vishal jain, 200411029 1.1 antnet agent based strategy for cmdr “agent based multiple destination...

Vishal Jain , 2004110291.1AntNet Agent Based Strategy for CMDR

“Agent Based Multiple Destination

Routing ”


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.


Steps in Sound Simulation


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?


Example


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 problemmin(cost(T))

Delay(PT(s,di)) <= delta for each destination

Bandwidth of each link included in tree must be greater than B.

| Delay(PT(s,di)) - Delay(PT(s,dj)) | <= del for each pair of destination.


How Ants Works To find Shortest Path

Advantages:1. Distributed in nature2. No direct communication is required among agents3. Robust and adaptive to changes


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


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 DelayStart Node

SelectionPool &

Decision

A

B


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

0 1 2 3 4 5 6 7 8 9

-1 -1 -1 6 4 6 -1 3 -1 -1


Program Structure:The Network


Program Structure:Node


Make Pilot Run


Tree Cost


Queue at Nodes


Comparison & Conclusion


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


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….


Index

Conceptual Framework Black Box Model Understanding Simulation Run Queuing Models and Results


Model


General Node Model


Black Box Model

Queuing ModelStops 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 ]


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


M/M/1

1/ = 0.055555555 1/ = 0.142857142 C=1 System Capacity = inf

Lq = .2474 Lq = .24666


Graph of Mean Q Length


M/M/1/N

1/ = 0.0416666661/ = 0.05C=1System Capacity = 4

Param Book Simulation

Lq 1.494 1.4877

Ls 2.36 2.35329

Wq 0.0863 0.0861177

Ws 0.1363 0.136138

Server Busy Time 0.865596

p3 0.2787 0.250932


Busy Server status


M/M/C

1/ = 0.05555551/ = 0.1666666C=4System Capacity = inf


Lq 1.53 1.52249

Ls 4.53 4.52685

Wq 0.085 0.0847795

Ws 0.252 0.252033


p0 0.0377 0.038582

What if only three servers are there?


M/M/C


Param Simulation New Simulation-old

Lq 61.4072 1.52249

Ls 64.3673 4.52685

Wq 3.44632 0.0847795

Ws 3.61261 0.252033

Server Busy Time 2.96004 3.00436

p0 0.00403955 0.038582


Graph


M/M/C/N



Lq 0.479 0.291294

Ls 3.05 2.99995

Wq 0.271 0.0179682

Ws 1.386 1.66044


p0 0.0757 0.0496009


M/M/C/C


Param Applet Simulation

Lq 0 0

Ls 2.3816 2.36732

Wq 0 0

Ws 0.166666 0.166295


p0 0.061 0.0635515


M/M/inf

1/ = 0.05555551/ = 0.1666666C=infSystem Capacity = inf

Param Applet Simulation

Lq 0 0

Ls 3 2.9935

Wq 0 0

Ws 0.166666 0.166841

Server Busy Time ------- 2.9935

p0 0.0497 0.0506344


M/M/C without & with priority Q

Without Priority With 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



Failures

INF=150 or more M/M/inf not satisfactory Dynamic module creation was not successful Calling source limit


What Next with OMNet++

NoC : Async2005 paper for ASD course SCMDR : Antnet Muticast routing for NO course Quantum Cryptography

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