Friday, March 01, 2013

Publication Culture in Computing Research Design for impact: Rethinking academic institutions from the ground up


Why do we pretend that a publication is an event, rather than a part of an ongoing
process?
Computer Science is a Soft Subject. We create artificial systems/artefacts, and explore
their behaviours. We then report on this by talking about the behaviours at workshops
and conferences, and writing about the systems in papers for web pages, online
archives or even traditional print journals.
People assume that the artificial dichotomy between social events (workshops,
conferences) and archival repositories (journals and the like) is right. And some of the
debate about CS publication culture is oriented around trying to get people to use
these two modalities  more like other disciplines.
I think this is fundamentally wrong, and flies in the face of real scientific method.
Science does not deliver truth. It delivers things that work, and explanations that are
the best, current, simplest ones (c.f. Popper on Objective Knowledge, and of course
Occam’s Razor).
This means that a work is not the final word. It is just the current word. A goal of this
proposal is to reduce the “slice and dice” culture present today due to various perverse
incentives.
So the notion that an “archival paper” has been thoroughly checked and is infinitely
more “correct” than a “rapidly” reviewed conference submission is not tenable. There
is every chance that during the necessarily longer process to create an archival version
of a work, subsequent work has improved over the results. Hence much archived
material is actually less accurate because it is less timely.
The solution, for me, is to remove the notion of immutable publications, and admit
that we should update work continuously
This can apply to the entire process of socialising our work, hence a dialogue (or
multilogue) between authors, reviewers and readers, continually adds accuracy or
timeliness (or invalidates a work).  The same can apply to citations (which should, by
the way, have a “sign bit” to indicate whether the citation is building on fro ma work,
or citing it as the thing the new work invalidates).
Recognising this mutable publication model, would allow work to be presented at any
point along the “production line”, perhaps merely by “acclaim” - some work has
reached a point where it is mature enough and timely and interesting enough to merit
presentation at a social event (workshop or conference) - this could happen before or
after some notional point when it is recognized that an archival version is the current
best knowledge we have (a rare event).

Along side this continual process, I think one would have to abandon ideas of
anonymity in both authorship of work, and reviews/critiques (viz, the “dialogues”
mentioned above could only work in that open way). It goes without saying that code
and data associated with a systems’ behaviour should also be openly available as part
of this ongoing process (after all, since when did we declare code “bug free”
correctly? Why, therefore do we declare journal papers “correct”?).
Finally, this isn’t exclusive to Computer Science, but we built the tools that would
make the new approach viable, so we should use them first.
In fact we also have the next generation tools for this – we just need to combine Arxiv
with Github (versioning repositories)
1
.
Causes of paper count inflation.
CS is notable (in most branches at least) for submitted to conferences more than
journals. There are two pressures to do this
1. Urgency
2. Promotion
CS is a young disciple, and the young are noted for being impatient and impetuous -
our slogan might even be said to be “Publish Early and Publish Often”
2
.
Urgency
We live in a nanosecond world. More than other disciplines, partly because we built
it.
We supplied the tools and tool chains (the net, e-mail, the web, PDF, bibtex/latex,
databases, HotCRP/EDAS, etc)  that let us cooperate to develop ideas, systems,
results, and write papers faster, and deliver them for review, editing, and presentation
more quickly than any previous generation. Surely, other disciplines use the tools, but
we live and breath them.
As a result, there’s a feedback loop between publication of hot new work, This instant
gratification leads to an increase in the rate of submission.
Our profession has also a tendency (at least anecdotally) to attract a share of people
with OCD/Attention Deficit problems, who maybe (amateur psychologist’s hand
waving here) seek instant rather than deferred gratification.
                                             
1
 Github because we want distributed repositories to avoid re-concentrating power in
one place all over again.
2
 I could speculate here about whether these factors also contribute to the gender
imbalance in Computer Science as a profession and academic career (whether
directly, or simply as proxies for a root cause).


