From: Kallol Kumar Bagchi <kkb@iesd.auc.dk>
Newsgroups: comp.os.research
Subject: Tutorial program: MASCOTS '93
Date: 3 Oct 1992 02:27:52 GMT
Organization: University of California, Santa Cruz
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			   Tutorial Program
				   
  International Workshop  on Modeling, Analysis  and Simulation  of
	 Computer and Telecommunication Systems (MASCOTS'93)

	     Part of the 1993 SCS Western Multiconference
			on Computer Simulation
				   
	   Sponsored by the SCS, ACM, IEEECS and IFIPWG7.3
				   
			 January 17- 20, 1993
	  Hyatt Hotel, La Jolla, San Diego, California, USA

 ....................................................................

				   
				   
			   Tutorial Summary


T1: "Memory  Systems    for  Multiprocessors   and Scalable
    Architectures: Design Choices and Performance Evaluation"
    Alok Choudhary, Syracuse University

T2: "Stochastic Petri Net Modeling, Analysis and Simulation"
    Giovanni Chiola, U. Torino, Italy.

T3: "Empirical Source Modeling with TES Processes"
    Benjamin  Melamed, NEC  USA,  Inc.,    Princeton,  New Jersey  

T4: "Techniques and Tools for Performance and Reliability Modeling"
    K.S.  Trivedi, Duke University and
    Gianfranco Ciardo, College of William and Mary, VA.

T5: "Evaluation of Network Reliability Metrics"
    Gerardo Rubino, IRISA, FRANCE.

T6: "Software Tools for Visualization of Parallel and 
    Distributed Programs and Systems"
    Thomas L. Casavant, University of Iowa.

T7: "Common Mistakes and Games of Simulation and Analysis" 
    Raj Jain, Digital Equipment Corporation

T8: "G-networks -- New Models for Neural and  Queueing Networks" 
    Erol Gelenbe, Universit Ren Descartes (Paris V), France

 ....................................................................

				   
			     Tutorial  T1
				   
	Memory  Systems    for  Multiprocessors   and Scalable
       Architectures: Design Choices and Performance Evaluation
				   
		 Alok Choudhary, Syracuse University
		       17th January, Full day

Structure  and  Background:  
--------------------------
Multiprocessor architectures have gained popularity in recent years to
provide high speed  computing power beyond  the limits of uniprocessor
technology.  One can  classify multiprocessor systems according to the
logical and physical structure of  their  memory systems.  The problem
of developing scalable multiprocessors is largely that  of  building a
scalable memory systems.    In fact, as  processor   speeds  increase,
almost doubling every two years, it is critical that memory systems be
developed that  can supply instructions and  data at comparable speeds
to  fully utilize the  potential  of  these processors.  It is  widely
accepted that  memory systems design for multiprocessors  is even more
critical.

The memory hierarchy  of a  typical system can  be  divided into three
classes; namely; cache  memories, main memory  and Input-Output (I/O).
Although  I/O  has  several  forms,    such  as disks,  visualization,
communication with external systems  etc., for  this tutorial, we will
only  consider disk I/O.  Hence,  this  tutorial will discuss  several
aspects of  cache  designs, main  memory  designs and disk systems for
high-performance multiprocessor systems.

Intended Audience: 
-----------------
This tutorial is intended  for   architects, compiler designers,   and
operating system designers of multiprocessor systems, both in academia
and industry.  Attendees   are  expected  to be   familiar  with basic
principles in uniprocessor computer architecture, cache memories, main
memories and I/O systems.

Outline of the Contents:  
-----------------------
  1.  Multiprocessor Cache Memories
 
      * Cache Coherency Protocols.
          Snoopy Cache Coherency Protocols,
          Directory Based Protocols,
          Compiler Techniques to Maintain Cache Consistency, and
          TLB Consistency
      * Performance Evaluation Techniques
  2.  Scalable Architectures
      * Distributed Shared Memory Algorithms
      * Distributed Shared Memory Architectures
  3. Case Studies: DASH, Kendall Square, CM-5, Intel Paragon and others
  4. High Performance I/O
  5. Language  Support  for   Obtaining  Performance from   Parallel
      Machines for Scientific Computations (High Performance Fortran)


