Wednesday, November 30, 2016

Principles of Communications -- Michaelmas Term 2016....weak ate (to Nov 30)

This week, wra up with Ad hoc Net capacity&coding tricks
+ systems structures
+ Course Overview - including 2 missing pieces
1/ didnt cover shared media (as was done in 1b)
2/ didn't cover traffic engineering&signaling (rsvp) as most the principles already covered in other lectures earlier in term (open & closed loop control, optimisation and fibbing).

Wednesday, November 23, 2016

Principles of Communications -- Michaelmas Term 2016....week 7 (to Nov 25)

scheduling & switching last week and this week - some associated info:



huis clos shows up in multicores, routers, and data centers:-)

will not cover shared media, friday, as was done in 1b physical & dala link layer really nicely already, so revise that - 
instead, will move on to ad hoc/mobile networking capacity *might talk about opportunistic networks and firechat too:-)

Friday, November 11, 2016

Principles of Communications -- Michaelmas Term 2016....week 5 (to Nov 11)

This week was feedback control, theory &
optimization (routing and congestion pricing)...a bit math/algebra/calculus heavy methinks (supervisions should work through one or two examples of a PID controller for different systems
and how you show stability&long term operating point) - next week, real world TCP, then scheduling.

Friday, November 04, 2016

Principles of Communications -- Michaelmas Term 2016....week 4 (to Nov 4)

Have  covered Sticky Random Routing (DAR material here) + Network Coding for TCP

see wikipedia for Gaussian Elimination, + Linear Network Coding articles - best source/explanation I can find

And Open Loop Flow Control (including leaky bucket regulators/policiers)

Next week, closed loop/feedback control, and underlying theory for controller design and stability/efficiency analysis.

Friday, October 21, 2016

Principles of Communications -- Michaelmas Term 2016....week 3 (to Oct 21)

Done centralised/hybrid routing/fibbing -- as someone pointed out, forwarding continues if a central controler caches - depends on timeout in openflow added state/fib entries - with fibbing, the timeout will be whatever OSPF Or equiv does - so a comparison is potentially a bit more subtle than as presented.
Also, SDN/Openflow lets you add entries based on 5-tuple, whereas fibbing lets you add by destination only.....so potentially more fine grain choices in SDN, even if at the cost of more state - so the "prefix hijack" in fibbing is neat, but not the last word in adding custom routes...(e.g. if you wanted by source, needs more state than can be added by fake Link State Advert - as far as I can see)

rest of week was on BGP - why, what, how, where, when, and why not!

Friday, October 14, 2016

Principles of Communications -- Michaelmas Term 2016....

Finishing up end of week 2 (lecture 4) with Compact routing
having done background graphs & reminder of routing basics.

See the  slides page for lecture material + links to papers with more detail if you want (where not covered in books -

I've also added some more pointers to background book like reading on the course materials page for people that want to read more around the area, as there's no specific single book that covers all the course materials.

ttfn

Friday, September 09, 2016

fairness, machine learning, versus optimal stopping and cognitive bias

There's a bunch of work in making sure that machine learning systems are, in some carefully defined sense, fair - see for example the MPI work by Krishna Gummadi, in removing biases in various ML use cases (e.g. gender as an explicit or implicit discriminator).

For me there's a really subtle problem here which links between this work and other problems of Optimal Stopping and Cognitive Biases, and how one choose to define fairness in ML and the feedback loop between this and human society and the views we take on each other.

So lets take two simple use cases:

1. Admissions to University and Gender

Imagine a Computer Science department has 100 applicants a month, over 3 months for 50 places and wants to pick the best 50 people. Naive use of Optimal stopping would say wait til you have 37% of the applicants (111 people), then pick. What if the population is drawn differently by gender - e.g. out of every 100 applicants, only 1 is female. Lets say this is because applicants are self selecting based on the position in the ability of their own sub-population.. You have about a 1/2 chance of having 0 women in the admissions. The feedback to the population in society is you have to be in the top 1% of female applicants, but in the top 18% of men. Assuming their isn't actually a gender basis for ability distribution. You've just built a system that re-enforces it. TO get out of this, you have to run a two-factor optimal stopping scheme. If you want to do this for other groups in society, it will get more complex too...

2. Stop&Search and Race

It may be the case that you stop and search people in safeguarding society by profiling individuals based on past cases of stopping and successfully apprehending miscreants. Lets say this leads to a higher probability of stopping people who "look middle eastern". Again there's a feedback loop between your "correct" but naive selection scheme, and how people behave - in this case, various cognitive biases in how society will regard the group you target, may lead to the group being marginalised, out of proportion to even your allegedly accurate statistical model. e.g. anchorism....or many others, will lead to over-weighting by society, especially since humans are risk averse.