考虑不同QoS功能的时变定价中的寡头竞争,提高网络服务提供商的收益

Cheng Zhang, Bo Gu, Zhi Liu, K. Yamori, Y. Tanaka
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引用次数: 2

摘要

由于用户的时间偏好不同,一天中不同时段的网络流量负载差异较大。网络拥塞可能发生在流量高峰时段。为了防止这种情况的发生,网络服务提供商(nsp)可以在一天的高峰时段为需求提供超额容量,或者使用动态时间相关定价(TDP)方案来减少流量高峰时段的需求。由于过度分配网络容量的成本很高,许多研究人员提出了TDP方案来控制拥塞并提高网络服务提供商的收入。据我们所知,所有这些研究只考虑了垄断的NSP情况。在我们之前的工作中,研究了双寡头垄断和寡头垄断的NSP案例。网络服务提供商试图通过设定与时间相关的价格来最大化其整体收入,而用户则根据自己的时间偏好、网络拥塞状态和网络服务提供商设定的价格来选择网络服务提供商。一个假设是每个NSP的服务质量(QoS)函数是线性的,这意味着QoS的退化程度与网络中的用户数量成正比。然而,在现实中,QoS的水平在达到某一点后可能会迅速下降,这并不能通过线性QoS函数反映出来。因此,凹形QoS函数是更好的选择。本文考虑了QoS函数为凹的情况。在不同的QoS函数下评估TDP。结果表明,在凹QoS函数下,TDP也是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Oligopoly competition in time-dependent pricing for improving revenue of network service providers considering different QoS functions
Network traffic load usually differs significantly at different times of a day due to users' different time preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all these studies consider only the monopoly NSP case. In our previous work, the duopoly and oligopoly NSP cases have been studied. NSPs try to maximize their overall revenue by setting time-dependent prices, while users choose NSPs by considering their own time preference, congestion statuses in the networks and the prices set by the NSPs. One assumption that has been made is that Quality of Service (QoS) function of each NSP is linear, which means that the level of QoS degradation is proportional to the number of users in the network. However, in reality, the level of QoS may degrade rapidly after a certain point, which is not reflected through linear QoS functions. Therefore, concave QoS function is a better choice. In this paper, the case of concave QoS function is considered. TDP is evaluated under different QoS functions. The results shows that TDP is also effective under concave QoS functions.
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