6th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2012)

 CALL FOR PAPERS
6th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2012)
Lyon, France
10-14 September 2012
http://saso2012.univ-lyon1.fr 

Important Dates
******************
Abstract submission: April 23rd, 2012
Full paper submission: April 30rd, 2012
Notification of acceptance : June 20th, 2012

Introduction
*************
The aim of the SASO conference series is to provide a forum for presenting the latest results about self-adaptive and self-organizing systems, networks and services. To this end, the meeting aims to attract participants with different backgrounds, to foster cross-pollination between research fields, to expose and discuss innovative theories, frameworks, methodologies, tools, and applications, and to identify new challenges. The complexity of current and emerging computing systems has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields (e.g., complex systems, control theory, artificial intelligence, sociology, biology, etc.) to find new ways of designing and managing networks, systems and services. In this endeavor, self-organization and self-adaptation have emerged as two promising interrelated facets of a paradigm shift.

Self-adaptive systems work in a top down manner. They evaluate their own global behavior and change it when the evaluation indicates that they are not accomplishing what they were intended to do, or when better function or performance is possible. A challenge is often to identify how to change specific behaviors to achieve the desired improvement. Self-organizing systems work bottom up. They are composed of a large number of components that interact locally according to typically simple rules. The global behavior of the system emerges from these local interactions. Here, a challenge is often to predict and control the resulting global behavior.

Topics of Interest
*******************
The SASO conference is interested in both theoretical and practical aspects of systems exhibiting self-* characteristics. A particular focus is the modeling of natural, man-made and social systems that exhibit self-adaptation and self-organization characteristics as well as the constructive use of the underlying basic principles in technical systems. The sixth edition of SASO particularly encourages submissions from the following, non-exclusive list of topic areas:

- Principles, Theory, Methods and Architectures for SASO Systems
- Robustness, Resilience and Fault-Tolerance in/with Self-* Systems
- Self-* Behavior in Communication Networks
- (Self-)Control, (Self-)Observation, (Self-)Monitoring of Engineered Systems
- Collective Phenomena in Social and Socio-Technical Systems
- Self-Organization and Self-Adaptation in Biological/Natural Systems
- Applications of Spatial and Physics-Inspired Self-Organization
- SASO Principles in Cyber-Security
- SASO Principles in Collective Robotic Systems
- SASO Principles in Cyber-Physical Systems
- Real-World Experience with Engineered Systems Exhibiting Self-* Properties

All contributions must present novel theoretical or experimental results, or practical approaches and experiences in building or deploying real-world systems and applications. Contributions that contrast "conventional" engineering principles with novel approaches making use of SASO principles are especially welcome.

Submissions Instructions
****************************
All submissions should be 10 pages and formatted according to the IEEE Computer Society Press proceedings style guide and submitted electronically in PDF format. Please register as authors and submit your papers using the SASO 2012 conference management system. The proceedings will be published by IEEE Computer Society Press, and made available as a part of the IEEE digital library. Note that a separate call for poster and demo submissions has also been issued.

Emerging Topic Papers
**************************
In addition to regular papers, SASO also encourages the submission of papers on emerging topics. These submissions should be clearly marked as such (indicating "Emerging Topic:" in the title) and should provide a well-rounded survey of novel questions, methods and abstractions that are relevant for the design of SASO systems along with a clear indication of the possible impact on the SASO community. In this category we particularly encourage submissions that present innovative applications of methodological frameworks being used in other fields of science that study SASO related phenomena, thus highlighting connections and potential for collaboration between different scientific communities.