About the Speaker: 
-----------------

Alok Choudhary   received  his  Ph.D.   from University   of Illinois,
Urbana-Champaign, in Electrical and Computer Engineering, in 1989, and
M.S.  from University of Massachusetts, Amherst,  in 1986.   He joined
the faculty of  Department  of Electrical  and Computer Engineering at
Syracuse University in 1989.  He was a visiting scientist at IBM, T.J.
Watson Research during the  summers of 1987, 1988 and  1991.  He was a
research and  teaching   assistant with the  Electrical   and Computer
Engineering  Department  and  the  for High-Performance   and Reliable
Computing  at University of  Illinois, Urbana-Champaign  from  1986 to
1989.  He worked as a system analyst and designer from 1982-1984.

His main research  interests are in  parallel  computer architectures,
software   development environments   and  applications for   parallel
computers, and  performance evaluation.   He has   co-authored  a book
``Parallel Architectures and Parallel Algorithms for Integrated Vision
Systems'' published by Kluwer Academic publisher.  In addition, he has
written two  book chapters  on parallel  architectures and algorithms.
He  was awarded an  IEEE Engineering Foundation  Award for research in
parallel processing.  He served  as a guest  editor for IEEE  Computer
Special  Issue on ``Parallel  Processing for Computer Vision and Image
Understanding'', published in February 1992. He is also a guest editor
of Journal of Parallel and Distributed Computing  for a  special issue
on ``Parallel I/O Systems'' to be published in January, 1993.  He is a
member of  IEEE Computer Society  and   the  Association for Computing
Machinery.

  
   
  ....................................................................
				   
			     Tutorial T2

	Stochastic Petri Net Modeling, Analysis and Simulation

		  Giovanni Chiola, U. Torino, Italy
		 17th January, 1/2 day, second half

Structure and  Background: 
-------------------------
The  proposed tutorial can  be divided in  three or four parts.    The
first three   parts are   more introductory and could  tentatively  be
scheduled for 40--60 minutes of presentation each (thus  summing up to
2--3   hours).   The fourth part  (if  included)  could  present  more
advanced, research  oriented topics  and  range from 1  to  2 hours of
presentation.

Intended Audience:  
-----------------
The first three parts of this tutorial can be targeted to a very large
and   various   audience including: system   engineers,   hardware and
software designers, modellers,  simulationists,  practitioners as well
as technical managers.   The fourth part, if included,   would address
more specific interests of researchers  (coming both from academia and
industry), modellers, and simulationists.

Outline of the Contents:  
----------------------- 
    1. Basic concepts and mathematical tools.
       * Petri nets,
       * Qualitative properties of Petri nets,
       * Timing semantics of Petri nets, and
       * Reachability graphs and Markov chains.
    2. Basic techniques and software tools
       * Structural analysis,
       * Operational analysis,
       * State space enumeration,
       * Markov chain analysis, and
       * Discrete event simulation. 
    3. Examples of applications
       * Simple computer architectures,
       * Communication protocols,
       * Flexible manufacturing, and
       * Parallel programming, mapping, complex parallel architectures. 
    4. Advanced topics
       * Parametric Modeling and analysis,
       * Colored Petri nets,
       * Model symmetries and their exploitation,
       * Parallel and distributed simulation, and
       * Hierarchic Modeling and analysis.


About the  Speaker:   
------------------
Giovanni Chiola received  his Doctor Degree in Electronics Engineering
from the Polytechnic of Torino, Italy, in  1983.  From 1983 to 1985 he
worked   as a consultant  for the   Department  of  Electronics of the
Polytechnic of  Torino developing   software for stochastic  Petri net
analysis and applying it to computer architecture Modeling. In 1986 he
joined the  Faculty of  the Department  of  Computer  Science  of  the
University of Torino as a researcher.  In  1992 he became an associate
professor of computer science  at the University  of Torino.  Giovanni
Chiola spent several months at the University of  Zaragoza, Spain, and
at  the University of Paris 6,  France, as an  invited professor doing
joint research   on  Colored Petri nets  and   on  the  computation of
performance bounds for several classes of net  models. In July 1992 he
was the guest editor of a special issue of the Journal on Parallel and
Distributed  Computing on  Petri  net models of  parallel   computers.
Since  1984 he  has  published    more than 55   refereed   papers  in
international journals  and  conferences  on Petri  nets,  performance
models, distributed systems and graphic interfaces.  He also served in
the program  committee of  7 international conferences on  Petri nets,
distributed systems, performance evaluation and tools.  Dr.  Chiola is
a member of the IEEE Computer Society, the ACM, and Sigmetrics.  He is
the author of  the GreatSPN package  for the Modeling and analysis  of
generalized stochastic Petri nets.



