Mobile Agents for Adaptive Routing

Mobile Agents for Adaptive Routing Presented by Hong-Jiun Chen & Manu Prasanna Gianni Di Caro & Marco Dorigo

Hong-Jiun Manu Outline • Introduction • Overview of Routing Algorithms • Communication Network Model • AntNet • Other Routing Algorithms • Experiment Settings • Experiment Results • Conclusion

Real ants have been shown to be able to find the shortest paths by using only the pheromone trail deposited by other ants I’m Real Ant Introduction • AntNet

Introduction • AntNet • A new routing algorithm for telecommunication networks • An adaptive, distributed, mobile-agents-based algorithm • Apply it in a datagram network

Introduction • Terminology • Routing • Throughput • Delay (Latency)

Introduction • Routing • It refers to the activity of building forwarding tables, one for each node in the network, which tell incoming data which link to use to continue their travel towards the destination node.

Introduction • Throughput • It is the number of bits which the network is able to carry in a given period of time

Introduction • Delay (latency) • Propagation delay • Queuing delay • Processing delay • Transmission delay: The time elapsed from the moment the first bit of the message is transmitted till the last bit of the message is transmitted

Outline • Introduction • Overview of Routing Algorithms • Communication Network Model • AntNet • Other Routing Algorithms • Experiment Settings • Experiment Results • Conclusion

Routing Algorithm • Goal • To direct traffic from sources to destinations • Network performance  • Costs

Routing Algorithm • The performance metrics: • throughput (bits/second) • delay (second) • Static or Adaptive?

Communication Network Model • Apply on datagram networks without concerning congestion and admission control • FIFO • When links resources are available, they are reserved and the transfer is set up • The time it takes a packet from one node to another depends on its size and the link transmission characteristics • No ACK

2 1 I’m Forward Ant AntNet • 1. Forward antFsd is launched Describe it by 6 simple steps: G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

2 1 A S 0 5 AntNet • 2. Ssd (k) is inserted, time elapsed is stored in stack G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

3 1 2 S A C 0 5 8 AntNet • 2.keep it going to next hop G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

5 6 4 3 C F E 15 11 18 1 2 S A C 5 8 0 AntNet • 3.A circle is detected G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

4 6 5 3 C F E 15 11 18 1 2 S A C 5 8 0 AntNet • 3.A circle detected, delete all the nodes in that circle from the stack G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

3 E F C 11 15 18 1 2 S A S C A G 8 5 5 0 7 0 AntNet • 3. Start over from the last node without circles G 2 2 A D 1 3 C 5 3 OLD NEW 5 E 3 S 4 F 1

4 1 3 2 S D G A 0 7 5 9 AntNet • 4. Destination node reached G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

I’m Backward Ant 1 S D G A 0 7 5 9 AntNet • 4. Destination node reached, the ant Fsd generates another backward antBds G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

1 S D G A 0 7 5 9 AntNet • 5. Backward ant pops its stack to know the next hop node G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

2 1 S A G 5 7 0 AntNet • 5. Backward ant pops its stack to know the next hop node G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

3 2 1 S A 0 5 AntNet • 5. Backward ant pops its stack to know the next hop node G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

4 3 2 1 S 0 AntNet • 5. Backward ant pops its stack to know the next hop node G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

4 3 2 1 AntNet • 6. Whenever the Backward ant arrives a node, it updates 2 things: • 1. A List Trip(i , i2) • 2. The Routing Table G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

4 3 1 AntNet • 1. Change A List Trip(i , i2) • It estimates arithmetic mean values i and associated variances i2 for trip times from the node itself to all the nodes i in the network G 2 2 A D 1 3 C 5 3 5 E 3 S 4 F 1

4 3 2 1 AntNet • 2. Change The Routing Table G OLD 2 2 A D 1 3 C 5 3 5 E NEW 3 S 4 F 1

Manu Outline • Introduction • Overview of Routing Algorithms • Communication Network Model • AntNet • Other Routing Algorithms • Experiment Settings • Experiment Results • Conclusion

Other Routing Algorithms

Experimental Settings • Topology and Physical properties • NFSNET with 14 nodes and 21 links • Bandwidth of links = 1.5Mbit/s • Link/node fault probability = 0 • Local buffer capacity = 1GB • Statistical multiplexing

Experimental Settings • Traffic Patterns • Static Model • Constant bit rate • Dynamic Model • Variable bit rate

Experimental Settings • Geographical Distribution of Traffic • Uniform-deterministic distribution • Uniform-random distribution • Uniform-deterministic-hot-spots • Uniform-random-hot-spots

Experimental Results • Performance of all algorithms near optimal for low and uniform traffic loads • AntNet especially good in CBR case • AntNet algorithm shows overall best performance • Daemon algorithm (used for comparisons)

Conclusion • AntNet shows a robust behavior • Reaction time of algorithm is acceptable • Impact on network resources is neglectable

Strengths Possible Weaknesses Strengths and Possible Weaknesses • Good idea • Nice buildup • Time tested idea (ants have been around for sometime… 80 million years) • Scalability issues are ignored • Setup costs and time? • Feasibility for wireless networks?

New Ideas • The term is defined in the Oxford English Dictionary as The process by which the results of an insects activity act as a stimulus to further activity, and is used in the mobile robotics literature to describe activity in which an agent supplies changes to the world architecting its future behavior, usually in a useful way AntNet: new algorithm for adaptive routing • Stigmergy

Relevance to IES • If the goal of AI/Robotics is to make machines as intelligent as humans we should first start with imitating lesser intelligent animals (eg: ants) • Social behavior, community behavior, cooperation among ants/bees can be applied easily in robotics

The Ants: A Community of Microrobots • Source: MIT Artificial Intelligence Lab • Goals • push the limits of microrobotics by integrating many sensors and actuators into a small package • form a structured robotic community from the interactions of many simple individuals

The Ants: A Community of Microrobots

The Ants: A Community of Microrobots • Community behavior: • Clustering around food

Questions?

Mobile Agents for Adaptive Routing