Review Criteria
*****************
Papers should present novel ideas in the topic domains listed above, clearly motivated by problems from current practice or applied research. We expect claims of contribution to be clearly stated and substantiated by formal analysis, experimental evaluations or comparative studies. Appropriate references must be made to related work. Since SASO is a cross-disciplinary conference, a particular criterion that will be strictly enforced by the program committee is that all papers must be understandable by researchers that are not members of the particular, highly-specialize scientific community. Emphasis should rather be placed on cross-cutting aspects that are relevant to a wider audience of researchers and engineers dealing with SASO systems. Furthermore, submissions making use of principles inspired by phenomena occurring in fields like biology, physics, sociology, economics, etc. are required to provide references for all relevant work in the respective field. Papers demonstr
ating SASO principles in practical applications are expected to provide an indication of the real world relevance of the problem that is solved, including some form of evaluation of performance, usability, or superiority to alternative state-of-the-art approaches. If the application is still early work in progress, then the authors are expected to provide strong arguments as to why the proposed approach will work in the chosen domain.

The program committee strongly suggests to review the list of common reasons for SASO submissions being rejected, which is available online. Furthermore, a collection of interdisciplinary approaches to the study of SASO-related phenomena is provided. Prospective authors are invited to check whether their research question can be related to this rich body of work, thus benefiting from tools, methods and findings developed in various disciplines.

Technical Meeting Committee
********************************
General chairs
Salima Hassas, Universite Claude Bernard-Lyon 1, France
Paul Robertson, DOLL, USA

PC chairs
Anwitaman Datta (Distributed Systems), NTU, Singapore
Marie-Pierre Gleizes (Self-organization), Universite de Toulouse, France
Ingo Scholtes (Socio-tecnical Systems), ETH Zurich, Switzerland

Local chair
Gauthier Picard, Ecole Nationale Superieure des Mines de Saint-Etienne

Finance chair
Frederic Armetta, Universite Claude Bernard-Lyon 1, France

Poster chair
Stefan Dulman, Univ. Delft, Netherlands

Contest and Demos track Chairs
Olivier Simonin, LORIA, France 

Antonio Coronato, ICAR-CNR, Italy

Workshop chair
Jeremy Pitt, Imperial College London, UK

Tutorial chair
Giuseppe (Peppo) Valetto, Drexel University, USA

Publicity chair
Jose Luis Fernandez-Marquez, Univ. Geneva, Switzerland
Zhang Jie, Univ. Singapore, Singapore
Sam Malek, George Mason Univ., Fairfax, USA

Publication chair
Sven Brueckner, Jacobs Technology Inc., USA

Sponsor chair
Bob Laddaga, DOLL, USA

Web and Wiki chair
Haytham El Ghazel, Universite Claude Bernard-Lyon 1, France

Symposium on Self-* Systems – Biological Foundations and Technological Applications

Symposium on Self-* Systems – Biological Foundations and Technological Applications

part of EMCSR 2012, the 21st European Meeting on Cybernetics and Systems Research

April 10-13, 2012, Vienna, Austria

http://www.emcsr.net/

Call for papers

Part 1. Biologically and Socially Inspired Self-* Systems

Chairs: Vesna Sesum-Cavic, Institute of Computer Languages, Vienna University of Technology, Vienna, Austria, and Carlos Gershenson, Instituto de Investigaciones en Matemáticas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico

The increased complexity in today’s’ IT industry is one of the top problems and important obstacles. Self-organization appears as one promising way to cope with the increased complexity. Generally, self-* systems should posses as many self-* properties as possible (self-healing, self-tuning, self-learning,…) in order to achieve self-organization. Self-organization surrounds us. Many interesting self-mechanisms exist in our environment from which we can learn a lot. A careful observation of mechanisms in nature and society can discover some new tools that could beneficially be applied to different IT-problems. This conference track will focus on both biologically and socially based self-* systems. The papers could be theoretically based as well as with practical applications to important IT-problems.

Session 1: Biologically Inspired Self-* Systems (chair: V.C.)
Session 2: Socially Inspired Self-* Systems (chair: C.G.)

For further information contact vesna@complang.tuwien.ac.at and cgg@unam.mx.

Part 2. Self-Organizing Networked Systems

Chairs: Wilfried Elmenreich, Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt, Austria, and Carlos Gershenson, Instituto de Investigaciones en Matemáticas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico

Part 2 of this symposium will present and discuss current and novel approaches for applications of self-organizing systems.

