1 / 70

Swarm Intelligent Networking

Swarm Intelligent Networking. Martin Roth Cornell University Wednesday, April 23, 2003. What is Swarm Intelligence?. Swarm Intelligence (SI) is the local interaction of many simple agents to achieve a global goal Emergence Unique global behavior arising from the interaction of many agents

luz
Download Presentation

Swarm Intelligent Networking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Swarm Intelligent Networking Martin Roth Cornell University Wednesday, April 23, 2003

  2. What is Swarm Intelligence? • Swarm Intelligence (SI) is the local interaction of many simple agents to achieve a global goal • Emergence • Unique global behavior arising from the interaction of many agents • Stigmergy • Indirect communication • Generally through the environment

  3. Properties of Swarm Intelligence • Properties of Swarm Intelligence are: • Agents are assumed to be simple • Indirect agent communication • Global behavior may be emergent • Specific local programming not necessary • Behaviors are robust • Required in unpredictable environments • Individuals are not important

  4. Swarm Intelligence Example The food foraging behavior of ants exhibits swarm intelligence

  5. Principles of Swarm Intelligence What makes a Swarm Intelligent system work? • Positive Feedback • Negative Feedback • Randomness • Multiple Interactions

  6. SI: Positive Feedback Positive Feedback reinforces good solutions • Ants are able to attract more help when a food source is found • More ants on a trail increases pheromone and attracts even more ants

  7. SI: Negative Feedback Negative Feedback removes bad or old solutions from the collective memory • Pheromone Decay • Distant food sources are exploited last • Pheromone has less time to decay on closer solutions

  8. SI: Randomness Randomness allows new solutions to arise and directs current ones • Ant decisions are random • Exploration probability • Food sources are found randomly

  9. SI: Multiple Interactions No individual can solve a given problem. Only through the interaction of many can a solution be found • One ant cannot forage for food; pheromone would decay too fast • Many ants are needed to sustain the pheromone trail • More food can be found faster

  10. Swarm Intelligence Conclusion • SI is well suited to finding solutions that do not require precise control over how a goal is achieved • Requires a large number of agents • Agents may be simple • Behaviors are robust

  11. SI applied to MANETs • An ad hoc network consists of many simple (cooperative?) agents with a set of problems that need to be solved robustly and with as little direct communication as possible • Routing is an extension of Ant Foraging! • Ants looking for food… • Packets looking for destinations… • Can routing be solved with SI? • Can routing be an emergent behavior from the interaction of packets?

  12. SI Routing Overview • Ant-Based Control • AntNet • Mobile Ants Based Routing • Ant Colony Based Routing Algorithm • Termite

  13. SI Routing Overview • Ant-Based Control • AntNet • Mobile Ants Based Routing • Ant Colony Based Routing Algorithm • Termite

  14. Ant-Based Control Introduction • Ant Based Control (ABC) is introduced to route calls on a circuit-switched telephone network • ABC is the first SI routing algorithm for telecommunications networks • 1996

  15. ABC: Overview • Ant packets are control packets • Ants discover and maintain routes • Pheromone is used to identify routes to each node • Pheromone determines path probabilities • Calls are placed over routes managed by ants • Each node has a pheromone table maintaining the amount of pheromone for each destination it has seen • Pheromone Table is the Routing Table

  16. ABC: Route Maintenance • Ants are launched regularly to random destinations in the network • Ants travel to their destination according to the next-hop probabilities at each intermediate node • With a small exploration probability an ant will uniformly randomly choose a next hop • Ants are removed from the network when they reach their destination

  17. ABC: Routing Probability Update • Ants traveling from source s to destination d lay s’s pheromone • Ants lay a pheromone trail back to their source as they move • Pheromone is unidirectional • When a packet arrives at node n from previous hop r, and having source s, the routing probability to r from n for destination s increases

  18. ABC: Routing Probability Update • Dp determined by age of packet • Probabilities remain normalized

  19. ABC: Route Selection (Call Placement) • When a call is originated, a circuit must be established • The highest probability next hop is followed to the destination from the source • If no circuit can be established in this way, the call is blocked

  20. ABC: Initialization • Pheromone Tables are randomly initialized • Ants are released onto the network to establish routes • When routes are sufficiently short, actual calls are placed onto the network

  21. ABC Conclusion • Only the highest probability next hop is used to find a route • Probabilities are changed according to current values and age of packet

  22. Reference • R. Schoonderwoerd, O. Holland, J. Bruten, L. Rothkranz, Ant-based load balancing in telecommunications networks, 1996.

