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API: adaptive proportional-integral algorithm for active queue management under dynamic environments

机译:API:动态环境下主动队列管理的自适应比例积分算法

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This paper studies active queue management (AQM), and proposes an adaptive proportional-integral (API) algorithm for AQM, based on an existing PI algorithm. API alms to expedite the control response through online measurement and adjustment. First, API needs to periodically measure average queue length and packet loss ratio; second, it can dynamically adjust the packet drop probability using the measured results. Due to the use of online measurement and adjustment, the queue length under API can be much more quickly converged to around the desired value q/sub 0/, and with a faster control response than PI, especially under heavy congestion. API is well suited to dynamic network environments where traffics change frequently and it is hard to predict them. The simulation results validate this advantage. Although API introduces online measurement and adjustment, it still maintains O(1) complexity and can be efficiently deployed in high-speed routers.
机译:本文研究了主动队列管理(AQM),并在现有PI算法的基础上提出了一种针对AQM的自适应比例积分(API)算法。 API可以通过在线测量和调整来加快控制响应。首先,API需要定期测量平均队列长度和丢包率;其次,它可以使用测量结果动态调整丢包概率。由于使用了在线测量和调整功能,API下的队列长度可以更快地收敛到所需值q / sub 0 /左右,并且控制响应比PI快,尤其是在拥塞严重的情况下。 API非常适合流量经常变化且难以预测的动态网络环境。仿真结果证明了这一优势。尽管API引入了在线测量和调整功能,但它仍然保持O(1)的复杂性,并且可以有效地部署在高速路由器中。

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