As the number of people who interact on social networks increases, and coupled with the greater capability made available within our computational devices, there is the potential to establish “Social Clouds”-a resource sharing infrastructure that enable people who have trust relationships to come together to share computational/ data services within a community. Social clouds can also provide the means to enhance multi-user collaboration and greatly stimulate the exchange of resources among participants. Recent research in the establishment and use of Social Clouds has raised significant interest by proposing an environment where users are able to trade resources mediated by a social networking mechanism. In such a cloud environment the incentives for sharing can represent a solution for improving resource utilisation and for making available additional capacity to friends and collaborators. In this paper we demonstrate how revenue can be earned within a social cloud community, by executing internal (intra community) and external (inter community) tasks. A number of different scenarios are first investigated through simulation, using the PeerSim simulator, in order to validate our approach. We use two key metrics: revenue and reputation, to evaluate how the system dynamics change as new tasks are added to one or more communities for execution, along with additional behaviours, such as nodes migrating from one community to another, or selectively reporting on the outcome of task execution. Subsequently, we develop a practical deployment using a federated cloud scenario using the CometCloud system-deployed over three sites: Cardiff (UK), Rutgers and Indiana. We show how approaches that have been simulated in PeerSim can be implemented in practice.

Summary Social Clouds provide the capability to share resources among participants within a social network—leveraging on the trust relationships already existing between such participants. In such a system, users are able to trade resources between each other rather than make use of capability offered at a (centralized) data center. Although such an environment has significant potential for improving resource utilization and making available additional capacity that remains dormant, incentives for sharing remain an important hurdle limiting its effective. In this paper, we utilize the socioeconomic model proposed by Silvio Gesell to demonstrate how a ‘virtual currency’ can be used to incentivise sharing of resources within a ‘community’. We subsequently demonstrate, through simulations, the benefit provided to participants within such a community using a variety of economic (such as overall credits gained) and technical (number of successfully completed transactions) metrics. Further, we describe our implementation of such a Social Cloud using CometCloud. CometCloud is an autonomic computing engine for cloud and grid environments. It supports highly heterogeneous and dynamic federated cloud/Grid infrastructures, integration of public/private clouds and autonomic cloudbursts. We demonstrate the implementation of two designs on the basis of the master/worker approach: (i) one tuple space per cluster and (ii) one coordination tuple space and multiple transient spaces—one per each cluster. Finally, we discuss an extended version of our Social Cloud model where intermediary relay nodes take on more active roles as traders in a transaction. Copyright © 2013 John Wiley & Sons, Ltd.

Cloud computing generally involves the use of data storage and computational resources from external providers. 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 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. 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.

Distributed online social networks (DOSN) have been proposed as an alternative to centralized Online Social Networks (OSN). In contrast to centralized OSN, DOSNs do not have central repository of all user data, neither impose control regarding how users data will be accessed. Therefore, users can keep control of their private data and are not at the mercy of the social network providers. However, one of the main problems in DOSNs is how to efficiently disseminate social updates among peers. In our previous work, we proposed Social Caches for social updates dissemination in DOSN. However, the selection of social caches requires knowledge about the entire social graph. In this paper, we propose four fully distributed social cache selection algorithms, and evaluate their performance on five well known graphs. Using simulations we show that these algorithms perform almost as good as the centralized best known approximation algorithm would do. These distributed caching techniques can be used as a basis for various applications such as those that represent fusions of social and vehicular networks.