  23. SI Routing Overview • Ant-Based Control • AntNet • Mobile Ants Based Routing • Ant Colony Based Routing Algorithm • Termite

  24. AntNet Introduction • AntNet is introduced to route information in a packet switched network • AntNet is related to the Ant Colony Optimization (ACO) algorithm for solving Traveling Salesman type problems

  25. AntNet Overview • Ant packets are control packets • Packets are forwarded based on next-hop probabilities • Ants discover and maintain routes • Internode trip times are used to adjust next-hop probabilities • Ants are sent between source-destination pairs to create a test and feedback signal system

  26. AntNet Route Maintenance(F) • Forward Ants, F, are launched regularly to random destinations in the network • F maintains a list of visited nodes and the time elapsed to arrive there • Forward Ant packet grows as it moves through the network • Loops are removed from the path list • F is routed according to next-hop probability maintained in each node’s routing table • A uniformly selected next hop is chosen with a small exploration probability • If a particular next hop has already been visited, a uniformly random next hop is chosen

  27. AntNet Route Maintnence(B) • When F arrives at its destination, a Backward Ant, B, is returned to the source • B follows the reverse path of F to the source • At each node, B updates the routing table • Next-hop probability to the destination • Trip time statistics to the destination • Mean • Variance

  28. AntNet Routing • Data packets are routed using the next-hop probabilities • Forward ants are routed at the same priority as data packets • Forward Ants experience the same congestion and delay as data • Backward ants are routed with higher priority than other packets

  29. AntNet Conclusion • AntNet is a routing algorithm for datagram networks • Explicit test and feedback signals are established with Forward and Backward Ants • Routing probabilities are updated according to trip time statistics

  30. AntNet Reference • G. Di Caro, M. Dorigo, Mobile Agents for Adaptive Routing, Technical Report, IRIDIA/97-12, Universit Libre de Bruxelles, Beligium, 1997.

  31. SI Routing Overview • Ant-Based Control • AntNet • Mobile Ants Based Routing • Ant Colony Based Routing Algorithm • Termite

  32. Mobile Ants-Based Routing Intro • Mobile Ants-Based Routing (MABR) is a MANET routing algorithm based on AntNet • Location information is assumed • GPS

  33. MABR Overview MABR consists of three protocols: • Topology Abstracting Protocol (TAP) • Simplifies network topology • Mobile Ants-Based Routing (MABR) • Routes over simplified topology • Straight Packet Forwarding (SPF) • Forward packets over simplified topology

  34. MABR: Topology Abstracting Protocol • TAP generates a simplified network topology of logical routers and logical links • All individual nodes are part of a logical router depending on their location • A single routing table may be distributed over all nodes that are part of a logical router

  35. MABR: TAP • Zones are created, each containing more logical routers than the last • Zones are designated by their location • Logical links are defined to these zones

  36. MABR Routing • An AntNet-like protocol with Forward and Backward ants is applied on the logical topology supplied by TAP • Forward ants are sent to random destinations • Ants are sent to the zones containing these destinations • Ants collect path information during their trip • Backward ants distribute the path information on the way back their source • Logical link probabilities are updated

  37. MABR: Routing

  38. MABR: Straight Packet Forwarding • Straight Packet Forwarding is responsible for moving packets between logical routers • Any location based routing protocol could be used • MABR is responsible for determining routes around holes in the network • SPF should not have to worry about such situations

  39. MABR Conclusion • The network topology is abstracted to logical routers and links • TAP • Routing takes place on the abstracted topology • MABR • Packets are routed between logical routers to their destinations • SPF • MABR is still under development • Results are not yet available

  40. SI Routing Overview • Ant-Based Control • AntNet • Mobile Ants Based Routing • Ant Colony Based Routing Algorithm • Termite

  41. Ant Colony Based Routing Overview • Ant-Colony Based Routing (ARA) uses pheromone to determine next hop probability • Employs a flooding scheme to find destinations

  42. ARA Route Discovery To discover a route: • A Forward Ant, F, is flooded through the network to the destination • A Backward Ant, B, is returned to the source for each forward ant received

  43. ARA Route Discovery • Reverse routes are automatically established as forward ants move through the network • Backward ants reinforce routes from destination to source

  44. ARA Routing • Next Hop Probabilities are determined from the pheromone on each neighbor link

  45. ARA Pheromone Update When a packet is received from r at n with source s and destination d: • r updates its pheromone table • n updates its pheromone table

  46. ARA Pheromone Decay Pheromone is periodically decayed according to a decay rate, t

  47. ARA Loop Prevention • Loops may occur because route decisions are probabilistic • If a packet is received twice, an error message is returned to the previous hop • Packets identified based on source address and sequence number • The previous hop sets Pn,d = 0 • No more packets to destination d will be sent through next hop n

  48. ARA Route Recovery • A route error is recognized by the lack of a next-hop acknowledgement • The previous hop node sets Pn,d = 0 • An alternative next hop is calculated • If no alternative next hop exists, the packet is returned to previous hop • A new route request is issued if the data packet is returned to the source

  49. ARA Conclusion • ARA is a MANET routing algorithm • Flooding is used to discover routes • Automatic retransmit used to recover from a route failure • Packet backtracking used if automatic retransmit fails • Next Hop probability proportional to pheromone on each link

  50. ARA Reference • M. Gunes, U. Sorges, I. Bouaziz, ARA – The Ant-Colony Based Routing Algorithm for MANETs, 2003.

More Related