 ....................................................................
				   
			     Tutorial T3

	     Empirical Source Modeling with TES Processes

	Benjamin Melamed, NEC USA, Inc., Princeton, New Jersey
		 17th January, 90 minutes, 1st half

Structure  and  background: 
--------------------------
This    tutorial describes  a    new  class  of methods   called   TES
(Transform-Expand-Sample)   for generating   autocorrelated     random
sequences.  TES methods are readily  implemented on a computer, have a
low computational    complexity   and give   rise  to  a    variety of
autocorrelation  functions  including monotone  as well as oscillatory
ones.  Since their autocorrelation functions  can be computed  by fast
numerical algorithms    (instead of    simulation-based    statistical
estimates), TES modeling can be accomplished via heuristic search over
a suitable parameter space.

Recently, an important applications domain has emerged with the advent
of high-speed communications  networks.   Here  the   newly   proposed
transport mechanism (e.g.    ATM), and  data services (e.g.,  database
queries,  full-motion  video etc.)  have been   shown to give  rise to
highly bursty  traffic.  From  a modeling  point of view,  performance
analysts are now required to provide answers  based on queueing models
with  correlated   arrivals  and services.   Standard queueing  models
assume that  customer   interarrival intervals  and  service times are
independent (GI),  each  being modeled  as  a  renewal process.  These
standard  models are easy to   simulate and under suitable assumptions
are even  analytically tractable;  unfortunately, they are  often poor
models of real-life  systems where correlations  do, in  fact, abound.
Worse still, various  workers have found that standard  models predict
overly  optimistic  performance  measures, such  as,  line lengths and
waiting  times.   The performance degradation    in   the presence  of
increasing correlations can be dramatic.

The tutorial will outline how to  construct a TES model from empirical
data (field measurements).  The resultant  TES  model is a  stationary
time  series  which   matches  the empirical    marginal  distribution
(histogram) and approximates  the empirical  autocorrelation function,
simultaneously.  The tutorial  will exemplify  the TES methodology via
several studies of real-life data from the domain of VBR (variable bit
rate) compressed video  at the  the  frame and GOB (group  of  blocks)
level.  It will conclude   with  a  brief description of  a  TES-based
modeling software package,  designed  to  support  visual  interactive
heuristic search for a suitable TES model.

Intended  audience: Performance analysts   and  modelers interested in
modeling  dependence in   random processes.  Simulation and technology
workers interested in   modeling  burstiness   in broadband    traffic
(particularly video service).

Outline of the contents: 
-----------------------  
     1. Problem Formulation 
     2. Brief Survey of the Past work
     3. TES Methods and the Empirical TES Methodology
        * A review of TES theory, 
        * The Empirical TES Methodology, and
        * Illustration with case studies from the domain of VBR 
          compressed video.
     4. TES Software
        * Description of the TEStool visual interactive modeling 
          package, and hands-on demo, subject to the availability 
          of facilities.

About the Speaker: 
-----------------
Benjamin Melamed is Head of the Performance Analysis Department at the
C Research Laboratories, NEC USA Inc., at Princeton, New Jersey, where
he  has been  employed since  1989.   His   research interests include
system  modeling and analysis,  simulation, stochastic   processes and
visual modeling environments.   Melamed  received  a B.Sc.  degree  in
Mathematics and Statistics  from  Tel Aviv University  in 1972,  and a
M.S.  and  Ph.D.  degrees in Computer  Science from the University  of
Michigan in 1973 and 1976, respectively.  From 1977  to 1981 he taught
at the department of  Industrial Engineering and Management Science at
Northwestern  University.     He joined     the  Performance  Analysis
Department  at  Bell  Laboratories  in  1981, and became   an AT  Bell
Laboratories Fellow in 1988. Melamed is a Senior Member of IEEE.

