Consistency and instanced massively multiplayer games

多人游戏允许许多人类玩家共同体验游戏，这已经成为一种非常受欢迎的游戏类型。理想情况下，这些世界是很大的，允许来自世界各地的许多人进行互动。然而，对于分布式游戏环境，网络和计算资源并不容易扩展到更大（大规模）的规模，并且当前的解决方案涉及对虚拟游戏世界规模、玩家交互性或确保每个玩家同时看到相同状态的能力的各种限制。我们的建议是基于当前“实例化”游戏模式的扩展，即玩家并非同时玩游戏，而是由最多100名玩家组成的小群体参与游戏，每个游戏实例与其他游戏实例完全分离。实例可以从主游戏世界或虚拟“大厅”环境访问，允许网络连接的玩家找到其他愿意的游戏参与者。通过扩展不同实例之间的可用交互，以及实例与游戏世界或大厅之间的可用交互，我们探索了新的游戏功能，并建立了扩展游戏可扩展性的进一步技术，允许更多玩家同时玩游戏，同时确保有效的底层实现。我们的工作将提供合适的新游戏一致性模型，以及在我们的猛犸研究游戏环境中的实验实施，以及商业游戏大厅环境。

Multiplayer games allow many human players to experience a game collectively, and have become an extremely popular genre. Ideally, these worlds are large, allowing a large number of people from across the world to interact. Network and computational resources, however, do not scale to larger (massive) sizes easily for distributed game environments, and current solutions involve various restrictions on virtual game world size, player interactivity, or ability to ensure each player simultaneously sees the same state. We base our proposal on an extension of current "instanced" game models, where players do not all play concurrently, rather a smaller group of at most around 100 players participate in a game, with each game instance quite separate from others. Instances can be accessed from a main game world or virtual "lobby" environment that allows network connected players to find other willing game participants. By extending the available interactions between disparate instances, and between instances and the game world or lobby we explore new game functionality as well as establish further techniques for extending game scalability, allowing more players to simultaneously play a game while ensuring an efficient underlying implementation. Our work will provide suitable new game consistency models, as well as experimental implementation in our Mammoth research game environment, and also a commercial game lobby environment.