CasGroup - User contributions [en]

Distributed GCD Algorithm

2010-11-24T08:28:12Z

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The '''distributed GCD algorithm''' belongs to a special class of
[[Distributed Algorithm|distributed algorithms]] for mathematical operations.
At each node in the distributed system, one or more values are stored, the
nodes can communicate with each other, and are allowed to perform certain
mathematical operations. The distributed algorithm now has to
determine the exact sequence of operations that each node
has to follow in order to achieve a global goal (for instance
find the max, min, gcd or average value for all nodes).

The distributed GCD algorithm is a very simple [[Distributed_Algorithm|distributed algorithm]]
to determine the Greatest Common Divisor (GCD) of n numbers.
Because it is so simple and elegant, it is often used as an
introductory example for [[Distributed_Algorithm|distributed algorithms]], for
explanatory purposes and to illustrate the workings of
[[Distributed_Algorithm|distributed algorithms]] in general
(for instance the problem of termination detection).
The algorithm for a ring topology with as many nodes
as numbers works as follows: each process begins
with a number z, and the algorithm is
finished if all nodes have the same number.

On receiving a message from a neighbor:

Receive (int e)
{
if (e < z)
{
z = ((z-1)%e)+1);
Send()
}
}

Sending messages:

Send()
{
SendMsg (z, left Neighbor)
SendMsg (z, right Neighbor)
}

[[Category:Distributed Algorithms]]

Web 2.0

2010-11-24T08:27:45Z

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Macrolevel

2010-11-24T08:27:38Z

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Failover

2010-11-24T08:27:33Z

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Curiosity

2010-11-24T08:27:29Z

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Lever Point

2010-11-24T08:27:19Z

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Self-Cleaning

2010-11-24T07:51:59Z

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Availability

2010-11-24T07:51:47Z

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Adaptive System

2010-11-24T07:51:46Z

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Self-Regeneration

2010-11-24T07:51:42Z

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El Farol Bar Model

2010-11-24T06:33:24Z

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Race Condition

2010-11-24T05:40:19Z

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A '''race condition''' can be defined as an anomalous behavior
due to unexpected unexpected ordering of events and critical
dependence on the relative timing of events. It characterizes an
undesirable situation in a concurrent or distributed system where
the cause for the output of a process depends on the arbitrary
timing of other events. There are many participants (messages,
threads, etc.) which have different speed, and each of
the participants can win. Only the winner determines the resulting
behavior.

In other words, the output of the process is unexpectedly and dependents
critically on a sequence or timing of other events. For instance, a computation
in a [[Distributed System|distributed system]] may depend on the
arrival order of two different contradictory messages, the first says
"maintain object", whereas the second says "release object", and the message
which arrives first - the winner of the race - determines the result.
A race condition can also occur when multiple processes access and manipulate
the same data concurrently, and the outcome of the execution depends on the
particular order in which the access takes place.

Race conditions are not only undesirable because the program doesn't
work as it's supposed to do. In debugging they are also very undesirable,
because you have to "run the race" many times before you can reproduce a
situation which a certain fault, error or failure. The error may also
vanish if you slow the processes down by debugging the computation
step-by-step.

Genetic Algorithm

2010-11-24T02:54:24Z

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Self-Design

2010-11-24T02:54:06Z

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ESOA

2010-11-24T02:53:46Z

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'''Engineering Self-Organizing Applications''' (ESOA) is the attempt
to apply the concept of [[Self-Organization|self-organization]] to engineering,
esp. software engineering. Wether this is possible or not is still controversial.
The goal (or hope) is to achieve complex collective tasks with relatively
simple individual elements, units and behaviors. The name ESOA is sometimes
also used as an abbreviation for "Enterprise [[Service-Oriented_Architecture|SOA]]",
i.e. to describe a "Service-Oriented Architecture for Enterprises".

== A Name for a Problem ==

EOSA is like [[AOSE]] more a name for a problem than its solution.
It is closely related to the problem of "[[Engineering_Emergence|engineering emergence]]".
Engineering is at odds with [[Emergence|emergence]], a phenomenon which
usually accompanies self-organization, and planned organization
as it is found in engineering contradicts more or less the concept
of self-organization. It is obviously difficult to organize a system
which is able to organize itself.

