Abstract
Internet routers generally see packets from a fast flow more often than a slow flow. This suggests that network fairness may be improved without per-flow information. In this paper, we propose a scheme using Most Recently Used List (MRUL)-a list storing statistics of limited active flows that sorted in most recently seen first mode-to improve the fairness of RED. Based on the list, our proposed scheme jointly considers the identification and punish of the fast and unresponsive fast flows, and the protection of slow flows. Its performance improvements are demonstrated with extensive simulations. Different from the previous proposals, the complexity of our proposed scheme is proportional to the size of the MRUL list but not coupled with the queue buffer size or the number of active flows, so it is scalable and suitable for various routers. In addition, another issue we address in this paper is queue management in RED. Specifically, we replace the linear packet dropping function in RED by a judicially designed nonlinear quadratic function, while original RED remains unchanged. We call this new scheme Nonlinear RED, or NLRED. The underlying idea is that, with the proposed nonlinear packet dropping function, packet dropping becomes gentler than RED at light traffic load but more aggressive at heavy load. As a result, at light traffic load, NLRED encourages the router to operate in a range of average queue sizes rather than a fixed one. When the load is heavy and the average queue size approaches the pre-determined maximum threshold (i.e. the queue size may soon get out of control), NLRED allows more aggressive packet dropping to back off from it. Simulations demonstrate that NLRED achieves a higher and more stable throughput than RED and REM. Since NLRED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by NLRED.
Highlights
With the increasing popularity of stream media applications, the fairness of networks has attracted much research attention [1,2,3,4,5,6,7,8,9,10,11]
We have proposed a mechanism improving the fairness of Internet routers, which called Scalable Fair RED (SFRED)
Simulations show that the SFRED proposed has significantly improved the fairness of Random Early Detection (RED), with only limited resource usage
Summary
With the increasing popularity of stream media applications, the fairness of networks has attracted much research attention [1,2,3,4,5,6,7,8,9,10,11]. The complexity of SFRED is proportional to the size of the MRUL but not coupled with the queue buffer size or the number of active flows, so it is scalable and suitable for various routers. Another issue we address in the paper is efficient queue management in RED. There are some concerns on the suitability of this approach, since the schemes designed are usually more complicated than the original RED This renders them unsuitable for backbone routers where efficient implementation is of primary concern.
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