Due to increasing popularity and strict performance requirements, online games have become a workload of interest for the performance engineering community. One of the most popular types of online games is the Minecraft-like Game (MLG), in which players can terraform the environment. The most popular MLG, Minecraft, provides not only entertainment, but also educational support and social interaction, to over 130 million people world-wide. MLGs currently support their many players by replicating isolated instances that support each only up to a few hundred players under favorable conditions. In practice, as we show here, the real upper limit of supported players can be much lower. In this work, we posit that performance variability is a key cause for the lack of scalability in MLGs, investigate experimentally causes of performance variability, and derive actionable insights. We propose a novel operational model for MLGs and use it to design the first benchmark that focuses on MLG performance variability, defining specialized workloads, metrics, and processes. We conduct real-world benchmarking of MLGs, both cloud-based and self-hosted, and find environment-based workloads and cloud deployment to be significant sources of performance variability: peak-latency degrades sharply to 20.7 times the arithmetic mean, and exceeds by a factor of 7.4 the performance requirements. We derive actionable insights for game-developers, game-operators, and other stakeholders to tame performance variability.

One of the most popular types of online games is the Minecraft-like Game (MLG), in which players can terraform the environment. MLGs currently support their many players by replicating isolated instances with limited scalability. We posit that performance variability is a key cause for the lack of scalability in MLGs and design the first benchmark that focuses on MLG performance variability, identifying specialized workloads, metrics, and processes. We conduct real-world benchmarking of MLGs, both cloud-based and self-hosted. We find environment-based workloads and cloud deployment are significant sources of performance variability: peak-latency degrades sharply to 20.7 times the arithmetic mean, and exceeds by a factor of 7.4 the performance requirements.