Swarm Computing

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Swarm Computing Routing Algorithms

• Dr. Mikhail Nesterenko
• Presented By
• Ibrahim Motiwala

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Swarm

• Swarm is a software package for multi-agent
  simulation of complex systems. It is a collection
  of object oriented software libraries which
  provide support for simulation programming.
• A Swarm is an object that implements
• memory allocation
• event scheduling

Model
GUI
Swarm kernel
Operating System
CPU
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Allows you to treat the model as a nested
hierarchy of models. Schedules are objects, all
agents in simulation can communicate with them
future events or cause actions to be dropped
based.
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• Schedule
• Is a ordered set of events or a group of events.
• Probe
• Probe attaches itself to an agent,
• send a message,
• change a variable or
• retrieve values by calling agent or
• reading variable directly
• Agent
• Provides service
• communicates with other agents
• No prior knowledge of the other agent can be
  assumed, not even the form of its communication.

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Event simulation
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The Swarm, OOP way
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Some terminology

• Class
• The definition of an object and the object
  factory
• Superclass
• A class that an object inherits behavior and
  variables from (recursive)
• Subclass
• A class that inherits behavior and variables from
  a superclass

• Instance
• An object (instance of a class) that has been
  created and exists in memory
• Instance variable
• A variable available to all functions in an
  object
• Method
• A function. Can be called by sending message to
  an object of this class

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• Encapsulation
• Objects hide their functions (methods) and data
  (instance variables and method variables)
• Inheritance
• Each subclass inherits all variables of its
  superclass
• Polymorphism
• Multiple instances of same class, sharing
  behavior but not state or memory
• Main difference between C and Objective-C
• Easy to learn A simple superclass of C - no new
  keywords
• Main difference Partially interpreted Dynamic
  binding at runtime for objects and methods.

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A few Objective-C basics
_at_interface Bug SwarmObject int xPos,
yPos int worldXSize, worldYSize id
foodSpace -setX (int) x Y (int)
y -step _at_end
Super class
Sub classes
Instance Variables
Methods
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• Scientific objectives
• Scale
• - components increase
• - critical mass.
• Physical Embedding
• - interacting directly with a
  physical environment.
• Device Administrator Ratio
• - automatic configuration
• - group formation
• - failure detection
• - fault-tolerance.
• - No individual devices.

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• Swarm intelligence for Routing in Communication
  Networks
• Routing algorithms must address 2 issues
• average throughput and delay
• Good delay throughput curve
• Quality of service (QoS)
• guaranteed allocation
  of bandwidth
• maximum delay
• minimum hop-count.
• Swarm intelligence mobile software agents n/w
  management autonomous entities
• adapt
• intelligent movement

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• Routing algorithms can be classified as static or
  dynamic, and centralized or distributed.
• Centralized algorithms
• - scalability
• - failure at the central controlling
  station.
• Static routing
• time-invariant.
• shortest-path route.
• Adaptive routing
• node failures , potential
  oscillations that lead to circular paths and
  instability
• frequently changing routing
• Routing algorithms
• non-minimal ( choosing the
  path by utilizing other heuristics).
• minimal (optimal routing and
  shortest-path routing)
• 2. QoS algorithm pertaining to
  delay and bandwidth.

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• Swarm Intelligence
• Swarm Intelligence appears in
  biological swarms of certain insect species.
  
• Interaction requires no
  supervision.
• Stigmergy, or communication through the
  environment.
• Pheromone , hormone that can
  be sensed by ants as they travel along trails.
• task-related stigmergy sand grain
  laying by termites when constructing
  nests .
• Random location -gt
  critical mass

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• Swarm Intelligence
• Scalability
• Population adaptation
• Scalability is also promoted
  by local and distributed agent interactions.
• Fault tolerance
• Do not rely on a centralized
  control mechanism.
• Graceful, scalable
  degradation.
• Adaptation
• Change, die or reproduce
• Speed
• Changes can be propagated
  very fast.
• Modularity
• Agents act independently
• Autonomy
• Little or no human
  supervision.
• Parallelism
• Operations are inherently
  parallel.

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• Routing Algorithms
• AntNet adaptive agent-based routing algorithm.
• ABC Ant-Based Control.
• ARS agent-based routing system.
• AntNet
• Two kinds of Agents(Ant Packets)
• Forward Ant.
• explores the network and collects information.
• when reaches the destination, changes into
  backward ant.
• Backward Ant.
• goes back in the same path as forward ant.
• update routing tables for all the nodes in the
  path.

