Abstract
To reduce congestion, numerous routing solutions have been proposed for backbone networks, but how to select paths that stay consistently optimal for a long time in extremely congested situations, avoiding the unnecessary path reroutings, has not yet been investigated much. To solve that issue, a model that can measure the consistency of path latency difference is needed. In this paper, we make a humble step towards a consistent differential path latency model and by predicting base on that model, a metric Path Swap Indicator (PSI) is proposed. By learning the history latency of all optional paths, PSI is able to predict the onset of an obvious and steady channel deterioration and make the decision to switch paths. The effect of PSI is evaluated from the following aspects: (1) the consistency of the path selected, by measuring the time interval between PSI changes; (2) the accuracy of the channel congestion situation prediction; and (3) the improvement of the congestion situation. Experiments were carried out on a testbed using real-life Abilene traffic datasets collected at different times and locations. Results show that the proposed PSI can stay consistent for over 1000 s on average, and more than 3000 s at the longest in our experiment, while at the same time achieving a congestion situation improvement of more than 300% on average, and more than 200% at the least. It is evident that the proposed PSI metric is able to provide a consistent channel congestion prediction with satisfiable channel improvement at the same time. The results also demonstrate how different parameter values impact the result, both in terms of prediction consistency and the congestion improvement.
Highlights
The traffic in backbone networks has grown exponentially during the past two decades
The current path chosen by OSPF was R4-5-6, the orange curve in Figure 3a, and the blue curve is the latency of the backup path R4-1-2-6
Inside the window the Path Swap Indicator (PSI) is on, which means route should be switched to the backup path, and vice versa—outside the window the PSI is off, during which the route should be switched back to the current path, because it indicates that the current path has a better condition than the backup one according to our algorithm
Summary
The traffic in backbone networks has grown exponentially during the past two decades. Deemed as the generation networking, SDN was adopted by most of the recent work on adaptive routing, taking advantage of the centralized control strategy [5,6,15,16,17,18] These works involve one or more centralized controllers in their design, collecting global knowledge of the network to achieve optimized QoS for the entire network. A metric of consistent adaptive routing is proposed to improve load balancing and achieve consistent adaptive routing by analyzing real-life backbone traffic and predicting future congestion situations. Relying on learning the latest congestion situation and predicting the onset of a long-term and consistent channel deterioration, the routing strategy is able to make a long-term effective decision on the path switching, i.e., to switch the path if and only if the target path’s condition will be better than the current one over a relatively long period of time.
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