A self-organizing system typically consists of many networked entities that organize themselves and cooperate through the exchange of information without the need of a centralized control instance but using a distributed approach. Information is exchanged locally among individual entities in the frame of the fulfillment of a certain global objective. Some simple and high-level rules in the individual entities lead to sophisticated functionality of the overall system. Many examples of successful distributed localized organization can be found in nature (e.g., ants, fireflies).

Self-organizing systems have various favorable properties:

• They typically adapt very easily to changes from inside and outside the system.
• Additional entities can be added and will be assimilated into the global system.
• Entities may be removed without too much affect on the global system, and other entities may take over crucial tasks of them.
• Furthermore, self-organizing systems scale very well and there is no bottleneck of a central authority.

Research into self-organizing networked systems not only has technical and user-oriented aims, it also enables a high degree of interdisciplinarity.

We encounter self-organizing systems on an almost daily basis in:

• the formations of swarms of fish and migratory birds
• the interplay of termites when they build their hills
• the activity of body cells during the healing of wounds.

In many areas of nature, single individuals or organisms work together without central coordination, but in perfect harmony. Large areas of the economy have already been functioning for many years according to this paradigm.

It is the aim of this symposium to create a forum for exchanging ideas, discuss solutions and share experiences among researchers and developers of self-organizing systems applications.
For further information contact wilfried.elmenreich@uni-klu.ac.at and cgg@unam.mx.

Confirmed keynote speakers include Edgar Morin, Péter Csermely, and Péter Érdi.

Submission details:

For submission and conference details, please visit http://www.emcsr.net/?page_id=55

Important Dates:

Submission deadline: January 14, 2012 extended to January 20, 2012
Notifications:             January 27, 2012
Schedule published:   February 7, 2012
Conference:                April10-13, 2012

Evolution as a tool for understanding and designing collaborative systems

Saudações de São Paulo (Greetings from Sao Paulo)!

I was invited to the IFIP Working Conference on Virtual Enterprises (PRO-VE 2011) to give the keynote talk on evolution as a tool for understanding and designing collaborative systems.

Here is a short summary of the talk:

Research on collaboration addresses the common tension between

• what is good for the individual actor in the short run, and
  
• what is good for the group in the long run
  

This research is based on game theory and, therefore, employs such models as the Prisoner’s Dilemma or public goods games as the basis for analysis. Using game theory, you can approach the question What is the most rational strategy? for a given model. However, in real systems often converge towards equilibria with behavior different from the calculated rational one. In order to explain these results, evolutionary approaches are a useful tool. To solve the contradiction, it is necessary to realize that typically interaction properties have not been designed by a central ruler but evolved over time. However, finding the appropriate interaction rules that induce a particular overall behavior is difficult due to the unpredictable or counterintuitive nature of such emergent and complex systems. Therefore, we propose evolutionary models to examine and extrapolate the effect and development of particular collaboration rules. An example of such an approach is our work on evolving cooperative behavior with neural controllers. Evolution, in this context, does not replace the work of analyzing complex social systems, but complements existing techniques of simulation, modeling, and game theory in order to lead for a new understanding of interrelations in collaborative systems. If you want to learn more, quickly come to the conference in Sao Paulo and/or check the slides below :-)

Evolution as a Tool for Understanding and Designing Collaborative Systems

Complexity on the workbench

Today’s technical systems contain more and more components which are typically networked and interacting with each other. So, these systems become very complex, which makes it difficult to engineer and maintain the system using traditional, hierarchical approaches.
Looking into complex systems in nature, we see that they are controlled by distributed self-organizing mechanisms that are simple, scalable, robust, and adaptive. However, putting a self-organizing approach into technical systems is not straightforward, because such complex systems are typically hard to predict. A particular change in an interaction mechanism might even have counter-intuitive effects.
In nature, the driving mechanism behind building self-organizing behavior is evolution - why not use the very same method in form of an evolutionary algorithm?
However, there is a need to integrate different tools and models like neural networks, mutation and recombination, and problem-specific simulations. With our tool FREVO we provide a unifying framework to reduce this problem to basically three components: a problem representation, an agent representation and an evolutionary algorithm.
FREVO has been used to solve quite different problems and is available as open source to everyone. It is a very flexible framework open to new components and simulations, thus, we are looking forward to see you testing your ideas with it :-)



This talk was given by István Fehérvári at FET 2011 in the science café. This work was supported in part by the Lakeside Labs project MESON (Modeling and Engineering of Self-Organizing Networks) and the Lakeside Labs GmbH.