== Demands ==

Why do we need [[Self-Organization|self-organization]] at all?
A demand for self-organization exists for instance in the
following systems:

*highly distributed systems without central control
*open systems embedded in fast changing dynamic environments
*pervasive or [[Ubiquitous_Computing|ubiquitous computing]] where computers become invisible

In short, self-organization is needed if the system is (or becomes)
very large, very distributed or very complex. Here is already
the first problem: such systems are not easy to create.

== Problems ==

Many systems in nature demonstrate that self-organization is
indeed possible. These natural system are of course quite
different from traditional software applications, they
are more like [[Agent|agent]]-based systems and
[[Multi-Agent System|multi-agent systems]] (MASs). There is also no clear
or precise definition of [[Self-Organization|self-organization]]
which makes an easy application to any domain difficult.
Another problem with [[Organic Computing|biologically inspired systems]]
is that there number is limited. There is certainly not a
countless number of techniques used in natural self-organizing
systems. A concrete application often boils down to the use
of pheromones, [[Stigmergy|stigmergy]] and [[Ant-Based_Systems|ant-based systems]].

The core problem of ESOA is to create a system with a certain
macroscopic global behavior by specifying only local
behavior and microscopic interactions.
It is the old contradiction or antagonism between
forced engineering and spontaneous [[Emergence|emergence]],
imposed purpose and independent autonomy,
planned organization and self-organization.
All these items pairs are more or less contrary to each
other.

In traditional engineering, the structure, function and
organization of the systems is carefully planned and
impressed from the outside on the system. If a situation occurs
which has not been anticipated or tested before, the system usually
crashes. The system is predictable, reliable and comprehensible,
but it is not always robust, adaptive and scalable.

In ESOA and [[Multi-Agent System|MASs]], the systems are partly able to
organize themselves, and the tolerance for unpredictable properties and
situations is a bit higher. If a situation occurs which has not
been anticipated before the system is expected to handle it.
A self-organizing system is robust, adaptive and scalable,
but usually less predictable, reliable and comprehensible.

== A possible solution ==

A possible solution to the fundamental ESOA problem is
the application of the scientific method by the engineer,
the step-by-step investigation of hypotheses with experiments
and simulations. The scientific method is an iterative process
that is the basis for any scientific inquiry, and it can also
be used to examine artificial systems and simulated worlds
(for instance synthetic societies of multi-agent systems).
The scientific method follows a series of steps: (1) identify a problem
you would like to solve, (2) formulate a hypothesis, (3) test the
hypothesis, (4) collect and analyze the data, (5) make conclusions
and restart with (1) (see also the paper preprint
[http://arxiv.org/abs/nlin.AO/0601002 On Engineering and Emergence]).

== References ==

ESOA 2003 workshop (from the AAMAS 2003 conference):
* Giovanna Di Marzo Serugendo et al., ''Engineering Self-Organizing Systems'', Springer LNAI 2977, 2004

ESOA 2004 workshop (from the AAMAS 2004 conference):
* Sven A. Brueckner et al., ''Engineering Self-Organizing Systems'', Springer LNAI 3464, 2005

Category:Applied Principles

2010-11-24T02:53:43Z

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This category contains the [[Applied_System_Theory|applied principles]] of systems and system theory.

Microlevel

2010-11-24T02:53:38Z

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In a system which can be observed and described at many different scales and resolutions, a '''microlevel''' is a microscopic level of organization with a high resolution observable by the help of tools, for example microscopes. When comparing two [[Level_of_Organization|levels of organization]] it is often convenient to refer to the lower (or higher resolution) level as the microlevel and the higher (or lower resolution) level as the [[Macrolevel|macrolevel]]. This is for example the case in the [[MML|micro-macro link (MML)]] problem.