Promotion
Our academic research culture is funded largely by tax payers money (NSF, DARPA,
EU), and the tax payers seek metrics to see their money is well spent, and they seek
such feedback on an annual basis. Paper counts (and to a lesser extent, citation
counts) serve this. The same problem (inflation) has hit the industry research and
development world, where patents are a proxy for real work, and are rewarded.
The amount rather than significance of work is measured - hence, the aforesaid dice
and slice approach to work, producing minimal publishable units, and multiplying the
number of venues and publishable units year on year.
Because CS is young and vigorous, we have in the past been able to keep up with this
inflation. We are close to the limits though.
In the UK, we have a national Research Excellence Framework, for which researchers
in universities do not return all their work. Instead, every 5 years, up to 4 “outputs”
(e.g. papers) are returned. Secondly, and in addition, impact stories (pieces of work
10-20 years old, that have had a long term effect on the world, economically, socially,
or in terms of further developments in a discipline) are employed.
It will be interesting to see the outcome of this process, but for me, it is probably a
better basis for looking at some one person, or groups progress, so if we were to use
these sorts of indicators for tenure or similar, this would remove the aforesaid
perverse inventive to maximise the number of publications.
Acknowledgements
Thanks to Richard Clegg and Ioannis Avramopoulos for comments on this draft.



Wednesday, November 28, 2012

Principles of Communications 2012 up to L24 - week 8

Finished up with signalling, admission control, capacity planning today.

Comments on quantity of material and supervision q&a welcome 0- will work on this a lot for next year.

Was asked about reference (e.g. textbook) on WiFi  - not sure of good text (neither of Keshav's books cover this) but there's a nice tutorial online at Berkeley here

I'll see if I can find a better standard text on this, as it is quite interesting I feel!

Friday, November 23, 2012

Principles of Communications 2012 up to L21 - week 7

Have covered capacity of ad hoc wireless mesh, plus a bit on LP this week

As students pointed out, PR versus nPR is like comparing "reservation" and "staggered" forwarding in ad hoc mesh (i.e. pipelined forwarding is equiv to the first hop winner getting access to the whole path, whereas non pipelined case defers)

On SPT v. MST
http://www.me.utexas.edu/~jensen/exercises/mst_spt/mst_spt.html

xkcd has a (not so rare) educational cartoon on spectrum allocation which is useful:
http://xkcd.com/273/

LP - simplex solver - see
http://en.wikipedia.org/wiki/Simplex_algorithm


Wednesday, November 21, 2012

Principles of Communications - interim lesson

If someone wants to become a major hero, then fixing the Raspberry Pi linux USB/ethernet driver to remove "buffer boat" would be a very nice exercise - see here for references on what to do, and why - its an interesting lesson in
buffering, latency, and TCP/Queue Management interactions

http://www.teklibre.com/~d/bloat/Not_every_packet_is_sacred-Battling_Bufferbloat_on_wifi.pdf

Not every packet is sacred

Friday, November 16, 2012

Principles of Communications 2012 up to L18 - week 7

Scheduling

Randomness is your friend...see below too

Switching

should mention monsieur Clos!!
n.b. there may be an error in the slide on sorting/batcher switch - will check:)

Sharing

mention inventor of spread spectrum:
http://en.wikipedia.org/wiki/Hedy_Lamarr

A week full of s's...

Friday, November 09, 2012

Principles of Communications 2012 up to L15 - week 6

This week, Optimisation, and a start on Scheduling.

One question came up in optimisation - In the formulation of delay as
F/(C-F),  which I characterised as the load over the "headroom",
this is a dimensionless result - yes - its basically the average number of customers in an M/M/1 Queue (see Richard Gibbens slides from Computer Systems Modelling). However, in a work conserving router with a fixed speed output link, this translates into delay by multiplying by the
mean packet size/the output link line rate (which would have dimension time:)

Several people asked for more info about control theory - the chapter in Keshav's book (as per course web sight) is really quite clear (goes a bit past what I would ask, but knowledge is good, right?)...so recommend an hour reading that chapter - it also has exercises that are useful.
Keshav, S. (2011). Mathematical Foundations of Computer Networking. Addison-Wesley,
covers all but the graph theory bits of the maths I cover (and also covers some queueing and other performance things you might find useful as an alternative text, if you are attending Dr Gibbens' course too).

As per previous blog,
Keshav, S. (1997). An engineering approach to computer networking. Addison-Wesley
is also a useful text for the more protocol-oriented parts of this course.


