Generation of synthetic workloads for multiplayer online gaming benchmarks

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.

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