[[Category:Organization]]

Heart Beat Algorithm

2010-11-24T02:53:36Z

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Edge Computing

2010-11-24T02:53:18Z

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Category:Basic Principles

2010-11-24T02:53:17Z

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This category contains the [[Basic_System_Theory|basic principles]] of systems and system theory.

Self-Optimization

2010-11-24T02:52:18Z

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Category:X-Computing Techniques

2010-11-24T02:52:17Z

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Most of the x-computing techniques are unconventional initiatives or visions to build new forms of computer systems, for
example fault-tolerant, flexible, robust and scalable computer systems.
Ubiquitous Computing and Autonomic Computing are still largely visions.

Units of selection

2010-11-24T02:52:10Z

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'''Units of selection''' can be any replicator in an [[Evolutionary_System|evolutionary system]]: genes, memes or other replicators which carry information. The units of selection persist across generations. They are often characterized by different scopes and levels, see [[Kin Selection]], [[Group Selection]] and [[Multilevel Selection]], if these levels are influenced by replicators. [[Natural_Selection|Natural selection]] acts on the genotype and selects always something which is replicated. This replicator can be a gene or meme.

Flocking

2010-11-24T02:51:56Z

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Boids Model

2010-11-24T02:51:55Z

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Engineering Emergence

2010-11-24T02:51:14Z

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Autonomy

2010-11-24T02:51:02Z

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Life

2010-11-24T02:50:47Z

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Shadow Emergence

2010-11-24T02:50:41Z

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There is perhaps another form of [[Emergence|emergence]] which doesn't
fit into the traditional categories of weak and strong
emergence: "imaginary" or "shadow" emergence. It is
comparable to strong [[Emergence|emergence]], and describes the
appearance of a new completely new [[System|system]]. Yet only
the image or shadow of the system appears, an image
without source or shadow without object. Shadow emergence
describes the appearance of a new system embedded in an
old one, which is so strongly connected to the old system
that it can not be isolated from it. It could also be
named `false'-, `pseudo'- or `quasi'-emergence. The mind,
[[Self-Consciousness|self-consciousness]] and the [[Self|"self"]]
belong probably into this category.

How is it possible that a mental substance arise out of
physical substance, how can neurons and interactions
between neurons give rise to something completely
different which "hovers" over the brain? This question
has kept philosophers busy since the middle ages. The
answer is: it is not possible in the way our brains
are designed, and there is no such thing as a mental
substance. There is only a shadow, something which
we can describe with a kind of projection. Mental
substances such as ghosts and spirits are projections
from one world to another, imaginary items which exist
only in our imagination. The "self" is a projection,
an illusion, a belief, an imaginary concept.

We see the universe and ourselves through our own lens,
and our brains learn from the beginning to treat
everything we encounter as a person or human, including
ourselves. Yet, as Steven Pinker has noticed, "the
intuitive feeling that there's an executive `I' that
sits in a control room of our brain, scanning the screens
of the senses and pushing the buttons of the muscles,
is an illusion." Although we have the feeling of
peering out at the world from the control room behind
our eyes, the self in the control room is only an
illusion, a projection from the physical to the mental
realm. 100 billion jabbering and flickering neurons
create for each of us the illusion that one exist and
is here. The imaginary idea of a "mental" self comes
as close as possible to a real entity, without being
real. It appears to be real and independent from the
system where it emerged, but it is neither real nor
independent from the system.

There is of course something which is responsible for
our actions and our behavior. It is the whole brain
with its myriads of neurons, synapses and connections.
Steven Pinker puts it like this: "consciousness turns
out to consist of a maelstrom of events distributed
across the brain. These events compete for attention,
and as one process outshouts the others, the brain
rationalizes the outcome after the fact and concocts
the impression that a single self was in charge all
along." If we could seperate the hardware form the
software in the brain (the many Exabytes of code and
data which specify how many neurons exist and how they
are connected), then one could argue that the software
as a whole is the mind or the self - the whole software
system including the maelstrom of events and the whirl
of information produced by it - however distributed,
dispersed, and diffuse it may be. Unfortunately, both
are intrinsically and inseparably tied to each together,
and there is no distinction between hardware and software
in the brain. If we compare the situation to hardware
and software, the brain is like a giant system where the
hardware has evolved to emulate a whole new computer system,
including hardware and software. The system recognizes
itself and feels that it is more than just hardware.
Yet the software cannot be isolated or transfered to
another computer because it is hard-coded and hard-wired
into the system, it is a fixed part of the hardware.

