Subject: [MLS] Some data

From: Richard Barnes <[email-masked]>
Date: Tue, 15 Oct 2019 11:56:49 -0400

Hey all,

I've managed to extract histories for a collection of Webex Teams groups, a
total of 950 groups (including 1-1 conversations) ranging in size from 2 to
5000 members.

What data would people be interested in understanding from this data set?

I'm currently working on extracting "transcripts" of
init/add/remove/message events, with the idea that they could be fed into a
simulator attached to some library to generate counts of actual crypto
operations.  But it would also be easy to extract summary statistics, such
as the inter-arrival rates for Adds / Removes, Add:Remove ratios, etc.

One caveat is that Webex Teams allows self-add and self-remove, in addition
to adds/removes initiated by someone in the group.  Self-adds account for
~6% of all adds, but self-removes (leaves) are 98% of all removes.  The
latter might be an artifact of "server-initiated" removes being reflected
as self-removes, but of course server-initiated removes have similar issues
w.r.t. MLS.

--Richard

---

From: Brendan McMillion <[email-masked]>
Date: Wed, 16 Oct 2019 14:27:25 -0700

This is great!

I’d love to understand:
- The distribution of message sizes (message = what the user actually types)
- Does WebEx Teams use a sub-protocol for messaging metadata, like XMPP? How much overhead does it add?
- How important do WebEx engineers consider compression? Given that human language / XML can have such high compression ratios

> On Oct 15, 2019, at 8:56 AM, Richard Barnes <[email-masked]> wrote:
> 
> Hey all,
> 
> I've managed to extract histories for a collection of Webex Teams groups, a total of 950 groups (including 1-1 conversations) ranging in size from 2 to 5000 members.
> 
> I'm currently working on extracting "transcripts" of init/add/remove/message events, with the idea that they could be fed into a simulator attached to some library to generate counts of actual crypto operations.  But it would also be easy to extract summary statistics, such as the inter-arrival rates for Adds / Removes, Add:Remove ratios, etc.
> 
> One caveat is that Webex Teams allows self-add and self-remove, in addition to adds/removes initiated by someone in the group.  Self-adds account for ~6% of all adds, but self-removes (leaves) are 98% of all removes.  The latter might be an artifact of "server-initiated" removes being reflected as self-removes, but of course server-initiated removes have similar issues w.r.t. MLS.
> 
> --Richard
> _______________________________________________
> MLS mailing list
> [email-masked]
> https://www.ietf.org/mailman/listinfo/mls

---

From: Paul Grubbs <[email-masked]>
Date: Wed, 16 Oct 2019 17:38:26 -0400

Thanks much Richard - this is very cool! Will any of the data (or maybe
just these transcripts you're generating) be released publicly?

In addition to what Brendan already suggested, just some basic summary
statistics about the distribution of group sizes would be really useful.
Also, understanding the frequency of group changes (adds/removes) vs
"regular" messages would help guide thinking about tradeoffs in designing
protocols, MLS and otherwise.

On Wed, Oct 16, 2019 at 5:27 PM Brendan McMillion <brendan=
[email-masked]> wrote:

> - How important do WebEx engineers consider compression? Given that human
> language / XML can have such high compression ratios
>
> > I've managed to extract histories for a collection of Webex Teams
> groups, a total of 950 groups (including 1-1 conversations) ranging in size
> from 2 to 5000 members.
> >
> > One caveat is that Webex Teams allows self-add and self-remove, in
> addition to adds/removes initiated by someone in the group.  Self-adds
> account for ~6% of all adds, but self-removes (leaves) are 98% of all
> removes.  The latter might be an artifact of "server-initiated" removes
> being reflected as self-removes, but of course server-initiated removes
> have similar issues w.r.t. MLS.
> >
> > --Richard

---

From: nalini elkins <[email-masked]>
Date: Wed, 16 Oct 2019 18:53:01 -0700

Richard,

simulator attached to
> some library to generate counts of actual crypto operations.  But it
would also be easy to extract summary statistics, such as the inter-arrival
rates
> for Adds / Removes, Add:Remove ratios, etc.

I don't know if this is appropriate but if you have per packet payload (or
payloads which can be distinguished that way),  it is possible to use the
TXT2PCAP program of Wireshark to create a PCAP or packet trace.

Once you have a packet trace, then it is quite easy to do summaries, flows,
etc.  I remember doing this with the early TLS1.3 code.

It this seems useful, I am happy to help.

Nalini

On Tue, Oct 15, 2019 at 8:57 AM Richard Barnes <[email-masked]> wrote:

> Hey all,
>
> I've managed to extract histories for a collection of Webex Teams groups,
> a total of 950 groups (including 1-1 conversations) ranging in size from 2
> to 5000 members.
>

-- 
Thanks,
Nalini Elkins
President
Enterprise Data Center Operators
www.e-dco.com

---

From: Cas Cremers <[email-masked]>
Date: Thu, 17 Oct 2019 10:46:23 +0200

Hi Richard,

That's really helpful, thanks!

Some of these have been mentioned already, but to ground some of our
design choices it would be useful to have:

 - Distribution of group sizes: one might expect many 1-1 and few of
5000, but how is the distribution in between?
 - (Main classes of) typical group dynamics over time, especially for
the larger groups. E.g., frequency of group size changes, typical size
curve over time, lifetime of groups, ...

I would it be convenient if we could also look into the data a bit
more (in how many groups are typical users / subgroup relations) but I
would assume that's not possible or desired.

Best,

Cas