Adaptive scheduling method for maximizing revenue in flat pricing scenario
End-to-end quality of service is critical to the success of current and future networked applications. Applications, such as real-time actions and transactions, should be given priority over less critical ones (such as web surfing). Furthermore, many multimedia applications require delay or delay variation guarantees for acceptable performance. Weighted fairness is also important both among customers or aggregates (depending on the tariff or subscription), and also within an aggregate (for example, to prevent starvation among sessions or service categories). This paper presents an adaptive scheduling algorithm for the traffic allocation. We use flat pricing scenario in our model, and the weights of the queues are updated using revenue as a target function. Due to the closed form nature of the algorithm, it can operate in the nonstationary environments. In addition, it is nonparametric and deterministic in the sense that any assumptions about connection density functions or duration distributions are not made.
flat pricing dynamical tracking of network parameters revenue maximization qos