Abstract-Peer-to-Peer Realm (P2PRealm) is an efficient peer-topeer network simulator for studying algorithms based on neural networks. In contrast to many simulators, which emphasize on detailed network simulation, the speed of simulation in P2PRealm is essential, because neural networks require a time consuming training phase. Efficiency has been obtained by optimizing training loops inside the simulator, using Java Native Interface (JNI) as well as distributing the simulator to hundreds of workstations using the P2PDisCo platform. In this paper we describe the architecture of P2PRealm and its input/output interfaces. Also, we present the mechanisms used for internally optimizing the implementation and the configuration used for distribution. Finally, we present the use of P2PRealm with the P2PStudio network visualization tool.

Value co-creation through involving users in service processes via resource integration is a focal service research interest. However, studies often take a firm-centric or generic approach and overlook value co-creation from the point view of an individual user. We address this gap by adopting a qualitative research approach and laddering interviews (n = 113) to examine users' hedonic and utilitarian drivers for value co-creation behavior in five service system contexts. We argue that underlying differences exist among all service systems and contribute with a novel approach by depicting the differences in value-based motivations for users to co-create value. As practical implications, our findings suggest services should be designed according to users' value drivers rather than system types. Furthermore, we demonstrate how the consumer information systems (CIS) framework can be used to benchmark users' value co-creation behavior with specific service systems or to compare such behavior between different service systems.

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Resource discovery is an essential problem in peerto-peer networks since there is no centralized index in which to look for information about resources. In a pure P2P network peers act as servers and clients at the same time and in the Gnutella network for example, peers know only their neighbors. In addition to developing different kinds of resource discovery algorithms, one approach is to study the different topologies or structures of the P2P network. In many cases topology management is based on either technical characteristics of the peers or their interests based on the previous resource queries. In this paper, we propose a topology management algorithm which does not predetermine favorable values of the characteristics of the peers. The decision whether to connect to a certain peer is done by a neural network, which is trained with an evolutionary algorithm. Characteristics, which are to be taken into account, can be determined by the inputs of the neural network.

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