1. Vishal Jain, 200411029 1.1 AntNet Agent Based Strategy for CMDR “Agent Based Multiple Destination Routing ”

2. Vishal Jain, 200411029 1.2 AntNet Agent Based Strategy for CMDR Goals and Non-Goals Goals  To see how simulation helps in studying a system?  To see, with an example of AntNet Multicasting, how modeling is done?  To see how by changing system configuration we can study system in better way?  To see how to validate your new idea with the help of simulation? Non Goals  To study computer network routing in details  To see other algorithm for multicasting.

3. Vishal Jain, 200411029 1.3 AntNet Agent Based Strategy for CMDR Steps in Sound Simulation

4. Vishal Jain, 200411029 1.4 AntNet Agent Based Strategy for CMDR Formulating the Problem:1 Warm up……  What is computer network?  Modeling network as graph  What is routing between network nodes?  What we wish to achieve in routing?  A new approach to routing : Multicasting….  Is multicasting is same as multiple point to point routing?

5. Vishal Jain, 200411029 1.5 AntNet Agent Based Strategy for CMDR Example

6. Vishal Jain, 200411029 1.6 AntNet Agent Based Strategy for CMDR Formulating the Problem:2 Problem statement  Let T(s,D) is a tree rooted at source node s with a set of destination nodes D.  Find a tree T(s,D), if it exists, which satisfies the following optimization problem min(cost(T))  Delay(P T (s,di)) <= delta for each destination  Bandwidth of each link included in tree must be greater than B.  | Delay(P T (s,di)) - Delay(P T (s,dj)) | <= del for each pair of destination.

7. Vishal Jain, 200411029 1.7 AntNet Agent Based Strategy for CMDR How Ants Works To find Shortest Path Advantages: 1.Distributed in nature 2.No direct communication is required among agents 3.Robust and adaptive to changes

8. Vishal Jain, 200411029 1.8 AntNet Agent Based Strategy for CMDR Study and Modelling Study of “A New Intelligent Agent Based Strategy for Constrained Multiple Destination Routing Problems”, David Elliman and Sherin M. Youssef. The computer Journal, BCS Jan 2004. Paper Model + True Distributive Approach = New Paper

9. Vishal Jain, 200411029 1.9 AntNet Agent Based Strategy for CMDR Brief Conceptual Model of Paper The forward agent colony searches for the target node it stores path as well as cost of path. At the destination(s) total tree cost is calculated and then corresponding amount of pheromone is updated at each traversed node in forward journey. This task is done by the backward agent 323.45 NodeCost Curr Path Delay Start Node Selection Pool & Decision ABAB

10. Vishal Jain, 200411029 1.10 AntNet Agent Based Strategy for CMDR Is conceptual model Valid?: No Why?  Global Locking  Cycle avoiding New Conceptual Model (Novel Idea © )  Each node will be having information about which colony has visited it already along with the information from where it arrived to this node. It helps in cycle avoiding as well as agents need not to be LOCKED. Example: 6 3 5 4 0123456789 646 3

11. Vishal Jain, 200411029 1.11 AntNet Agent Based Strategy for CMDR Program Structure:The Network

12. Vishal Jain, 200411029 1.12 AntNet Agent Based Strategy for CMDR Program Structure:Node

13. Vishal Jain, 200411029 1.13 AntNet Agent Based Strategy for CMDR Make Pilot Run

14. Vishal Jain, 200411029 1.14 AntNet Agent Based Strategy for CMDR Tree Cost

15. Vishal Jain, 200411029 1.15 AntNet Agent Based Strategy for CMDR Queue at Nodes

16. Vishal Jain, 200411029 1.16 AntNet Agent Based Strategy for CMDR Comparison & Conclusion

17. Vishal Jain, 200411029 1.17 AntNet Agent Based Strategy for CMDR

18. Vishal Jain, 200411029 1.18 AntNet Agent Based Strategy for CMDR Successes & Failures Successes  Desired size topology can be constructed at runtime  Any value of alpha, beta, gama, delta, del can be given  Excellent visualization  Results are accurate again Failure  Couldn’t able to separate data and ant generator  Traffic move as random not as per the routing table as it moves in the real network.  Note: But to check the feasibility of my idea these assumptions are ok

19. Vishal Jain, 200411029 1.19 AntNet Agent Based Strategy for CMDR Write Computer Program and Verify…… Used OMNet++ as a tool…..  Confidence in tool ?  Do tool support real life scenario for the simulation ?  Degree of insight it gives for the model you are programming…  Support for development (manual, community site, mailing list etc)….. Developed an application….

