OPTICAL PACKET SWITCHING OVER ARBITRARY PHYSICAL TOPOLOGIES USING THE MANHATTAN STREET NETWORK

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• OPTICAL PACKET SWITCHING OVER ARBITRARY PHYSICAL
  TOPOLOGIES USING THE MANHATTAN STREET NETWORK
• AN EVOLUTIONARY APPROACH
• Olufemi Komolafe - University of Strathclyde, UK.
• David Harle - University of Strathclyde, UK.
• David Cotter - Corning Research Centre, UK.

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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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OPTICAL PACKET SWITCHING
Optical logic devices immature (w.r.t.
electronics)
Unavailability of static random access optical
memory devices
Avoid contentions in optical domain (or resolve
minimising buffering)
Simple routing processing
Multiprocessor Interconnection Architectures
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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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Manhattan Street Network (MSN)

• Directed graph
• N X N nodes
• N is even
• Degree 2
• Toroidal network
• all nodes topologically equivalent
• Traditional routing scheme
• based on computing relative addresses
• contentions in network

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MSN - Clockwork Routing

• Operation
• All nodes synchronised to global clock
• Timeslots arranged in Modulo N sequence of frames
  (for N x N MSN)
• Automated switch state changes at nodes
• No additional routing processing at intermediate
  nodes
• only for me or not for me?
• No contention within network
• peripheral electronic buffering used

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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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DEPLOYMENT
How can the MSN be used to reduce routing
complexity avoid optical domain buffering over
topologies such as these?
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Deploy as Virtual Topology

• Use MSN as virtual topology in WDM networks
• Nodes of MSN mapped onto physical topology nodes
• Nodes interconnected by lightpaths established
  according MSN connectivity
• Clockwork Routing used to switch packets
  optically at lightpath terminals
• Lightpath length will vary according to mapping
  of MSN nodes onto physical topology nodes

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Deployment Cost

• Cost Mean lightpath length
• impacts end-to-end number of hops packets
  traverse
• affects number of ? needed
• indicates number of optical cross-connects
  traversed between adjacent MSN nodes
• affects deployment of optical amplifiers
  consumption of other network resources

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8 Costs

• Maximum mean
• lightpath length
• inter-nodal distance
• Shortest Path routing discipline
• inter-nodal distance
• Random Choice routing discipline
• inter-nodal distance
• Minimum number of physical hops
• post-embedding route selection

of ? physical hops (i.e. OXCs) traversed by
lightpaths
optical cross connects traversed by packets
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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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GENETIC ALGORITHMS (GA)

• Adaptive computational models inspired by nature
• population of solutions evolve to yield better
  solutions
• Features of problems to which GA applied
• Readily accurately encodable as a binary string
• No ordering dependencies
• Frequency of value inconsequential

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Problem Encoding

• Each individual in population represents an
  embedding of the MSN in physical topology

MSN node 4 mapped onto physical topology node 1
Fitness/Cost Resulting mean lightpath length
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Crossover Techniques

• Crossover
• Used to produce offspring for next generation
  from 2 parents
• Simplest form
• selecting random point in parents and then
  swapping tails

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Simple Crossover
2 Parents..
A
B
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Simple Crossover
A
B
Randomly selected crossover point
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Simple Crossover
PARENTS
OFFSPRING
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BUT.
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Crossover Techniques

• Duplications and omissions will occur which
  produce invalid offspring
• Similar problems faced when applying GA to
  Travelling Salesman Problem
• GA encoding
• Individual Touring order of city
• Fitness/Cost Length of tour
• Crossover Techniques developed and adapted for use

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Crossover Techniques

• 4 different crossover techniques used
• Crossover Correct
• correct any omissions and duplications
• Partially Mapped Crossover
• swap elements between 2 randomly chosen points
• Order Crossover
• seeks to preserve parents order in offspring
• Cycle Crossover
• ensures each element in offspring comes from same
  position in a parent

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Selection of Optimum GA Parameters

• GA parameters empirically selected
• Population size
• speed memory limitations important
• sizes of 250, 500, 1000 and 2000 considered
• Crossover probability
• parents may survive unaltered in next generation
• probability of 0.5 and 1 considered
• What is the best population size and crossover
  probability for each crossover technique?

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Crossover Correct (XC)
Mean lightpath length
Generations
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Partially Mapped Crossover (PMX)
Mean lightpath length
Generations
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Order Crossover (OX)
Mean lightpath length
Generations
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Cycle Crossover (CX)
Mean lightpath length
Generations
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Selection of Optimum GA Parameters

• Crossover Correct, Cycle Crossover
• Population 2000
• P(Crossover) 1
• Partially Mapped Crossover
• Population 1000
• P(Crossover) 1
• Order Crossover
• Population 2000
• P(Crossover) 0.5

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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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RESULTS

• Optimum parameters applied for each crossover
  technique and used to deploy MSN in arbitrary
  physical topologies
• Different MSN sizes used
• 4x4, 6x6, 8x8,10x10

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16 Node Physical Topology
Mean lightpath length
Generations
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36 Node Physical Topology
Mean lightpath length
Generations
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64 Node Physical Topology
Mean lightpath length
Generations
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100 Node Physical Topology
Mean lightpath length
Generations
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Explanation Exploration Vs Exploitation

• 2 fundamental operations of search algorithms

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Explanation Exploration Vs Exploitation

• Order Crossover gives worst result
• good at exploration but exploits wrong
  information
• seeks to preserve order NOT position

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Order Crossover (OX)
P(Crossover) 1 yields no improvement hence
little relevant exploitation done
Mean lightpath length
P(Crossover) 0.5 means that there is some
chance of important characteristics being passed
down generations
Generations
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Explanation Exploration Vs Exploitation

• Order Crossover gives worst result
• good at exploration but exploits wrong
  information
• seeks to preserve order NOT position
• Crossover Correct obtains good results despite
  relative simplicity
• Cycle Crossover arguably best at exploitation
• all positions in offspring filled with element
  from same position in parent
• Partially Mapped Crossover best combines
  exploration and exploitation synergistically

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Comparison with Other Approaches

• Other techniques may be used to find good
  embeddings of MSN in physical topology
• Simulated Annealing (SA)
• Hill Climbing (HC)
• Random Search (RS)
• Results obtained compared to best GA result found

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Results of All Approaches
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Comparison with Other Approaches

• GA gives best result for most network sizes
• Evaluating cost function time-consuming
• indicative of time taken for solution to be found
• GA and HC consider similar number of solutions,
  but SA and RS consider 10 and 20 times more
  solutions
• ? GA typically gives the best result and does so
  significantly quicker than the other techniques

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OVERVIEW

• INTRODUCTION
• MSN WITH CLOCKWORK ROUTING
• DEPLOYMENT
• GENETIC ALGORITHMS
• RESULTS
• CONCLUSION

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CONCLUSION

• Manhattan Street Network with Clockwork Routing
• no optical domain contention
• simple routing scheme
• Genetic algorithms proposed and implemented as
  method to deploy Manhattan Street Network in
  arbitrary physical topologies
• Genetic algorithms seen to out-perform other
  heuristics and yet still be significantly quicker