Goal: Provide a scalable testbed for P2P gaming mechanisms Requirements:• Gameplay representing a real game • Attractive game to real players, which provide the reference behavior • Simple enough for focusing on the important aspects • Run in a real network as well as in a deterministic emulated network environment • Resource-efficient for a good simulation scalability • Well-defined and flexible interfaces to facilitate the replacement of network components

We present an approach to the generation of realistic synthetic workloads for use in benchmarking of (massively) multiplayer online gaming infrastructures. Existing techniques are either too simple to be realistic or are too specific to a particular network structure to be used for comparing different networks with each other. Desirable properties of a workload are reproducibility, realism and scalability to any number of players. We achieve this by simulating a gaming session with AI players that are based on behavior trees. The requirements for the AI as well as its parameters are derived from a real gaming session with 16 players. We implemented the evaluation platform including the prototype game Planet PI4. A novel metric is used to measure the similarity between real and synthetic traces with respect to neighborhood characteristics. In our experiments, we compare real trace files, workload generated by two mobility models and two versions of our AI player. We found that our AI players recreate the real workload characteristics more accurately than the mobility models.

Abstract-In this demo we present a performance evaluation testbed for peer-to-peer gaming overlays. It consists of a 3D first person shooter game that is designed to run in a simulated network environment as well as on a real network. Simulation with autonomous players (bots) guarantees scalability, a controlled workload, and reproducible results; a prototype deployment on a real network can then validate the simulation results. The information dissemination overlay pSense is implemented as a first subject for evaluation.

We present an approach for integrating voice communication into massively multiplayer online games (MMOG). The currently available third-party software solutions involve additional fees and are not connected to the game logic. Other approaches use the game servers directly for mixing and transmitting speech. Doing so allows for a tight coupling of speech and in-game events but consumes large amounts of bandwidth and computational power on the servers. Our novel idea is a mix between server-based signaling and peer-to-peer data transmission. While the entire game context resides on the server, audio streams are transmitted in a peer-to-peer fashion and processed at the peer nodes only. This technique splits up the workload among all machines while still allowing to relate speech processing to game events. We further improve the processing performance on the clients by using SOUND, a novel technique for distance based resampling.

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