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• Subdivision of agents
• Backward ants to utilize the useful
  information gathered by the forward ants on their
  trip from source to destination.
• Update the routing table of the nodes.
• No node routing updates are performed by
  the forward ants.
• Report delay conditions
  to the backward ants, trip times, update the
  routing table of the nodes.
• The entries of the routing table are
  probabilities, and as such, must sum to 1 for
  each row of the network.
• (1) the exploration agents of the
  network use them to decide the next hop to a
  destination, randomly selecting among all
  candidates based on the routing table
  probabilities for a specific destination
• (2) the data packets
  deterministically select the path with the
  highest probability for the next hop.
• 
  AntNet routing table

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• 1. Each network node launches forward ants to all
  destinations in regular time intervals.
• 2. The ant finds a path to the destination
  randomly based on the current routing tables.
• 3. The forward ant creates a stack, pushing in
  trip times for every node as that node is
  reached.
• 4. When the destination is reached, the backward
  ant inherits the stack.
• 5. The backward ant pops the stack entries and
  follows the path in reverse.
• 6. The node tables of each visited node are
  updated based on the trip times.

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• Raw information contained in the trip
  time is processed and then used to manage the
  system more efficiently .

Intermediate quantity in the processing of the
raw trip time information, we need a measure that
takes on small values when the trip time is short
relative to the mean and vice-versa. This
quantity, r'
where T is the trip time, µ is average of T, and
C is a scaling factor, usually set to 2. Except
for the routing table, each node also possesses a
table with records of the mean and variance of
the trip time to every destination.
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• Ratio of the variance to the mean, ( µ / s ),
• measures the consistency of
  the trip times,
• alter the effect of the trip
  time
• On r?, determine the relative
  goodness of the trip time of an ant.
• Strategies of either decreasing or increasing the
  value of r by a certain amount. Based on setting
  the threshold for the good/bad trip time to 0.5,
  and selecting a threshold d for the ( µ / s )
  ratio.

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• Small values of r' correspond to small values of
  T and vice versa.
• e.g. . Where consistency
  is high and the time is good, r? to be even
  smaller underscores goodness of this trip time
  and its consistency.
• an exponential quantity is subtracted. This
  quantity is the exponentially decaying function
  of the consistency ratio and achieves its highest
  value when the variance is very small. The decay
  rate can be controlled by a? and a.
• ve or -ve reinforcement of good or bad routes
  is next, via -ve feedback.
• ve reinforcement è-negatively proportional to
  current probabilities
• -ve reinforcement è proportional to current
  probabilities.
• Prevent saturation to 0 or 1 of the
  routing table probabilities.
• Positive reinforcement is the one from
  which the backward ant comes.
• Neighbors èvely reinforced to
  preserve the unit sum probabilities of next hops.
  
• The
  reinforcement equations

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• where Pdf, Pdn are the previous routing table
  probabilities,
• f is the node from which the backward
  ant comes,
• Nk is the neighborhood of node k
  (current node), and
• d is the destination node.

The last step is to update the routing table
probabilities using the following rules.
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• Ant-based Control ( ABC )
• Algorithm shares many key features with AntNet,
• Use of a multitude of agents interacting
  using stigmergy.
• Adaptive and exhibits robustness under
  various network conditions.
• Randomness in the motion of ants.
• Ants only traverse the network nodes
  following highest probability
• Differences
• Only one class of ants, which is launched from
  the sources to various destinations at regular
  time intervals.
• The ants are eliminated once they reach their
  destination.
• Probabilities of the routing tables are
  updated as the ant visits the nodes, based on the
  life of the ant at the time of the visit.
• Update rules are different.

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• Multiple Round Trip Routing
• Nodes launch forward ants in regular intervals.
• Utilizes the cost measured by forward ants to
  update the routing table entries.
• The interesting improvement to this algorithm is
  based on Bellmans principle of dynamic
  programming.
• Every node in the path of a
  source-destination pair s-d, is considered a
  destination.
• The back-propagating agent will
  update the routing table of a visited node n not
  just for the destination, but also for the
  intermediate nodes.
• Hence the updates occur all at
  once.
• example, on node n , the backward agent will also
  update the entry for node s1.

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• Conclusion
• 1. AntNet and Multiple Trip Routing
• Round-trip agents. the forward ants act
  as investigators and the backward ants are the
  ones who update the routing tables.
• 2. ABC
• Forward agents, who perform the update
  as they travel through the network.
• update is faster and
  more reliable, no delay.
• Inherent properties of swarm intelligence
• massive system scalability
• emergent behavior and intelligence
  from low complexity local interactions
• autonomy, and stigmergy
• communication through the
  environment.