Finally, if you are interested in the optimisation framework, then I recommend some of Frank Kelly's papers from the statslab - for example, this one briefly menions why we might take the sum of willing to pay times log of rates
w ln(x) 
as the  network view of the utility function..
.Fairness and stability of end-to-end congestion control

The two plots of functions of u_l (link utilisation of link l) are for
two different cost functions where the first one is exp(u_l)
and the second is n*(u_l^n) where n is a parameter (not the number of users - its just to generalise the function to a class of functions whose steepness/convexity can be varied by increasing n!

Friday, November 02, 2012

Principles of Communications 2012 up to L12 - week 5

Just made a hash of control theory....need to re-hash on monday to clarify- slides updated to show how G1 and G2 fit - see slide 23 on
control theory slides

Main point was to go through the decomposition of the control+gain+feedback
into separate boxes, to allow one to play with different controllers, and then re-compose to check the final transfer function for stability and for steady state error:- (slide update also fixes a couple of typos_

Hence, need to expand all the steps in the worked example with the video server and setpoint cpu load monitor

Will re-do on monday w/ additional steps for deriving the overall tranfer function in the s domain for the two different controllers of the CPU system whose basic (G0) behaviour is an integrator in time domain, so 1/s in Laplace transform/freq domain. this applies to the setpoint (Us) and the Mean Completion rate (Rc), so that when we look at these in the transform domain, we have the integral of them over time, which gives us a 1/s in the terms
for Us(s) -> Us/s and Rc(s) -> Rc/s


Looking at the slide where we first encounter G1 and G2, this is basically the design of a ne wsystem where G2 is what G0 was before (i.e. the video server modelled as an integrating service over time, but now with a new, regulated/controlled input), plus G2, which is the controller C, which has inputs which are the setpoint, and the demand, and outputs the new accepted/admitted flows which now go as inputs into our G2 (what was G0) who has an additional input, Rc(s) (or Rc/s).....

G0 = 1/s
Now add an (as yet unspecified) controller, C
and expand to the two stages, G1 and G2:

G1 = C.G0 / (1 + C.G0)
hence G1 = C/s (1+C/s) = C / (s + C)
G2 = G0 / (1 + C.G0)
hence G2 = 1/s / (1 + C/s) = 1 / (s + C)

Now our overall system is the composition of G1+G2, with
G1 handling the input decision, and G2 taking that plus the completion rate of work:
U(s) = G1.Uset/s + G2.Rc

so proportional controller just as C = K
and proportional-integral (PI) controller has C = K(1 + Ki/s)
where K and Ki are the constants to be chosen by designer:)


U(s) = C.Uset/s.(s+C)    + Rc / (s+C)       1.
which with C=K
 ( a proportional controller), gives
U(s) = K.Uset/s.(s+K) + Rc / (s+K)
Proportional controller:
stability: pole at s=-K, therefore ok.
error: lim of s.U(s)
= s . [K.Uset/s.(s+K) + Rc(s) / (s+K) ]
assume Rc(s) = Rc/s (i.e. Rc doesn't vary fast compared with feedback loop time)
= s.  [K.Uset/s.(s+K) + Rc /s.(s+K) ]
= [ K.Uset - R / (s+K) ]
which as s->0, goes to
Uset - Rc/K - so the error is Rc/K

For PI controller, put C = K(1 + Ki/s) in to 1 instead


G1=C G0 / (1 + C G0)
G2= G0 / (1 + C G0)

C = K(1+Ki/s)
G0 = 1/s

so G1 = K/s(1+Ki/s) / (1 +  K/s(1+Ki/s)) (* top and bottom by s^2)
 = (Ks + KKi) / (s^2 + Ks + KKi)

G2 = 1/s / (1 +  K/s(1+Ki/s)) (* top and bottom by s^2)
 = s / (s^2 + Ks + KKi)

response = Us G1 / s + Rc / s G2


....need to do this in tex:)

If people are interested in the stability of TCP's AIMD, then I have to say that its complex - to my knowledge, no-one has shown it for a network with FIFO "drop tail" queues, and heterogeneous RTTs - however, with an Active Queue Management system (like RED - see upcoming lectures on Scheduling and QUeue Management) there are some solutions - see
1. paganini's proof
and
2. INRIA work