Pavan,

Thanks for your answer.


  *   where the information for some links takes a few ms to get updated in the 
TED and for some others it takes tens of seconds to get updated in the TED

“tens of seconds” to update the TED i.e. to flood across the area… woaw, not a 
duration I had in mind indeed. I now better understand the TE use case, thanks.

Let’s hope that LSPs used by distributed SPT computations are flooded faster.

Regards,
--Bruno


From: Vishnu Pavan Beeram <[email protected]>
Sent: Friday, November 8, 2024 10:50 AM

@Tony - Thanks for jumping in.

Bruno, Hi!

For the example application cited in my previous email, knowing the average 
time (something along the lines of the suggestion in Tony's response) it takes 
for a topological element specific update (originated on a TE node) to get 
reflected in the TED used by a TE Path Compute node would be useful. If you 
have a network where the link state information gets updated uniformly on all 
TE Path compute nodes within 50ms, then this example application doesn't apply 
for you. But if there is a network, where the information for some links takes 
a few ms to get updated in the TED and for some others it takes tens of seconds 
to get updated in the TED, then knowing the relative delay statistics is useful 
(to cater to the cited example application).

Regards,
-Pavan

On Fri, Nov 8, 2024 at 3:26 PM Tony Li 
<[email protected]<mailto:[email protected]>> wrote:
Hi Bruno,

I haven't seen a reply from Pavan, so let me jump in...

I can give a simple distributed TE application example (there are several, but 
this should give a general idea how this information can be leveraged). In 
RSVP-TE networks, a TE path computed using a specific snapshot of TED can get 
rejected during signaling by a transit node because of bandwidth unavailability 
on a specific link (link bandwidth information in the snapshot of TED used 
during computation cannot always be “current”). When ingress gets notified of 
this “error” via RSVP signaling, the link in question is avoided in the 
subsequent path computation and an alternate path is sought. Most 
implementations tend to use a configurable “hold time” to determine how long 
this link needs to be avoided. The awareness of the distribution delay 
statistics can potentially be used by implementations to dynamically determine 
an appropriate “hold time” for a given TE link (instead of using a blanket 
topology-wide configuration).



By « delay » do you mean the IS-IS flooding time across the flooding domain or 
do you mean something else?

Yes, the flooding time across the area.


If this is the flooding time, what distribution delay statistics do you have in 
mind?

Mean plus 2x std dev. would be a good guess at a cutoff. I reserve the right to 
update this based on future experience.


In current deployment and as a goal in order to neglect this flooding time?

e.g., if flooding time could be reduced to 50 ms would you still need this 
timestamp?

We cannot guarantee that flooding time can be reduced to 50ms.  While fast 
flooding is of course a good thing, reality is a harsh mistress. Other 
implementations might be in the flooding path. Old software will be in the 
network for a very long time. So measuring reality would be helpful.

T


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