Vishal Jain, AntNet Agent Based Strategy for CMDR “Agent Based Multiple Destination Routing ”

Vishal Jain, 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.

Vishal Jain, AntNet Agent Based Strategy for CMDR Steps in Sound Simulation

Vishal Jain, 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?

Vishal Jain, AntNet Agent Based Strategy for CMDR Example

Vishal Jain, 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.

Vishal Jain, 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

Vishal Jain, 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 Paper Model + True Distributive Approach = New Paper

Vishal Jain, 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 NodeCost Curr Path Delay Start Node Selection Pool & Decision ABAB

Vishal Jain, 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:

Vishal Jain, AntNet Agent Based Strategy for CMDR Program Structure:The Network

Vishal Jain, AntNet Agent Based Strategy for CMDR Program Structure:Node

Vishal Jain, AntNet Agent Based Strategy for CMDR Make Pilot Run

Vishal Jain, AntNet Agent Based Strategy for CMDR Tree Cost

Vishal Jain, AntNet Agent Based Strategy for CMDR Queue at Nodes

Vishal Jain, AntNet Agent Based Strategy for CMDR Comparison & Conclusion

Vishal Jain, AntNet Agent Based Strategy for CMDR

Vishal Jain, 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

Vishal Jain, 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….

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

Vishal Jain, AntNet Agent Based Strategy for CMDR Model

Vishal Jain, AntNet Agent Based Strategy for CMDR General Node Model

Vishal Jain, AntNet Agent Based Strategy for CMDR

Vishal Jain, 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 ]

Vishal Jain, 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

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

Vishal Jain, AntNet Agent Based Strategy for CMDR Graph of Mean Q Length

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/1/N 1/ = /  = 0.05 C=1 System Capacity = 4 ParamBookSimulation Lq Ls Wq Ws Server Busy Time p

Vishal Jain, AntNet Agent Based Strategy for CMDR Busy Server status

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/C 1/ = /  = C=4 System Capacity = inf ParamBookSimulation Lq Ls Wq Ws Server Busy Time p What if only three servers are there?

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/C 1/ = /  = C=3 System Capacity = inf ParamSimulation NewSimulation-old Lq Ls Wq Ws Server Busy Time p

Vishal Jain, AntNet Agent Based Strategy for CMDR Graph

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/C/N 1/ = /  = C=3 System Capacity = 6 ParamBookSimulation Lq Ls Wq Ws Server Busy Time p

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/C/C 1/ = /  = C=4 System Capacity = 4 ParamAppletSimulation Lq00 Ls Wq00 Ws Server Busy Time p

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/inf 1/ = /  = C=inf System Capacity = inf ParamAppletSimulation Lq00 Ls Wq00 Ws Server Busy Time p

Vishal Jain, AntNet Agent Based Strategy for CMDR M/M/C without & with priority Q Without PriorityWith Priority S[0]= S[1]= S[3]= S[4]= S[0] = S[1] = S[3] = S[4] = / = /  = C=4 System Capacity = inf

Vishal Jain, 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

Vishal Jain, 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