Replacing the Java random generator

The Java random number generator was implemented in 1995. It is based on a linear congruential generator. Since then, there was considerable advancement in algorithms for pseudo random number generators. For most applications, Java's random number generator might be sufficient, but at a closer look, the algorithm has a fairly short period of 2⁴⁸ and fails some tests on the randomness of its output.
The good news is that there is an algorithm which is faster and better: the Xorshift algorithm. Xorshift is using a few (in the following implementation exactly three) of shift and exclusive-or operations. The best way to integrate it to Java is to make a subclass of java.util.random and to overwrite the seed variable and the next() method:

import java.util.Random;

/**
 * A subclass of java.util.random that implements the 
 * Xorshift random number generator
 */

public class XSRandom extends Random {
 private long seed;

 public XSRandom(long seed) {
  this.seed = seed;
 }

 protected int next(int nbits) {
  long x = seed;
  x ^= (x << 21);
  x ^= (x >>> 35);
  x ^= (x << 4);
  seed = x;
  x &= ((1L << nbits) - 1);
  return (int) x;
 }
}

Since all methods of the Random generator (nextBoolean(), nextInt(), nextLong(), nextFloat(), nextDouble()), nextBytes(), nextGaussian()) depend on the next() method, this efficiently changes all number generation to the Xorshift algorithm.
A complete Java class including also a clone() method can be downloaded here (Code is under LGPL Version 3). Note that this implementation, unlike the java.util.Random is not exactly thread-safe - concurrent threads might access and change the seed variable inconsistently. However, note that concurrent access on the same random object would anyway end up in a nondeterminstic sequence of numbers for each thread.
This implementation is about 30% faster than the generator from java.util.random. It's output passes the Dieharder test suite with no fail and only two announced weaknesses. To use the class in legacy code, you may also instantiate an XSRandom object and assign it to a java.util.Random variable:

  java.util.Random rand = new XSRandom();

All method calls to rand are then using the newer, faster implementation.

Pseudo random number generators

In the previous post, we learned that a complex system can exhibit deterministic, but unpredictable behavior. Actually, this aspect has a strong application in computers that has been around for a long time: random number generators.
Many computer programs, like for example games or Monte Carlo simulation require a random function. Typically, a computer operating on defined binary states that only change at particular clock instants has no true randomness - its operations are deterministic. So, typically pseudo random number generators are used to generate number sequences similar to random ones.
In 1946, John von Neumann suggested such an algorithm named the middle-square method. Von Neumann's algorithm is simple: take a number as the "seed", square it, remove the middle digits of the resulting number (when viewed in decimal format) and take the remaining digits as your "random number". This number becomes also the new seed. The algorithm is fast, but statistic tests on the generated random numbers show that the distribution of numbers significantly differs from true random numbers. Worse, once the seed becomes zero, the algorithm gets stuck.
A few decades after Neumann, the programming language C came with a library function for generating random numbers using the functions rand (to pull a number) and srand (to set the seed). For generating one number, the algorithm linearly combines several terms based on constants derived from the seed. This means a higher implementation effort, but the test passes most statistical tests on randomness, such as for example are given ba the Dieharder random test suite.
The famous home computer Commodore 64 had a different algorithm for random number generation, which was simpler: The last seed is multiplied with a big number, a small number is added and the resulting number (given in a floating point format) is mixed up by switching bytes. Although the C64 algorithm was considered practically useful in a publication from Bowman, the Diehard test quickly shows a lot of deficiencies of the algorithm.
In 1997, Makoto Matsumoto and Takuji Nishimura came up with a very good algorithm, the Mersenne Twister. While this algorithm passes most of the Dieharder tests, the implementation has also grown in its complexity. For example, a common implementation of the algorithm requires an array of 624 numbers.