Shadow emergence is strong emergence without an explicit
[[Code|code]] or compiler, similar to hard-coded software. It
is like a image, shadow or statue of a living thing,
although the living thing itself is not there (and has
never existed). It is the imitation or anticipation
of strong emergence where one special instance of
the new system is mirrored in the structure of the
old system. A new system which is embedded and
hard-coded in an old one, without specifying the
code or compiler.

In Ray Bradbury's words we are "too soon from the cave,
too far from the stars". Just as we cannot leave the
earth to explore interplanetary space (contrary to
machines and robots), we cannot leave our body, although
we feel we are more than just a piece of meat. All we
can do is making small hops in the near atmosphere,
and small hops into virtual worlds as they could be.
Although we would like to leave and surpass our body,
we cannot. Although we don't exist, we feel we do.
This tragic illusion would only be true if there were a
distinction between hardware and software in the brain.
Our selfs disappear with their bodies, and when the
brain disintegrates, the mind vanishes, too. In the
words of Steven Pinker, "when the physiological activity
of the brain ceases, as far as anyone can tell the
person's consciousness goes out of existence [...]
Every moment of consciousness is a precious and
fragile gift."

(the quotes from Steven Pinker are from his article
[http://www.time.com/time/magazine/article/0,9171,1580394,00.html The mystery of consciousness], Time Feb. 12 (2007))

--[[User:Jfromm|Jfromm]] 20:47, 23 July 2008 (GMT)

== Links ==

* Blog entry for [http://blog.cas-group.net/2009/06/the-ghost-in-the-machine/ The Ghost in the Machine]

[[Category:Consciousness]]

Category:Evolutionary Principles

2010-11-24T02:50:31Z

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This category contains some basic principles of evolutionary systems

Mutual Exclusion Algorithm

2010-11-24T02:49:37Z

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Reliability

2010-11-24T02:49:06Z

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Rejuvenation and Cancer

2010-11-24T02:48:58Z

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== Rejuvenation and Cancer ==

It is obvious that any mechanism which has the power to rejuvenate or regenerate
a system has also the power to destroy it: if the regeneration process goes
wrong, and something different is generated instead of the original system,
then the consequences are very severe.

Cancer itself is like uncontrolled "rejuvenation" in an organism: "rejuvenation" went awry.
Like stem cells or embryonic cells, cancer cells do not age and can proliferate
without limits. Cancer cells and small tumors influence their environment to increase
local blood supply and they suppress the maternal immune system, just like embryos.
Cancer cells act like embryonic cells, who proliferate vigorously, are
capable of extensive migration, and secrete factors that increase the local supply of blood.
In general one can find many similarities, tumor cells may grow and spread by exploiting
the genetic programs normally active only during embryonic and fetal development
[http://www.scientificamerican.com/article.cfm?id=cancer-clues-from-embryos]:

* Tumor originating cells ([[Cancer_Stem_Cell|cancer stem cells]]) are like stem cells
* Tumors themselves are like embryos
* Tumor development resembles embryonic and fetal development

If there is indeed a close link between rejuvenation and cancer,
then the consequences would be severe: suppressing tumor promoting
proteins would prevent them from doing needed repair work, i.e.
suppressing cancer may increase aging processes, while rejuvenating
drugs which replace and repair damaged or senescent tissues would
increase the risk of cancer.

== Self-renewal and Stem Cells ==

Self-renewal is the key property of stem cells. They are fundamental for organ
development and tissue repair. Although they belong to the
most beneficial cells and enable rejuvenation and regeneration of the body,
they also seem to be at the heart of every tumor, as new research about
[[Cancer_Stem_Cell|cancer stem cells]] has found out. Cancer cells and stem
cells are disturbingly similar [http://www.nature.com/nature/journal/v460/n7259/full/4601085a.html].
Is it possible that the same cells which are responsible for maintaining a tissue or
an organ are also responsible for destroying it through the worst form of disease?

