Online games with modifiable virtual environments (MVEs) have become highly popular over the past decade. Among them, Minecraft-supporting hundreds of millions of users―is the best-selling game of all time, and is increasingly offered as a service. Although Minecraft is architected as a distributed system, in production it achieves this scale by partitioning small groups of players over isolated game instances. From the approaches that can help other kinds of virtual worlds scale, none is designed to scale MVEs, which pose a unique challenge―a mix between the count and complexity of active in-game constructs, player-created in-game programs, and strict quality of service. Serverless computing emerged recently and focuses, among others, on service scalability. Thus, addressing this challenge, in this work we explore using serverless computing to improve MVE scalability. To this end, we design, prototype, and evaluate experimentally Servo, a serverless backend architecture for MVEs. We implement Servo as a prototype and evaluate it using real-world experiments on two commercial serverless platforms, of Amazon Web Services (AWS) and Microsoft Azure. Results offer strong support that our serverless MVE can significantly increase the number of supported players per instance without performance degradation, in our key experiment by 40 to 140 players per instance, which is a significant improvement over state-of-the-art commercial and open-source alternatives. We release Servo as open-source, on Github: https://github.com/atlarge-research/opencraft.

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.

Gaming is one of the most popular and lucrative entertainment industries. Minecraft alone exceeds 130 million active monthly players and sells millions of licenses annually; it is also provided as a (paid) service. Minecraft, and thousands of others, provide each a Modifiable Virtual Environment (MVE). However, Minecraft-like games only scale using isolated instances that support at most a few hundred players in the same virtual world, thus preventing their large player-base from actually gaming together. When operating as a service, even fewer players can game together. Existing techniques for managing data in distributed systems do not scale for such games: they either do not work for high-density areas (e.g., village centers or other places where the MVE is often modified), or can introduce an unbounded amount of inconsistency that can lower the quality of experience. In this work, we propose Dyconits, a middleware that allows games to scale, by bounding inconsistency in MVEs, optimistically and dynamically. Dyconits allow game developers to partition offline the game-world and its objects into units, each with its own bounds. The Dyconits system controls, dynamically and policy-based, the creation of dyconits and the management of their bounds. Importantly, the Dyconits system is thin, and reuses the existing game codebase and in particular the network stack. To demonstrate and evaluate Dyconits in practice, we modify an existing, open-source, Minecraft-like game, and evaluate its effectiveness through real-world experiments. Our approach supports up to 40% more concurrent players and reduces network bandwidth by up to 85%, with only minor modifications to the game and without increasing game latency.

Online gaming applications entertain hundreds of millions of daily active players and often feature vastly complex architecture. Among online games, Minecraft-like games simulate unique (e.g., modifiable) environments, are virally popular, and are increasingly provided as a service. However, the performance of Minecraft-like services, and in particular their scalability, is not well understood. Moreover, currently no benchmark exists for Minecraft-like games. Addressing this knowledge gap, in this work we design and use the Yardstick benchmark to analyze the performance of Minecraft-like services. Yardstick is based on an operational model that captures salient characteristics of Minecraft-like services. As input workload, Yardstick captures important features, such as the most-popular maps used within the Minecraft community. Yardstick captures system- and application-level metrics, and derives from them service-level metrics such as frequency of game-updates under scalable workload. We implement Yardstick, and, through real-world experiments in our clusters, we explore the performance and scalability of popular Minecraft-like servers, including the official vanilla server, and the community-developed servers Spigot and Glowstone. Our findings indicate the scalability limits of these servers, that Minecraft-like services are poorly parallelized, and that Glowstone is the least viable option among those tested.