Due to increasing popularity and strict performance requirements, online games have become a topic of interest for the performance engineering community. One of the most popular types of online games is the modifiable virtual environment (MVE), in which players can terraform the environment. The most popular MVE, Minecraft, provides not only entertainment, but also educational support and social interaction, to over 130 million people world-wide. MVEs 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 MVEs, investigate experimentally causes of performance variability, and derive actionable insights. We propose an operational model for MVEs, which extends the state-of-the-art with essential aspects, e.g., through the consideration of environment-based workloads, which are sizable workload components that do not depend on player input (once set in action). Starting from this model, we design the first benchmark that focuses on MVE performance variability, defining specialized workloads, metrics, and processes. We conduct real-world benchmarking of Minecraft-like MVEs, 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. We derive actionable insights for game-developers, game-operators, and other stakeholders to tame performance variability.
翻译:由于日益受欢迎和严格的绩效要求,在线游戏已成为业绩工程界感兴趣的一个话题。最受欢迎的在线游戏类型之一是可变虚拟环境(MVE),球员可以在这种环境中改变环境。最受欢迎的MVE(Minecraft)不仅为全世界超过1.3亿人提供娱乐支持,而且还提供教育支持和社会互动。MVes目前通过复制孤立的例子来支持其众多球员,这些例子只在有利的条件下支持每个球员达几百个球员。实际上,正如我们在这里所显示的那样,支持球员的真正上限可能要低得多。在这项工作中,我们假设业绩的变异性是造成MVes缺乏可变性的主要原因,对业绩变异性进行实验性调查,并得出可操作的洞察力。我们为MVes提出了一个操作模型,通过考虑基于环境的工作量,我们发现不依赖球员投入的工作量是可变化的。从这个模型开始,我们设计了业绩变异性的第一个基准,我们从实际的变变异性的角度,我们从模型到模型来决定了业绩的变变异性,我们从高的变异性标准环境,我们从微的变变变的数值到测测测测测了其他变数。