A link between self-renewal, [[Self-Rejuvenation|self-rejuvenation]] and self-reproduction would
perhaps explain why cancer is most common in the organs responsible for
self-reproduction: breast cancer or endometrial cancer in women, and prostate
cancer in men. It would also explain why “many of the body’s tissues that
are most prone to cancer, like the blood, skin and lining of the gut”
are self-regenerating tissues. These self-regenerating tissues are composed of
short-lived cells, and contain a self-renewing population of stem cells that
maintain them.

Cancer is a very complex disease. It can take years and decades until
enough mutations and damages are accumulated to enable a tumor. One
problem is that every tumor looks different.
Future research will hopefully shed more light on this complicated topic.
Recent research has found a close relationship between stem cells and tumor
generating cancer cells. Tumors seem to be the price for high
age. The reason is not only accumulated damage in form of progressive
deterioration, gradual abraison and inevitable wear and tear damages. Old
age is only possible through rejuvenation and repair. It looks like stem
cells which are responsible for renewal, rejuvenation and regeneration have
not only the ability to regenerate a system, but also the possibility to destroy
a system.

The more specialized an organism or a cell is, the more it seems to loose
its ability to replicate and regenerate itself. Stem cells are primal undifferentiated
cells that retain the ability to produce an identical copy of themselves,
whereas ordinary cells, specialized for a certain purpose, loose the ability to
produce an identical copy of themselves. Intermediary cells are restricted by
an internal counting mechanism to a finite number of cell divisions, which
declines with increasing differentiation.

Has this something to do with the unique history of each organism or
cell (are old, highly differentiated and specialized cells usually polluted or
used up) ? Or is this hierarchy useful for avoiding chaos and confusing which
could arise if every cell would be allowed to replicate itself?
If no cells would be able to copy and replicate itself, then the body would
not be able to rejuvenate and repair itself in order to replace damaged or old
cells. If all cells would be able to do this, this would result in ”chaos” and
every cell would be a potential cancer cell. It looks like Nature has invented
a compromise in form of stem cells.
Stem cells are a blessing, because they can replace damaged or old cells,
but mutated or damaged stem cells are a curse. Stem cells have like tumor
generating cancer cells the same potential to proliferate, they have an
unlimited life span and the ability to generate a diverse range of other cell
types.

New research gives strong evidence that cancer is the price for an
evolvable system (subject to mutation) with the ability of self-replication,
[[Self-Rejuvenation|self-rejuvenation]], [[Self-Regeneration|self-regeneration]]
and self-repair. It seems to be the price for the self-* properties
which enable our existence and keep us alive. Perhaps this helps to explain
why it is so difficult to find a cure for it. Recently, researchers have suggest
again new strategies to fight cancer, for instance viruses (modified common
cold viruses), chillies (spicy food) or sugars (a combination of sugar and
short-chain fatty acid). So far, there is no miracle cure for cancer. And
probably none of these new approaches will be a miracle cure, either. But
hopefully one approach in the future - perhaps due to the new insight that
the disease is related to self-rejuvenation - will lead to something useful in
the ongoing battle against this terrible disease.

== Cancer in CAS ==

This rises the urgent question if artificial systems can develop some form
of cancer, too, if we try to build systems with these very desirable [[Self-Star_Properties|self-* properties]].
Is there a form of cancer in complex adaptive systems in general, not only
in biological organisms, but also in ecological, economical, social or
political systems ? This would perhaps be an element arising from the need
for self-rejuvenation which proliferates, spreads and replicates itself until it
has destroyed the whole system. Joseph Schumpeter introduced a similar idea of
[[Creative Destruction|creative destruction]] in the context of capitalism. [[Creative Destruction|Creative destruction]]
means one thing is replaced by another from within.

* Ecologies systems allow the self-rejuvenation by “granting the right” to create new species and to explore new ways to survive. In an ecological system, this freedom can lead to mass extinctions or epidemics caused by parasites or viruses with the capacity to damage or destroy the whole ecological system.

