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Demand Forecasting in P2P and GRID Systems

机译:P2P和GRID系统中的需求预测

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In this paper we propose to analyze and forecast requests for data objects in P2P or GRID systems by using Box and Jenkins time series modelling. Thereby we presume a hybrid system providing a centralized instance named as Active Rendezvous Server (ARS). P2P participants are offering their disk capacity to the community but are not aware what content is stored on their disks (similar to Freenet). We furthermore assume that popularity requests regarding individual content objects may change rapidly. The ARS performs popularity forecasts, which base on past request observations. Thus, the ARS is able to create replicas before the request boost occurs. As an example, we apply a distributed Video on Demand P2P application.
机译:在本文中,我们建议使用Box和Jenkins时间序列建模来分析和预测P2P或GRID系统中对数据对象的请求。因此,我们假定一个混合系统提供名为Active Rendezvous Server(ARS)的集中实例。 P2P参与者正在向社区提供其磁盘容量,但不知道其磁盘上存储了什么内容(类似于Freenet)。我们进一步假设有关单个内容对象的受欢迎程度请求可能会迅速变化。 ARS根据过去的请求观察结果执行受欢迎程度预测。因此,ARS能够在请求提升发生之前创建副本。例如,我们应用了分布式视频点播P2P应用程序。

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