From steveq@nist.gov  Wed Jun 11 03:12:40 2003
From: steveq@nist.gov (Stephen Quirolgico)
Date: Tue, 10 Jun 2003 22:12:40 -0400
Subject: [KP] Deriving Knowledge for the Knowledge Plane (paper)
Message-ID: <5.2.1.1.0.20030610220224.02b71c58@email.nist.gov>

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Content-Type: text/plain; charset="us-ascii"; format=flowed

Chris,


Attached is a short paper that presents some of our ideas regarding the
use of ontologies for capturing information from the KP's participating nodes
as well as the use of translators for mapping this information to a form
that may be used by the KP.  The purpose of this paper is to initiate 
discussion
about issues regarding knowledge representation, ontologies, translators, and
knowledge requirements.  It is also intended to highlight some of the issues
with respect to existing management information models and ontologies.


Steve




--
Stephen Quirolgico
National Institute of Standards and Technology
Advanced Network Technologies Division
100 Bureau Dr., Stop 8920
Gaithersburg, MD 20899-8920
Tel: (301) 975-8426
Fax: (301) 590-0932

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--=====================_536067812==_--


From pstone@cs.utexas.edu  Sun Jun 15 05:10:31 2003
From: pstone@cs.utexas.edu (Peter Stone)
Date: Sat, 14 Jun 2003 23:10:31 -0500
Subject: [KP] experimental design for the knowledge plane
Message-ID: <16072.1055650231@beartrap.cs.utexas.edu>

Dear knowledge plane list members,

First, by way of brief introduction, I am a professor of computer
sciences at the University of Texas at Austin.  My research specialty
is in artificial intelligence - specifically machine learning and
multiagent systems.  I have spent some time in the past thinking about
applying machine learning to network routing, though in an abstract
simulator that is admittedly far removed from the real challenges of
networking [1].  Recently, I have been thinking about ways to leverage
this and some of my other research results towards cognitive systems
and I am very intrigued by what I have seen on the know-plane website.

This message is in response to a thread started about a month ago in
which Chris Ramming called for discussion on a Distributed EMbedded
Internet TESTbed.  I've spoken with him about some ideas and he
suggested that I post them to this list.



> J. Christopher Ramming wrote:
>
> An extrapolation for brainstorming purposes, but
> presumably within reach for a DARPA program,
> would be to consider whether and how our research
> communities could benefit from a "Distributed
> EMbedded Internet TESTbed" (DEMITEST?) for
> cognitive networking comprising a set of
> independent and artificial (e.g. completely
> controlled) autonomous systems scattered
> across the open Internet, and which could
> be used to conduct a wide variety of
> experiments involving fault injection,
> reconfiguration, etc to exercise the capabilities
> we're interested in developing. 
> 
> What would DEMITEST look like?  What classes of
> experiment could it support?  Might it be the
> basis for developing an ongoing challenge for
> the emerging area of cognitive networking
> in the vein of Robocup [5]?  And what would
> that challenge problem look like?

I have been deeply involved in RoboCup from the beginning as well as
in the Trading Agent Competitions (see below) and have put some
thought into what makes for good challenge problems.  I will put forth
a rough outline of a proposal for DEMITEST here, with the full
understanding that it could not be taken as is, but with the hope that
it will spark some discussion and perhaps lead to something that is
fully specified.


1.  TOPOLOGY: Start with an overlay network with hundreds of machines
placed around the world.  Perhaps this could be done on an existing
overlay network, such as PlanetLab.  Or, given the resources, it could
be a new set of dedicated machines.  Create a simulated initial
network topology.  That is, some of the machines in the overlay
network could be considered endpoints, and others could represent
internal nodes such as routers.