* Economies allow the self-rejuvenation by granting the right to found new companies and to explore new market forms. In an economical system, this freedom can lead to market monopolies, which destroy the market system. Or it can lead to disruptive innovations which destroy a large number of existing companies by [[Creative Destruction|creative destruction]]

* Democracies allow the self-rejuvenation by granting the rights to found new parties, to assemble, to demonstrate, and to educate. In a political system, this can lead to terrorism or an ideology like fascism. In a religious system, it can lead to schisms and sects.

* Science allows the self-rejuvenation by “granting the right” to create new theories and to explore new ways to explain nature. This freedom can lead to subsystems with the capacity to threaten the whole scientific system, for example in form of a paradigm shift.

In a 1995 essay "Eternal Fascism", Umberto Eco attempts to
list general properties of fascist ideology. One could add
"lack of differentiation", "uncontrolled growth" beyond
the normal limits, invasion of adjacent systems, and
generally "malignant, aggressive and egoistic behavior"
to the list - the hallmarks of cancer.
Fascism is a bit like cancer for politics. The political
system allows the self-rejuvenation by granting the rights
to demonstrate and to found new parties. This can lead
to an ideology like fascism, which spreads and growths
like cancer, and eventually destroys the old system
completely. Cancer is the result of a self-renewal or
self-rejuvenation which has gone wrong. It is a bit like
the evolution of an ancient or alien life form inside of the
own system which is normally prohibited, and cancer
development is a remarkably similar to the process
of embryonic development and evolution itself.

In general, if a [[Complex_Adaptive_System|complex adaptive system]] has a built-in self-rejuvenation ability, and it grants it’s agents the rights to create and to found new elements, these elements can severely damage or threaten the function of the whole system. Any system which is able to remould and reinvent itself can replace itself by something else.

[[Category:Cancer]]

Deadlock Detection

2010-11-24T02:48:54Z

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Distributed '''deadlock detection''' algorithms realize deadlock detection in a [[Distributed System|distributed system]].
It is difficult to detect deadlocks in distributed systems, because in general no node or process has complete information
about every process. A large variety of algorithms exist, for instance the ones proposed by

* Chandy-Misra-Haas (1983)
* Bracha-Toueg (1984)

[[Category:Distributed Algorithms]]

Echo Algorithm

2010-11-24T02:48:38Z

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Category:Distributed Algorithms

2010-11-24T02:48:25Z

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This page points to resources concerning Distributed Algorithms.

Self-Regulation

2010-11-24T02:48:11Z

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See the [[Invisible Hand]]

[[Category:Self-Star Properties]]

Ashby Theorems

2010-11-24T02:48:10Z

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Phase Algorithm

2010-11-24T02:48:07Z

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Category:Self-Star Properties

2010-11-24T02:47:58Z

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A list of common Self-* Properties

Exploitation and Exploration

2010-11-24T02:47:57Z

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System

2010-11-24T02:47:54Z

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Self-Repairing

2010-11-24T02:47:52Z

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Recovery-Oriented Computing

2010-11-24T02:47:50Z

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'''Recovery Oriented Computing''' (ROC) was started as a joint Berkeley/Stanford project in order to investigate novel techniques for building highly-dependable Internet services [http://roc.cs.berkeley.edu/]. Contrary to traditional fault-tolerance approaches in distributed systems, ROC doest not assume that total avoidance and prediction of failures is possible. The philosophy of ROC is to accept the fact that failures will happen, but to be well prepared to recover quickly. It emphasizes fast recovery from failures rather than complete failure-avoidance.

