Felix Fietkau <[email protected]> writes: > Rate success probability usually fluctuates a lot under normal conditions. > With a simple EWMA, noise and fluctuation can be reduced by increasing the > window length, but that comes at the cost of introducing lag on sudden > changes. > > This change replaces the EWMA implementation with a moving average that's > designed to significantly reduce lag while keeping a bigger window size > by being better at filtering out noise. > > It is only slightly more expensive than the simple EWMA and still avoids > divisions in its calculation. > > The algorithm is adapted from an implementation intended for a completely > different field (stock market trading), where the tradeoff of lag vs > noise filtering is equally important. It is based on the "smoothing filter" > from http://www.stockspotter.com/files/PredictiveIndicators.pdf. > > I have adapted it to fixed-point math with some constants so that it uses > only addition, bit shifts and multiplication > > To better make use of the filtering and bigger window size, the update > interval time is cut in half. > > For testing, the algorithm can be reverted to the older one via > debugfs
This looks interesting! Do you have any performance numbers from your own testing to share? :) -Toke