2.  TRAFFIC PATTERNS: Model traffic patterns among nodes in the
overlay network in terms of jobs initiated by users.  Specifically,
each endpoint has a population of users, with each user having a
probability distribution governing its rate of initiating different
types of jobs such as:
 - downloading large files
 - sending email
 - browsing web servers 
 - streaming audio or video
 - etc.
Each type of job has different characteristics in terms of the packet
flow generated and in terms of what represents satisfactory
performance (see discussion on metrics below).  By construction, these
traffic patterns will be somewhat predictable (i.e. learnable), though
the user distributions should always change slowly; and periodic brief
changes in traffic patterns can be modeled in terms of rapid changes
in user desires, for instance a spike in popularity of a particular
web site or streaming app.


3.  NETWORK CHANGES: Model network changes, both routine and
catastrophic.  For example, machines/links in the overlay network can
go down or change in bandwidth, or new nodes can be added.  If
desired, denial of service attacks and sharp spikes in traffic can
also be modeled.  Of course some of this will come for free, since the
nodes in the overlay network are connected via the real internet.  But
for the purposes of measurement, changing patterns in the real
internet represents noise/uncertainty in the environment (so multiple
or long-lasting runs will be needed for making significant
conclusions).  The simulated network changes in the overlay network
itself can be controlled and replicated precisely.


4.  METRICS: The key to making the task measurable enough for a DARPA
program will be the specific performance metrics used.  Some potential
metrics are easy to define, while others are harder.  Some candidates
include:

 - job performance (e.g. file download time, email delivery time, web
 page completion time, consistency of streaming packet delivery,
 etc.).  Different jobs have different costs associated.  For
 instance, it is more important that web pages download quickly than
 it is that email arrive instantly; and it is more important that data
 for streaming apps arrive at a consistent rate than that data for a
 file download does.  I think it's very important to make measurements
 in terms of JOBS, not in terms of packets so that the knowledge plane
 can reason about how to balance the requirements of different jobs.

 - availability: what percentage of the time is each node reachable
 from each other node?

 - security: if there is a simulated attack, how long does it take to
 detect it; how long does it take to curb it; and what percentage of
 nodes are affected?

 - adaptability: how long does it take to get back to a steady state
 after permanent small changes in the network?  How well does the
 network react to temporary spikes or catastrophic failures.

 - manageability (the hardest to measure): what is the cognitive load
 on an expert human operator helping to managing/reconfigure the
 network?  How many decisions need to be made?  How long does it take?
 What is the eventual performance.


5.  TESTS: I see two basic ways of creating challenge problems in this
setup: the global perspective and the ISP perspective.  

 - The global perspective is that a single participant in the program
 controls the entire network for a given run.  That is, the
 participant gets to define what network statistics are measured, how
 and when they are transmitted, and how each node detects and responds
 to changes in traffic, topology, etc.  In this case, learning
 algorithms that are run could be distributed throughout the whole
 network.  Any overhead generated in the form of extra traffic for the
 purposes of measurement and/or learning would of course affect the
 performance metric above.

 - The ISP perspective is that each participant in the program
 controls a subset of the nodes in the overlay network and tries to
 maximize the satisfaction of its own users without any regard for the
 other ISPs' users.  This scenario could be realized as a
 market-oriented approach in which each ISP controls some network
 resources and has a need for others.  ISPs could bid for bandwidth
 from each other or exchange promises to give each others' packets
 higher priority.

In both of these cases, multiple runs with identical initial
conditions and patterns of traffic and topology changes could be run
to isolate the effects of small changes in the algorithms.  At the
outset, a "how-it's-done-now" approach should be implemented as a
baseline.

Note that at least in the global perspective, the above framework need
not define a single challenge problem in the form of a competition.
That is, each participant could demonstrate its improvement over
baseline with regards to a different metric.  For example, a project
focussed on security might show improved detection and response times
to intrusions even at a small cost to performance, whereas another
project might focus on improving the manageability metric.



Clearly, this is not yet a fully-specified challenge problem or set of
challenge problems.  And given my AI perspective, I assume there will
be some needed revisions from the networking side.  But I hope that
it's at least useful as a starting point.