In large distributed software systems, failures are inevitable, and the exact cause of the fault or error is not always necessary for many recovery techniques. A simple method to recover quickly is to restart the corresponding components of the system: the server, the service or single EJBs. The goal of ROC is to create [[Dependable System|dependable systems]] and to reach higher levels of [[Fault Tolerance|fault tolerance]] and [[Scalability|scalability]]. The two principles of ROC are therefore

* Microreboot - selective restart of small system parts
* System-Level Undo - reconfiguration by rollback recovery

[[Category:x-Computing_Techniques]]

Evolutionary System

2010-11-24T02:47:26Z

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Self-Healing

2010-11-24T02:47:03Z

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Self-Healing is a [[Self-Star_Properties|Self-* Property]], see also [[Self-Repairing]]. It is part of the [[Autonomic_Computing|autonomic computing vision]] from IBM and means automatic discovery, and correction of faults.

[[Category:Self-Star Properties]]

Epidemic Computing

2010-11-24T02:46:56Z

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An epidemic or infectious disease like influenza, cholera or yellow fever is of course a bad thing:
"an epidemic is generally a widespread disease that affects many individuals in a population" [http://en.wikipedia.org/wiki/Epidemics]. For the construction of distributed systems it is
an interesting phenomenon. "Epidemic techniques" can be used in building reliable and scalable
distributed systems: epidemic algorithms can be used for

# replicated database maintenance
# replacing and refreshing of components in a large [[Distributed System|distributed system]],
# disseminating information in large scale and dynamic systems
# failure detection

The idea behind epidemic computing is to reach the same amount of scalability and robustness
as in natural epidemics, but to use it for a good purpose.
Biological epidemics scale really well and are very robust: once
a few subjects are infected and a certain threshold is reached, it is almost impossible to
stop the spread, even if you isolate or extinguish the original source. An epidemic spreads
like gossip from person to person, until the complete population is infected.

Epidemic communication techniques are related to randomized flooding,
gossip and rumor spreading, and probabilistic data dissemination.
[http://en.wikipedia.org/wiki/Gossip Gossip] is defined as both the act of spreading news
from person to person, especially rumors or private information, and as the news
spread through the act of gossiping. This is one of the oldest and (still)
the most common means of spreading and sharing information. Human
gossip is also notorious for the introduction of errors and
other variations into the information thus transmitted.

In gossip protocols, a member forwards new information to randomly chosen members,
whereas in flooding and [[Wave Algorithm|wave algorithms]] a node sends new
information to all of its neighbors.
The key idea of epidemic computing and gossip techniques is that P2P systems could
“gossip” about important information or replicated data. Now and then, each node picks
some “peer” (at random, more or less) and sends it a snapshot of its own data
(called “push gossip”) or asks for a snapshot of the peer’s data (called “Pull” gossip).
A combination of both, a combined push-pull interaction usually works best. The infected
or informed “peers” do the same with their neighbours, and the information spreads
exponentially fast through the network.

To detect failures, the protocol gossips to figure out whom else is still
gossiping. Robbert van Renesse et al. proposed such a protocol in 1998. In
their gossip protocol each member, node or computer of a group maintains a
list for all other members of the group with the corresponding
address and a kind of heartbeat counter. From time to time,
each node increases its heartbeat counter and forwards its
list to a randomly chosen node. The node merges the list
with its own list. If a heartbeat counter has not increased
for a certain time (i.e. if it falls below a certain threshold),
the corresponding node is considered as failed

Epidemic computing was first proposed by Werner Vogels, Robbert van Renesse,
and Ken Birman from Cornell University, see
[http://weblogs.cs.cornell.edu/AllThingsDistributed/archives/000451.html History of Epidemics]
and [http://weblogs.cs.cornell.edu/AllThingsDistributed/archives/000456.html Epidemic Computing at Cornell]

== Articles ==

[http://www.cs.cornell.edu/home/rvr/papers/PowerEpidemics.pdf The Power of Epidemics: Robust Communication for Large-Scale Distributed Systems] Werner Vogels, Robbert van Renesse, Ken Birman, in Proceeding of HotNets-I '02: First Workshop on Hot Topics in Networks, special issue of the ACM SIGCOMM Computer Communication Review, Princeton, NJ, October 2002.

Kenneth P. Birman, ''The Suprising Power of Epidemic Communication'', Workshop on Future Directions in Distributed Computing (FuDiCo 2002). Bertinoro, Italy (June 2002). Springer-Verlag.

