On Mon, Jun 26, 2017 at 03:59:20PM +0000, Piotr Gregor wrote: > Hi Sebastian, > > I think Rolf understands that but he is simply interested in deviation anyway. > I can agree deviation gives some more insight into nature of latency observed even if it is clear > then max peak is what determines real-timeness of the setup. One may be interested in distribution > of latency - you may have two setups with same average and max peak while there is much less > meaningful peaks on one than on the other. > you have to be careful here - any statistics estimation of the maximum with e.g. asymtotic extreemvalue distribution is only valid if the data is in fact iid values. The problem is that it is not assured that you actually have a distribution (implying a stochastic process as source) but the max can be systematic problem e.g. SMIs or other HW effects that are do not follow a distribution at all - for estimation of extreemvalues the most important property is that the tail characteristic is resonably constant - any systematic effects could mess that up. So before trying to use any statistics you need to verify that you actually have a stochastic process at the core - and if you want to use simple metrics that apply if normal-distribution can be assumed you need to verify this assumptions first rt-systems are rarely (if ever) normally distributed. That ping is printing standard-deviations is a bit funny as network times need not be clean distributions at all (and by no means stable over time) and the tail characteristics of ping are dependent on systematic effects (e.g. bandwidth trhotling of providers etc.) so I would question the validity of such exercises - just producing numbers without checking precoditions is a well known method of missusing statistics. Even a ping in the local network is multimodal and calculating a std-dev on it is quite meaningless. as . For some rt-systems it is resonably to do statistical estimations of means and extreem values based on measurement sets like you find in the QA-Farm but you can not do that with a single data set - bascially you treat the data sets as samples and then you can derive predictions for the system maximum based on the distribution of the local maxima of each of the data sets (say 2h measurements each or so). Off-topic side-note: If you do have a statiscially deterministic system (stable distribution, iid assumption holds, homoscedasticity, etc.) at the root - then you can in fact compensate jitter by replicaion over cores. So depending on what you want to achieve with the system a well behaved distribution with a max of 500us can actually provide guarantees of 400us response-time if you replicate the task and let the "winner" continue and the "looser" (task-replica) go to sleep again without performing any actions. thx! hofrat -- To unsubscribe from this list: send the line "unsubscribe linux-rt-users" in the body of a message to majordomo@xxxxxxxxxxxxxxx More majordomo info at http://vger.kernel.org/majordomo-info.html