> Bikash Sabata wrote:
> 
> On another note ... since you mention robocup in the subject line; there 
> is another competition -- Trading Agents Competition ( 
> http://ai.eecs.umich.edu/people/wellman/pubs/iaai02ext.pdf ). Briefly it 
> is a competition with agents that participate in 5 different auctions to 
> obtain a bundle of goods for the user at the best value. There are some 
> very interesting lessons learned. For me the two most significant ones are
> 
>   1. Learning based method does not necessarily does the best. Simple
>      strategies (open loop -- with no feedback) work great many times.
>   2. Although this was framed as an intelligent multiagent problem, no
>      one in the competition tried to model and learn the behavior of
>      their adversaries.

As I mentioned above, I have been deeply involved in the Trading Agent
Competitions from the start and led the teams that finished in first
place the first two years (2000 and 2001).  While both of the above
lessons are true to some extent, they should be explained a bit more
fully.

    1.  There was a little bit of learning (at least "adaptivity") in
    our 2000 approach, but yes, it was a fundamentally open loop
    approach.
    2.  In 2001, a different team took an open loop approach, whereas
    we took a fundamentally closed-loop, learning-based approach that
    did in fact learn different models for different adversaries
    (it learned the aggregate effects of their actions as opposed to
    predicting individual bids) [2].  That year, our agent and the
    open-loop agent essentially tied for first place (each finishing
    first according to one of the two metrics).

So, while it's true that learning does not NECESSARILY do the best,
and that adversary modeling wasn't a prevalent technique, both
learning and adversarial modeling did play some role in successful
approaches.



> Thomas G. Dietterich wrote:
> 
> I would like to speak against challenge problems.  I believe they overly
> focus the research and become a barrier to addressing the fundamental
> problems that must be solved to achieve DARPA's goals.  They mostly
> serve to exclude research groups that have important ideas which don't
> exactly match the challenge problems.

As a frequent participant in competitions and challenge problems, I am
sensitive to this issue and understand that there are both pros and
cons to defining specific challenge problems.  Note that there is some
discussion above about how the scenario I've proposed could be used as
a flexible platform in which each project defines its own performance
metric with only the burden of showing an improvement over a common
baseline in SOME way.  However, I do also think that there are some
advantages to a large (project-sized) community focusing on a common
problem.  I have a paper that outlines my views on the pros and cons
of such competitions [2].
	



In any case, I hope that this message can spark some renewed
discussion on this list.  If so, I look forward to hearing your
reactions.

Best Regards,

	Peter


[1] "TPOT-RL Applied to Network Routing":
http://www.cs.utexas.edu/~pstone/Papers/2000ml/routing.ps

[2] "ATTac-2001: A Learning, Autonomous Bidding Agent":
http://www.cs.utexas.edu/~pstone/Papers/2002amec/tac2001.ps

[3] "Multiagent Competitions and Research: Lessons from RoboCup and
TAC": http://www.cs.utexas.edu/~pstone/Papers/2002robocup/comps.ps



___

Peter Stone, Assistant Professor   
Department of Computer Sciences    phone: 512-471-9796 
The University of Texas at Austin  fax:   512-471-8885
1 University Station C0500         pstone@cs.utexas.edu 
Austin, Texas 78712-1188  U.S.A.   www.cs.utexas.edu/~pstone

 




From braden@ISI.EDU  Fri Jun 20 23:01:31 2003
From: braden@ISI.EDU (Bob Braden)
Date: Fri, 20 Jun 2003 15:01:31 -0700 (PDT)
Subject: [KP] Paper completed
Message-ID: <200306202201.PAA16550@gra.isi.edu>

NOW on the know-plane web page:

David Clark, Craig Partridge, Chris Ramming, and John Wroclawski, "A
Knowledge Plane for the Internet", ACM SIGCOMM 2003 FDNA Workshop,
Karlsruhe, August 2003. {PDF}.