  
 ....................................................................
				   
			     Tutorial  T4
   Techniques   and Tools  for Performance and Reliability Modeling
				   
		  K.S.  Trivedi, Duke University and
	 Gianfranco Ciardo, College of William and Mary, VA.
		       17th January, full-day.

Structure and Background:  
------------------------

This tutorial consists of two parts, each of 1/2 day duration.

Part 1:  We present  techniques such  as fault-trees and Markov models
using  simple reliability  modeling  examples.  We  then review recent
progress   in  modeling   the   reliability/performability of  complex
fault-tolerant systems. The problems faced while modeling such systems
include the need to consider a very large state space, and the need to
perform a combined evaluation of performance and reliability.  Some of
the proposed solutions will  be discussed and  current tools (SPNP and
SHARPE) to facilitate evaluation of such systems will be described.

Part 2:  SPNP is a  software package for  solving stochastic Petri net
(SPN)   models.  Its  main   characteristics   are   a  powerful model
description language and very efficient  and stable numerical solution
algorithms.

The input language  for SPNP  is CSPL  (short  for C-based  stochastic
Petri  net language).  A  single CSPL file  can describe several SPNs,
because  it can be   completely parameterized.   CSPL  deals  with the
specification of both the SPN model and the outputs  to be computed by
the analysis.  A powerful reward-based approach allows the  modeler to
specify arbitrarily  complex  reward structures over  the markings and
the transitions among markings of the SPN.

The     basic   formalism underlying  CSPL allows   for  exponentially
distributed or zero firing times,  as  in the GSPNs, defined by Ajmone
et.  al.   in 1984.   Many additional  features,  though, increase the
flexibility of CSPL.

Regarding the numerical solution of the underlying stochastic process,
SPNP  allows for both transient and  steady-state analysis.  In either
case, both point measures (e.g., probability of an event at time  t or
in steady-state) and cumulative measures (e.g., total length of time a
condition is  verified during the  interval [0-t) or until absorption)
can be  computed.  Parametric sensitivity analysis  is  also provided.
This allows  to compute  the derivative of a  requested   measure with
respect to a real parameter such as the firing rate of a transition.

The numerical algorithms used are very stable  and  use sparse storage
techniques, thus allowing the solution of models with large underlying
state-spaces (models   with hundreds  of thousands  of   states can be
solved on a modern workstation).

Intended Audience:    
-----------------
This tutorial is particularly intended for  engineers dealing with the
reliability and  performance  analysis  of computer and  communication
networks and, more generally, for researchers  and students interested
in   Markov  models and stochastic    Petri  nets.  The  background is
elementary probability and statistics.

Outline of the contents:  
-----------------------
    1. Reliability Modeling
    2. SPNP and its use
       * The formalism underlying SPNP,
       * Organization and use of SPNP, and
       * Examples of practical applications solved using SPNP.

About the Speakers:  
------------------
Kishor S.    Trivedi received the  B.Tech.   degree  from  the  Indian
Institute  of Technology   (Bombay),  and M.S.  and  Ph.D.  degrees in
computer  science  from the  University of Illinois, Urbana-Champaign.
He  is   the   author of  a   text    Probability  and Statistics with
Reliability, Queuing and Computer  Science Applications, published  by
Prentice-Hall.  Both the  text,  and his related  research activities,
have  been  focused on  a unified mathematical modeling foundation for
computing system  reliability and performance evaluation.   Presently,
he   is a Professor  of   Electrical  Engineering at  Duke University,
Durham,  NC. He  has served as   a Principal Investigator  on  various
AFOSR, ARO,  Boeing,  Burroughs, DEC, IBM,  NASA,  NIH, NSF,  ONR, and
Union   Switch and  Signals funded   projects and as  a consultant  to
industry and research  laboratories.  He  is  a  co-designer  of HARP,
SAVE, SHARPE and  SPNP modeling packages.   He served as an  Editor of
the IEEE Transactions on Computers and and of Journal of  Parallel and
Distributed Computing.  He is a Fellow of IEEE.