[http://www.cs.cornell.edu/home/rvr/papers/FightingFire.pdf Fighting fire with fire: using randomized gossip to combat stochastic scalability limits] Indranil Gupta, Kenneth P. Birman, Robert van Renesse. In Special Issue Journal Quality and Reliability Engineering International: Secure, Reliable Computer and Network Systems (ed. Nong Ye), vol. 18, no. 3, pp. 165-184, May/June 2002.

[http://www.irisa.fr/paris/Biblio/Papers/Kermarrec/EugGueKerMas04IEEEComp.pdf From Epidemics to Distributed Computing] P.T. Eugster et al.

[http://dcg.ethz.ch/lectures/ws0304/seminar/papers/randomized_rumor.pdf Randomized Rumor Spreading] R. Karp, C. Schindelhauer, S. Shenker, B. Vocking, Proc. IEEE Symposium on the Foundations of Computer Science, 2000.

[http://citeseer.ist.psu.edu/vanrenesse98gossipstyle.html A Gossip-Style Failure Detection Service]
Robbert van Renesse, Yaron Minsky, and Mark Hayden (1998) Technical Report TR98-1687, Cornell University, in Proc. of Middleware’98, pages 55–70

== arXiv papers ==

http://www.arxiv.org/abs/nlin.AO/0411017 Sheng Li, Meng Meng, Hongru Ma, ''Epidemic Spreading in Dynamic Small World Networks''

http://www.arxiv.org/abs/nlin.CD/0105044 Michelle Girvan, Duncan S. Callaway, M. E. J. Newman, Steven H. Strogatz,
''A Simple Model of Epidemics with Pathogen Mutation''

== SFI papers ==

[http://www.santafe.edu/research/publications/wpabstract/200502002 SFI Working Paper 05-02-002]
Luis M. A. Bettencourt et al., ''The Power of a Good Idea: Quantitative Modeling of the Spread of Ideas from Epidemiological Models''

[http://www.santafe.edu/research/publications/wpabstract/200412037 SFI Working Paper 04-12-037] Lauren Ancel Meyers, M. E. J. Newman, and Babak Pourbohloul, ''Predicting Epidemics on Directed Contact Networks''

[http://www.santafe.edu/research/publications/wpabstract/200204020 SFI Working Paper 02-04-020] M. E. J. Newman, ''The Spread of Epidemic Disease on Networks''

[http://www.santafe.edu/research/publications/wpabstract/200112073 SFI Working Paper 01-12-073] M. E. J. Newman, ''Exact Solutions of Epidemic Models on Networks''

[http://www.santafe.edu/research/publications/wpabstract/200105030 SFI Working Paper 01-05-030] Michelle Girvan, Duncan S. Callaway, M. E. J. Newman, and Steven H. Strogatz, ''A Simple Model of Epidemics with Pathogen Mutation''

[http://www.santafe.edu/research/publications/wpabstract/200001002 SFI Working Paper 00-01-002] Cristopher Moore and M. E. J. Newman, ''Epidemics and Percolation in Small-World Networks''

[[Category:x-Computing Techniques]]

Agent

2010-11-24T02:46:51Z

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Ubiquitous Computing

2010-11-24T02:46:43Z

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'''Ubiquitous computing''' (ubicomp) describes a type of computing where computers are everywhere invisibly embedded into our everyday environment. It is a model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. As opposed to the desktop paradigm, in which a single user consciously engages a single device for a specialized purpose, someone "using" ubiquitous computing engages many computational devices and systems simultaneously, in the course of ordinary activities, and may not necessarily even be aware that they are doing so.

[[Category:X-Computing_Techniques]]

Self-Management

2010-11-24T02:46:29Z

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Self-Management is a [[Self-Star_Properties|Self-* Property]] and a basic form of [[Self-Organization]]. It means to organize the system by introducing organizers, managers, and directors: to manage the systems by managers with obey the objectives and goals. Hierarchies of managers are in fact ubiquitous in social systems and [[Organization|organizations]].

[[Category:Self-Star Properties]]

Total Algorithm

2010-11-24T02:46:00Z

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