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 • Stigmergy • Indirect communication • Generally through the environment

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

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

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

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

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

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

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

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

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?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MABR: Routing

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

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

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

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

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

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

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

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

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

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

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

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

Swarm Intelligent Networking