Gianfranco Ciardo  is presently with  Department of  Computer Science,
College  of  William and  Mary,   Williamsburg,  VA.  He has  enormous
expertise  in formal  modeling,  petrinets and in reliable/distributed
system design.  He has served with distinction in both academia and in
industry. He is one of the main designers of the SPNP package.

  
 ....................................................................

			     Tutorial T5

	      Evaluation of Network Reliability Metrics

		    Gerardo Rubino, IRISA, FRANCE.
		 17th January, 1/2 day, second half.

Structure and Background: 
------------------------
Network reliability  theory is an  important tool  in the quantitative
evaluation of communication systems and, more generally, in the system
reliability area.   The  associated problems  are computationally hard
and  a  constant effort  has  been developed    in designing efficient
techniques to deal with them.  The tutorial intends, first,  to give a
general introduction to the theory and,  second, to present some basic
approaches that allow  to deal with real   size models.  Moreover,  we
shall also  consider the  problem  of performing  sensitivity analysis
without changing  the  complexity order  of  the  techniques used   to
compute the reliability.  The considered methods have been used in the
Modeling  of some  real  communication networks.    In particular, the
Monte Carlo approaches have allowed to evaluate systems  with hundreds
of components, even in the highly reliable case.

Intended Audience:  
-----------------
This tutorial is particularly intended for  engineers dealing with the
quantitative   analysis of  real  communication  networks   and,  more
generally,   for  researchers and  students   interested  in   network
reliability theory.    The background  is elementary  probability  and
statistics.

Outline of the contents:
-----------------------
     1. Overview.  
        * Overview of network reliability problems and solutions.  
        * Computational aspects.
     2. Models and metrics
        * Stochastic  graphs,  structure functions.   
        * Usual reliability metrics.
        * Other related system representations.  
        * Static and dynamic models.
     3. Exact analysis.	 
        * Background  on    graph  algorithms.     
        * Polynomial    simplifications (series-parallel, polygon-to-chain.)  
        * Disjoint  products   approaches.
        * The family of factoring algorithms. Algorithmic issues.
     4. Monte Carlo methods. 
	* Background  on  estimation    theory.    
        * Crude Monte   Carlo estimation.   	 
        * Dynamic  and  topologic   crude methods.    
        * Variance reduction techniques.   	   
        * The  dagger   sampling plan.  
        * The  general antithetic  algorithm.   
        * The case of systems with identical components.
     5. Sensitivity analysis.
        * Interest  of sensitivity  analysis.   
        * Sensitivities and simplification techniques.  
        * Sensitivities  and exact computations.  
        * Sensitivities and Carlo methods.

About the Speaker: 
-----------------
G.  Rubino has  a research  position  at   the INRIA  french  research
laboratory.  He his currently  working in   a Modeling  team,  dealing
particularly   with  reliability   theory, performability analysis and
performance evaluation.  He received a PhD in  Computer Science at the
University of Rennes in 1984.

  
 ....................................................................

				   
			     Tutorial  T6

       Software   Tools  for    Visualization of Parallel  and
		   Distributed Programs and Systems

	       Thomas L. Casavant, University of Iowa.
		       20th January, Full day.

Structure  and   Background:  
---------------------------
This tutorial will  overview the major  contributions to the field  of
visualization  as   applied   to     development  of  software     for
parallel/distributed computing  to date.   The emphasis will placed on
how to improve the  software development process for  high-performance
parallel computers through the   use of visualization  techniques both
for  program creation, as well  as  for  debug ging, verification, and
maintenance.