From craig@aland.bbn.com  Fri Jun 20 23:12:00 2003
From: craig@aland.bbn.com (Craig Partridge)
Date: Fri, 20 Jun 2003 18:12:00 -0400
Subject: [KP] Paper completed
In-Reply-To: Your message of "Fri, 20 Jun 2003 15:01:31 PDT."
 <200306202201.PAA16550@gra.isi.edu>
Message-ID: <200306202212.h5KMC0qG093858@aland.bbn.com>

In message <200306202201.PAA16550@gra.isi.edu>, Bob Braden writes:

>David Clark, Craig Partridge, Chris Ramming, and John Wroclawski, "A
>Knowledge Plane for the Internet", ACM SIGCOMM 2003 FDNA Workshop,
>Karlsruhe, August 2003. {PDF}.

Well, small correction -- it is the first paper of the SIGCOMM Conference
(not the FDNA workshop).  But it is on-line now, and represents a lot
more thinking than the prior draft.

Craig

From braden@ISI.EDU  Fri Jun 20 23:15:28 2003
From: braden@ISI.EDU (Bob Braden)
Date: Fri, 20 Jun 2003 15:15:28 -0700 (PDT)
Subject: [KP] Paper completed
Message-ID: <200306202215.PAA16609@gra.isi.edu>

  *> From craig@aland.bbn.com  Fri Jun 20 15:12:50 2003
  *> To: Bob Braden <braden@ISI.EDU>
  *> cc: know-plane@ISI.EDU
  *> Subject: Re: [KP] Paper completed 
  *> Date: Fri, 20 Jun 2003 18:12:00 -0400
  *> From: Craig Partridge <craig@aland.bbn.com>
  *> X-AntiVirus: scanned by AMaViS 0.2.1
  *> 
  *> 
  *> In message <200306202201.PAA16550@gra.isi.edu>, Bob Braden writes:
  *> 
  *> >David Clark, Craig Partridge, Chris Ramming, and John Wroclawski, "A
  *> >Knowledge Plane for the Internet", ACM SIGCOMM 2003 FDNA Workshop,
  *> >Karlsruhe, August 2003. {PDF}.
  *> 
  *> Well, small correction -- it is the first paper of the SIGCOMM Conference
  *> (not the FDNA workshop).  But it is on-line now, and represents a lot
  *> more thinking than the prior draft.
  *> 
  *> Craig
  *> 
OOOOPS, I knew that!!!  Sorry, I will fix the citation.

Bob

From brad@cse.ucsc.edu  Wed Jun 25 05:10:14 2003
From: brad@cse.ucsc.edu (Brad Smith)
Date: Tue, 24 Jun 2003 21:10:14 -0700 (PDT)
Subject: [KP] Subscribe.
Message-ID: <200306250410.h5P4AEUt060171@x.cse.ucsc.edu>

Subscribe.

From braden@ISI.EDU  Wed Jun 25 16:42:47 2003
From: braden@ISI.EDU (Bob Braden)
Date: Wed, 25 Jun 2003 08:42:47 -0700 (PDT)
Subject: [KP] Subscribe.
Message-ID: <200306251542.IAA18338@gra.isi.edu>

  *> From know-plane-admin@ISI.EDU  Tue Jun 24 21:15:31 2003
  *> From: Brad Smith <brad@cse.ucsc.edu>
  *> To: know-plane@ISI.EDU
  *> Subject: [KP] Subscribe.
  *> X-BeenThere: know-plane@mailman.isi.edu
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  *> List-Help: <mailto:know-plane-request@mailman.isi.edu?subject=help>
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  *> Date: Tue, 24 Jun 2003 21:10:14 -0700 (PDT)
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  *> 
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Please see the hyperlink on the know-plane web page for
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Bob Braden