Modern Massively Multiplayer Online Games (MMOGs) allow hundreds of thousands of players to interact with a large, dynamic virtual world. Implementing a scalable MMOG service is challenging because the system is subject to high variabilities in the workload, and nevertheless must always operate under very strict QoS requirements. Traditionally, MMOG services are implemented as large dedicated IT infrastructures with aggressive over-provisioning of resources in order to cope with the worst-case workload scenario. In this paper we address the problem of building a large-scale, multi-tier MMOG service using resources provided by a Clo-ud computing infrastructure. The Cloud paradigm allows the service providers to allocate as many resources as they need using a pay as you go model. We harness this paradigm by describing a dynamic provisioning algorithm which can resize the resource pool to adapt to workload variabilities, still maintaining a response time below a user-defined threshold. Our algorithm uses a Queueing Network performance model to quickly evaluate different configurations. Numerical experiments are used to validate the effectiveness of the proposed approach.
Dynamic Scalability for Next Generation Gaming Infrastructures
S. Ferretti;
2011
Abstract
Modern Massively Multiplayer Online Games (MMOGs) allow hundreds of thousands of players to interact with a large, dynamic virtual world. Implementing a scalable MMOG service is challenging because the system is subject to high variabilities in the workload, and nevertheless must always operate under very strict QoS requirements. Traditionally, MMOG services are implemented as large dedicated IT infrastructures with aggressive over-provisioning of resources in order to cope with the worst-case workload scenario. In this paper we address the problem of building a large-scale, multi-tier MMOG service using resources provided by a Clo-ud computing infrastructure. The Cloud paradigm allows the service providers to allocate as many resources as they need using a pay as you go model. We harness this paradigm by describing a dynamic provisioning algorithm which can resize the resource pool to adapt to workload variabilities, still maintaining a response time below a user-defined threshold. Our algorithm uses a Queueing Network performance model to quickly evaluate different configurations. Numerical experiments are used to validate the effectiveness of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