Outline of the Contents: 
----------------------- 
     1. Context and Background 
	    This section briefly motivates the area, and some of its
	    central problems.  It also provides background (if necessary) on 
            parallel processing architectures and algorithms, as well as 
	    simulation.
     2. Concepts in Visualization: A Spectral Approach
	    This section presents a completely new taxonomical
	    classification scheme to make sense of the area of visualization 
	    across a spectrum of uses from Visual Programming to Program 
	    Visualization. This spectral taxonomy is then used throughout 
	    the remainder of the tutorial to help make the presentation 
	    a continuous flow from  topic to topic without abrupt changes 
	    of setting.
     3. Scenario of Use -- Space Station
	    This short section illustrates how an application such as a space
	    station can involve parallel processing and how all stages
	    of design can benefit from visualization. Parallel software
	    development phases are emphasized.
     4. Gathering the Information: A Critical Problem

            This  section (which   can  be made  optional  depending on  
            available time) probes an important side  issue -- that of 
            gathering information from running systems such that the  
            information is  accurate and gathered without unnecessarily 
            encumbering the user.
     5. Broad Survey of Tools
	    This section conducts a brief examination of more than 50 tools.
	    The salient features of each is presented along with an assignment
	    of each to a point of the spectral taxonomy earlier presented.
     6. In-Depth Case Studies
	    This section looks at roughly 20 tools in greater detail. While 
	    the text contains in-depth material on each tool, the oral
	    presentation can be tailored to examine a subset most 
	    appropriate for the audience.
     7. Video Demonstration of Tools

	    This section consists of a videotape, which runs roughly 45
	    minutes, and which examines about 10 of the most
	    well-developed prototyped or production tools to date.
      8. Bibliography
	    The text contains an extensive bibliography to allow students to
	    gain further knowledge following the tutorial on their own.


Intended Audience: 
-----------------
As a relatively young field, this topic has appeal to individuals from
both academia as well as industry.  The area  is growing in importance
for a large  community  of parallel computer users.  Such   users  are
becoming  more and  more common   as parallel/distributed systems work
their way into more and more  application settings.  Advances  to date
have come mostly from  academics, but the  influence on industrial and
commercial settings for the future will be dramatic.

About The  Speaker: 
------------------
Thomas Lee Casavant received the B.S.  degree in Computer Science with
high distinction in 1982 from the University of Iowa, Iowa City, Iowa.
He received the M.S.  degree in Electrical and Computer Engineering in
1983, and the Ph.D.   degree  in Electrical  and  Computer Engineering
from the  University of Iowa  in  1986.  In 1986, Dr.  Casavant joined
the School of Electrical  Engineering   at  Purdue   University,  West
Lafayette,   Indiana  as an  Assistant  Professor  specializing in the
design and    analysis of parallel/distributed     computing  systems,
environments, algorithms, and programs.  From June  1987 to June 1988,
he was Director of the PASM Parallel Processing Project.

>From July  1988  until  July 1989 Dr.   Casavant  was  Director of the
Purdue EE School's  Parallel Processing  Laboratory.  He has developed
upper-division graduate courses in  distributed computing and parallel
processing,  an undergraduate  course  in Operating System Engineering
and  was   instrumental  in  the organization of   a joint  Electrical
Engineering/Computer Science  seminar series on Parallel Processing at
Purdue. In August, 1989, he  joined the faculty  of the Department  of
Electrical and Computer Engineering  at the University  of Iowa, where
he  is  an  Associate Professor.   There,  he  has continued  research
supported by  NSF, DARPA,  and  industrial  partners in the   areas of
parallel processing, visualization, and software tools.

Dr.   Casavant    has  published technical papers  on     parallel and
distributed computing and has presented his work at conferences in the
United States, Europe,  and Asia,  and has won  awards for both papers
and presentations at conferences.  His work in the area of distributed
computing includes design and implementation  of an  operating  system
for a point-to-point network  of  work-stations to behave as a  single
integrated transparent multiprocessor, contributions  to the design of
the AT 3B4000 distributed  multiprocessor  system at Bell Laboratories
in 1984, research in the  design and analysis  of load-balancing-based
scheduling algorithms, distributed algorithm modeling, and research in
theory     and       implementation      of    programming  tools  for
parallel/distributed computing.   He  has been  invited to lecture  on
topics related to computer systems, software engineering, and parallel
processing in general.