20. Vishal Jain, 200411029 1.20 AntNet Agent Based Strategy for CMDR Index Conceptual Framework Black Box Model Understanding Simulation Run Queuing Models and Results

21. Vishal Jain, 200411029 1.21 AntNet Agent Based Strategy for CMDR Model

22. Vishal Jain, 200411029 1.22 AntNet Agent Based Strategy for CMDR General Node Model

23. Vishal Jain, 200411029 1.23 AntNet Agent Based Strategy for CMDR

24. Vishal Jain, 200411029 1.24 AntNet Agent Based Strategy for CMDR Black Box Model Queuing Model Stops by statistical data Arrival Rate Service Rate Num Of Server System Capacity Server Busy Time Mean Q Length Mean Life Time in System Dropped Customer Number of Customers Mean Number of customer present in system Mean P[ i ]

25. Vishal Jain, 200411029 1.25 AntNet Agent Based Strategy for CMDR Features of Implementation The server is selected for service on the basis of idle time FreeServerList[] Two way send() Methods isEmpty(), recordReq(), Request() Stop by statistical information Generalized Very accurate results

26. Vishal Jain, 200411029 1.26 AntNet Agent Based Strategy for CMDR M/M/1 1/ = 0.055555555 1/  = 0.142857142 C=1 System Capacity = inf Lq =.2474 Lq =.24666

27. Vishal Jain, 200411029 1.27 AntNet Agent Based Strategy for CMDR Graph of Mean Q Length

28. Vishal Jain, 200411029 1.28 AntNet Agent Based Strategy for CMDR M/M/1/N 1/ = 0.041666666 1/  = 0.05 C=1 System Capacity = 4 ParamBookSimulation Lq1.4941.4877 Ls2.362.35329 Wq0.08630.0861177 Ws0.13630.136138 Server Busy Time0.865596 p30.27870.250932

29. Vishal Jain, 200411029 1.29 AntNet Agent Based Strategy for CMDR Busy Server status

30. Vishal Jain, 200411029 1.30 AntNet Agent Based Strategy for CMDR M/M/C 1/ = 0.0555555 1/  = 0.1666666 C=4 System Capacity = inf ParamBookSimulation Lq1.531.52249 Ls4.534.52685 Wq0.0850.0847795 Ws0.2520.252033 Server Busy Time3.00436 p00.03770.038582 What if only three servers are there?

31. Vishal Jain, 200411029 1.31 AntNet Agent Based Strategy for CMDR M/M/C 1/ = 0.0555555 1/  = 0.1666666 C=3 System Capacity = inf ParamSimulation NewSimulation-old Lq61.40721.52249 Ls64.36734.52685 Wq3.446320.0847795 Ws3.612610.252033 Server Busy Time2.960043.00436 p00.004039550.038582

32. Vishal Jain, 200411029 1.32 AntNet Agent Based Strategy for CMDR Graph

33. Vishal Jain, 200411029 1.33 AntNet Agent Based Strategy for CMDR M/M/C/N 1/ = 0.0555555 1/  = 0.1666666 C=3 System Capacity = 6 ParamBookSimulation Lq0.4790.291294 Ls3.052.99995 Wq0.2710.0179682 Ws1.3861.66044 Server Busy Time2.70865 p00.07570.0496009

34. Vishal Jain, 200411029 1.34 AntNet Agent Based Strategy for CMDR M/M/C/C 1/ = 0.0555555 1/  = 0.1666666 C=4 System Capacity = 4 ParamAppletSimulation Lq00 Ls2.38162.36732 Wq00 Ws0.1666660.166295 Server Busy Time2.36732 p00.0610.0635515

35. Vishal Jain, 200411029 1.35 AntNet Agent Based Strategy for CMDR M/M/inf 1/ = 0.0555555 1/  = 0.1666666 C=inf System Capacity = inf ParamAppletSimulation Lq00 Ls32.9935 Wq00 Ws0.166666 0.166841 Server Busy Time-------2.9935 p00.04970.0506344

36. Vishal Jain, 200411029 1.36 AntNet Agent Based Strategy for CMDR M/M/C without & with priority Q Without PriorityWith Priority S[0]=0.72591 S[1]=0.688951 S[3]=0.634994 S[4]=0.572459 S[0] = 0.65387 S[1] = 0.653838 S[3] = 0.653846 S[4] = 0.6539 1/ = 0.0555555 1/  = 0.1666666 C=4 System Capacity = inf

37. Vishal Jain, 200411029 1.37 AntNet Agent Based Strategy for CMDR Failures INF=150 or more M/M/inf not satisfactory Dynamic module creation was not successful Calling source limit

38. Vishal Jain, 200411029 1.38 AntNet Agent Based Strategy for CMDR What Next with OMNet++ NoC : Async2005 paper for ASD course SCMDR : Antnet Muticast routing for NO course Quantum Cryptography