So - do you think now there is a tradeoff between quality of randomness and computation effort? Wrong!

The late (and great) George Marsaglia showed us a simple way to generate high quality numbers: the Xorshift algorithm.

public long randomLong() {
  x ^= (x << 21);
  x ^= (x >>> 35);
  x ^= (x << 4);
  return x;
}

The computational effort of the algorithm is obviously very low. If you chose "good" values for the three magic numbers (e.g., 21, 35, 4 as in the above example) Xorshift passes the Diehard tests. As it is the case in many complex systems, there is a simple way to control the systems behavior as intended, but is very difficult to find that simple way. In the case of pseudo random numbers it took 60 years from John von Neumann's middle-square to George Marsaglia's Xorshift.

• J. von Neumann. "Various techniques used in connection with random digits", in A.S. Householder, G.E. Forsythe, and H.H. Germond, eds., Monte Carlo Method, National Bureau of Standards Applied Mathematics Series, 12:36-38, reprinted 1951.
  
• M. Matsumoto and T. Nishimura. "Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator". ACM Transactions on Modeling and Computer Simulation 8(1):3–30, 1998
  
• R. Bowman. Evaluating Pseudo-Random Number Generators. Comput. &amb; Graphics, Vol. 19, No. 2, pp. 315-324, 1995.
  
• G. Marsaglia. "Xorshift RNGs". Journal of Statistical Software Vol. 8 No. 14, 2003.
  

Langton's Ant - from simple rules to complex behavior

The following system is created using a very simple set of rules:

1. Let's assume an infinite 2D grid world. Each grid cell can be either black or white.
   
2. For simplicity, all grid cells are white at the beginning.
   
3. There is an ant, which can move up to four directions (N,E,S,W).
   
4. Whenever the ant enters a white field, it toggles the grid color and performs a clockwise (right) turn.
   
5. Whenever the ant enters a black field, it toggles the grid color and performs a counter-clockwise (left) turn.
   

You got the rules? Let's play! Get yourself some squared paper, a pencil and a rubber and start drawing. Or, if you are lazy, just use the Java applet below. Unfortunately I failed in simulating the infinite grid, so the simulation reverses whenever the ant leaves the system boundary, but this does not make a difference for the first 10000 iterations. You may use the mousewheel to scroll and modify the simulation speed in order to investigate what the ant is up to.


Despite the simple rules, the ant is following an interesting and unexpected path. This relates to what Carlos Gershenson has pointed out in his blog: a deterministic system does not necessarily mean that it is predictable. A system is deterministic if no randomness is involved in the development of future states of the system. Thus, given a particular starting condition, the system will always develop its future states in the same way. The real physical world, considering the randomness in quantum effects, is not deterministic. But the simulated ant in our example is. On the other hand, prediction means the ability to make a forecast of a system's state (either qualitatively or quantitatively). These two terms represent different concepts and should not be mixed up.

In our deterministic ant system, it is not possible to predict the behavior by looking at the rules unless you have tried out the simulation beforehand. Although we have just one agent, we face a complex system, since the agent is interacting with every single cell it arrives. And each move changes the cell and orientation, thus creates new information. The interesting and unexpected thing is what the ant is doing after roughly 10200 steps. Until then the movement is chaotic. But subsequently, the ant produces a "highway" by walking away to the south-east in repeated sequences of 104 steps. From that point on, the future system states can be predicted for all future states.
If you liked this, check out the work of Propp et al. on Langton's ants with multiple grid colors. They present a three-color model where it is not known if the ant will ever enter a stage producing a predictable highway. So far on deterministic models - in your face, predictability!

• Langton's ant@Wikipedia
  
• Gale, D.; J. Propp, S. Sutherland, S.Troubetzkoy, Further Travels with My Ant. Mathematical Entertainments column, Mathematical Intelligencer 17: 48–56, 1995.
  
• Carlos Gershenson, Determinism != Predictability, Complexes Blog, September, 2010