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DCRA: Decentralized Cognitive Resource Allocation Model for Game as a Service

机译:DCRA:游戏即服务的分散式认知资源分配模型

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Game as a Service (GaaS) has rapidly emerged to the industry of cloud gaming. The power of GaaS lies on having one source of code with multiple users. Several systems were proposed to model GaaS. However, none has built a scalable and reliable model for such a service. The importance of having such a model lies on having an Internet-scale platform able to provide flexibility of different types of games genre and lower the barrier of end systems (i.e. mobile clients) while taking into consideration the probability of excessive loads and failures. In this paper, we implement a Distributed Cognitive Resource Allocation (DCRA) model to run mobile games on a large-scale distributed system. On the contrary of the existing centralized models, DCRA scales with the increase of mobile clients to handle high concurrent loads of clients' requests while providing a stable level of gaming experience. The results show that DCRA is able to scale well by providing almost fixed throughput and delay while increasing the clients requests load. Also, the system preserve its key features while simulating failures.
机译:游戏即服务(GaaS)已迅速涌入云游戏行业。 GaaS的强大之处在于拥有一个拥有多个用户的代码源。提出了几种系统来对GaaS进行建模。但是,没有人为这种服务建立可扩展且可靠的模型。拥有这样一个模型的重要性在于拥有一个互联网规模的平台,该平台能够提供不同类型游戏类型的灵活性并降低终端系统(即移动客户端)的障碍,同时考虑到过多负载和故障的可能性。在本文中,我们实现了分布式认知资源分配(DCRA)模型以在大型分布式系统上运行手机游戏。与现有的集中式模型相反,DCRA随着移动客户端的增加而扩展,以处理客户端请求的高并发负载,同时提供稳定级别的游戏体验。结果表明,DCRA通过提供几乎固定的吞吐量和延迟,同时增加客户端请求负载,能够很好地扩展。此外,系统在模拟故障时仍保留其关键功能。

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