Dr. Casavant  is a senior member of  the  Institute of  Electrical and
Electronics Engineering  (IEEE) professional  society and a  member of
the Association of Computing Machinery (ACM) professional society.  He
served as guest co-editor for the August, 1991 issue  of IEEE Computer
on ``Distributed Computing Systems,'' as guest editor for the May 1993
issue   of the    Journal of Parallel    and  Distributed Computing on
``Visualization Tools for Parallel  Programs and Systems,'' as program
vice-chair  (1990)  and program  committee member (1991)  for the IEEE
International  Conference  on Distributed  Computing Systems, co-chair
(1991),  and program committee  member  (1989, 1990, and 1992) for the
IEEE International Computer  Software and Applications  Conference, as
program  committee    member   for Frontiers  of Massively    Parallel
Computation   (1992), as  guest editor   for  a special  issue  of the
Newsletter for the IEEE Technical Committee on Distributed Processing,
and  has been a  reviewer for  IEEE  and  ACM journals and conferences
since 1984.

 ....................................................................
				   
			     Tutorial T7

	 Common Mistakes and Games of Simulation and Analysis

	       Raj Jain, Digital Equipment Corporation
		       17th January, Full day

Structure and Background:  
------------------------

If you   need  to compare  two  or   more systems (products,  devices,
designs, or algorithms),  you  may benefit  from this  seminar  on the
common mistakes in simulation  and analysis.  You would also  learn to
protect yourself from the tricks (or games) played by some analysts to
show  the superiority of their   proposed system.  Based  on his  book
entitled  ``The Art  of    Computer  Systems    Performance  Analysis:
Techniques  for   Experimental  Design, Measurement,  Simulation,  and
Modeling,'' Raj  Jain discusses mistakes in  all aspects of simulation
and analysis including experimental design and result presentation.

Given   a  set of performance data    on two   or  more systems, it is
sometimes possible to manipulate the data,  analysis, metrics, or data
presentation such that either  system can  be  shown to outperform the
other.  A knowledge  of such games will help  you defend your products
when your competitors play such games.

While games are played by experienced analysts, beginning analysts may
make mistakes that may either lead to wrong conclusions or may fail to
make the desired  impact  on the  decision  makers or  the  customers.
Among the  common mistakes of analysis  are  too complex  analysis, no
experimental design,  no   goals  for  the  analysis,  and    improper
presentation of the results.

Mistakes  in simulation include  invalidated  models, improper detail,
bad random number generators, and careless selection  of  seeds.  Most
simulation projects get canceled  before  completion due to inadequate
time   and manpower    planning.  Experimental  design   and  analysis
techniques allow getting  the maximum information  from a given number
of simulation  runs.  Most simulation   analysts are  unaware  of such
techniques and,  therefore, waste time  running  too many simulations.
Also, they may come up with invalid conclusions  since the  effects of
randomness and    various  factors are   not  isolated.   Mistakes  in
presentation of   results   and  statistical analysis   will   also be
discussed.  Although most  of the examples used in  the talk are based
on evaluation   of   computer  systems.     The discussion  applies to
virtually any type of subsystem or product.

All attendees of this  seminar will receive  a copy of Raj Jain's  720
page book (a 52.95 value).  The book provides more extensive  lists of
mistakes and techniques discussed in the seminar.

Intended Audience: 
-----------------
Any one who does simulation,  analysis, or manages such projects  will
find this  seminar useful.   Mathematical sophistication is avoided in
the  seminar to keep  the discussion simple and cartoons  are  used to
make  it interesting.  Beginners  as well as experienced analysts will
have something new to learn.


About the Speaker: 
-----------------
Raj   Jain is   a Senior Consulting   Engineer   at  Digital Equipment
Corporation where  he has  been involved  in performance  modeling and
analysis of a  number of computer systems  and networks  including VAX
clusters, DECnet, Ethernet, and FDDI.  He  received a  Ph.  D.  degree
from  Harvard University in  1978 and has taught  graduate  courses in
performance analysis at Massachusetts  Institute of Technology.  He is
the Vice-Chair of ACM SIGCOMM  and serves  on the  editorial  advisory
boards of  Computer Communications (UK)  and the Journal of High Speed
Networks.  He received "The Best How-To Book - Systems" Computer Press
Award  for his  1991  book "The Art  of  Computer  Systems Performance
Analysis" published by Wiley, New York.

