Comparison and analysis of the revenue-based adaptive queuing models
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Comparison and analysis of the revenue-based adaptive queuing models
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This paper proposes a model to maximize revenue whileserving customers with different Quality-of-Service (QoS)requirements. A provider's goal is to share resources betweenactive customers to ensure all QoS requirements. Atthe same time, a provider is interested in maximizing therevenue. Since the amount of active users varies a providerfunctioning can be optimized by allocating different portionsof resources. The proposed model is based on theWeighted Fair Queue policy, which is extended so that theusage-based revenue criterion can be used to dynamicallyadapt weights. The model is flexible in that different servicesare grouped into service classes and are given differentperformance characteristics. It guarantees the QoSrequirements and maximizes the revenue by manipulatingweights of the WFQ model. The simulation of the proposedmodel considers a single node with several service classes.It is shown that the total revenue can be significantly improvedwhen compared to a non-adaptive approach.