-in and more economic choice for a user. Based on this observation, work on “Social Clouds” has involved using social relationships formed between individuals and institutions to establish Peer-2-Peer resource sharing networks, enabling market forces to determine how demand for resources can be met by a number of different (often individually owned) providers. In this paper we identify how trading and consumption within such a network could be enhanced by the dynamic emergence (or identification) of brokers – based on their social position in the network (based on connectivity metrics within a social network). We investigate how offering financial incentives to such brokers, once discovered, could help improve the number of trades that could take place with a network, thereby increasing consumption. A social score algorithm is described and simulated with PeerSim to validate our approach. We also compare the approach to a distributed dominating set algorithm – the closest approximation to our approach.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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