Dr.  Jain is known for introducing the packet  train model of computer
network  traffic along with  several congestion control and  avoidance
schemes.   He has authored  numerous papers on  networking performance
and is a popular  speaker.  He has  actively participated as a program
committee member, invited  speaker, or a tutorial speaker   at several
conferences  including    SIGCOMM, SIGMETRICS,     Data Communications
Symposium, InterOp,  ICCD, ICC, TENCON, LCN,  SCSC, ORSA, and ITC.  He
was a member of  a US Computer  Communications delegation to China  in
1987.

Dr.  Jain is a Senior Member of the IEEE, a member of ACM, Society for
Computer Simulation,  Operations Research Society of America, Authors'
Guild,  and a professional  member  of  National Writers Club.   He is
listed  in  Who's Who in  the  Computer Industry, 1989,  Who's  Who in
Writers, Editors, Poets 1991-92, and Contemporary Authors 1991.

 
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			     Tutorial T8

      G-networks -- New Models for Neural and  Queueing Networks

       Erol Gelenbe, Universit Ren Descartes (Paris V), France
			17th January, Full day

Structure and Background: 
------------------------
Recently we have developed a new  stochastic network model,  which was
initially motivated  by neural network  modeling.  Indeed, it  is well
known that signals in neural networks are formed by impulses or action
potentials, traveling much like customers in a queueing network.

We call this model a G-network because it  serves  as a unifying basis
for diverse areas  of  stochastic Modeling such as queueing  networks,
computer   network modeling,  computer system  performance  and neural
networks.

In   the  simplest version of  this   model, "negative" and "positive"
signals or customers circulate among  a finite set  of units, Modeling
inhibitory and excitatory signals  of a neural  network,  or "negative
and  positive customers"  of  a queueing  network.  Signals can arrive
either from other units or from the outside  world, are accumulated at
the input of each unit, and constitute its signal potential. The state
of each unit or neuron is its signal potential (which is equivalent to
the  queue length), while the  network state is   the vector of signal
potentials at each neuron.  If  its potential is  positive, a unit  or
neuron fires,  and sends  out signals to the other  neurons or  to the
outside world. As it does so its signal potential is depleted.

In the  Markovian  case, this  model   has product  form,  i.e.    the
steady-state probability distribution of  its potential vector  is the
product of the marginal probabilities of the potential at each neuron.
The signal or customer  flow equations  of the network, which describe
the rate at which positive or negative  signals arrive to each neuron,
are non-linear.

We will discuss in detail the relationship  between this model and the
usual  connectionist (formal)  model  of  neural  networks,   and then
present three major application areas :

    1. Combinatorial optimization of  NP-hard  problems, including a detailed
       presentation  of the graph  covering   problem, the graph partitioning
       problem and the traveling salesman problem. In each  case, we show how
       the G-network model can help obtain efficient approximate solutions.
    2. Learning : a backpropagation  learning algorithm for the
       recurrent (feedback) G-network will  be presented and applied to image
       recognition.
    3. Texture analysis and recognition. An efficient algorithm
       for black-and-white or color texture generation will be detailed.

Extensions of the G-network  model to  "batch removals",  to "multiple
signal classes", and to "networks with triggered customer motion" will
also be presented. This will  lead to  a discussion of G-networks as a
tool for computer network design and on-line optimization.

We will also examine the general  stability conditions which guarantee
that the network has a well-defined steady-state behavior.

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	       General Information about the tutorials

Any combination of  T1-T8 can be selected.   Please note the dates and
times.  Both half- and  full-day rates  are  available.  Student rates
are also available as follows:
  
 Full--day, Early Registration:   $185
 Full-day,  Late Registration:    $275
 Full-day,  Students:             $125
  
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For additional information contact:

Dr. Kallol Bagchi                        Dr. Patrick Dowd
Institute of Electronic Systems          Dept. of Electrical and
Aalborg University                         Computer Engineering
Fredrick Bajers Vej 7                    SUNY/Buffalo
DK 9220 Aalborg Ost, Denmark             Bell Hall
Phone: +45 98 15 42 11, Ext. 4901        Buffalo, NY 14260
Fax:   +45 98 15 67 40                   Phone: +(1) 716 645-2406
email: kkb@iesd.auc.dk                   Fax:   +(1) 716 645-3656
kkb@vaxa.aud.auc.dk                      email: dowd@eng.buffalo.edu

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