Computer Simulation of Networks Rudra Dutta CSC 401- Fall 2011, Section 001

Objective - Performance Study Direct Measurement Reactive Disrupts the user/system, affects the behavior/characteristics Can be done only on completed running systems Predictive Analytical Methods Useful if the model is available and is computationally efficient Most systems are complex and require highly complex mathematical models Usually only steady state can be modeled Simulation Give more detail than analytical modeling Very useful when large design space to be explored Can study dynamic behavior Not exact and involves some amount of approximation Can explore only part of operational space Halfway approach - some implementation Copyright Fall 2011, Rudra Dutta, NCSU

Image courtesy of GSU, “Hyperphysics” site, Rovio Simulation and Model Image courtesy of GSU, “Hyperphysics” site, Rovio Copyright Fall 2011, Rudra Dutta, NCSU

Simulation Programmatic representation of system entities We know how bits and pieces of the system work algorithmically Possibly for some bits we know mathematically Implement these algorithms and let them interact Two main types of simulation Discrete event discrete points in time, using random number generators Continuous time State changes occur continuously across time Packages E.g. OPNET (commercial), NS-2 (open source), OMNeT++ Levels of detail E.g. packet level, flow level Copyright Fall 2011, Rudra Dutta, NCSU

Discrete Event Simulation Useful for speeding up when details are abstracted Decide what happens first Many things happen after this that are not modeled in the simulation E.g. in GBN the next thing after receiving frame is sending ACK Decide what events are triggered by this event that are modeled by simulation Arrange in order of time of occurrence Jump to next event, do the same Until you run out of events, or have run it for “long enough” Copyright Fall 2011, Rudra Dutta, NCSU

Discrete Event Example Sender transmits three messages at times 0s, 10s, 20s The channel takes 2s to propagate Receiver takes a random time between 1s and 9s to process Sends back either a “more info” request or an “OK”, alternately Upon “more info”, sender sends one more message immediately 0s: S sends P1 2s: R rcvs P1 10s: S sends P2 20s: S sends P3 Copyright Fall 2011, Rudra Dutta, NCSU

Discrete Event Example 0s: S sends P1 Sender transmits three messages at times 0s, 10s, 20s The channel takes 2s to propagate Receiver takes a random time between 1s and 9s to process Sends back either a “more info” request or an “OK”, alternately Upon “more info”, sender sends one more message immediately 2s: R rcvs P1 9s: R sends MI 10s: S sends P2 11s: S rcvs MI 11s: S sends P1+ 12s: R rcvs P2 13s: R rcvs P1+ 20s: S sends P3 Copyright Fall 2011, Rudra Dutta, NCSU

Event Processors Each event generates new events Event generation algorithm is an algorithmic model of the part of the system being simulated “Callback” handlers or processors Processing algorithms represent specific entities, e.g. GBN receiver These can obviously grow quite complex Usually the bulk of the effort of simulation Also the problem specific part The Discrete Event framework is an engine and can be commoditized Hence the concept of “entity models” that plug into such an engine Copyright Fall 2011, Rudra Dutta, NCSU

Introduction to OPNET A variegated software package Modeler, IT Guru, SP Guru, … Modeler: a comprehensive development environment driven from GUI Features Object-oriented modeling Discrete event simulator Integrated data analysis tool More scalable and efficient simulation engine Hundreds of protocol and vendor device models Flexibility to develop detailed custom models Extensive online documentation and tutorials - quick overview here Copyright Fall 2011, Rudra Dutta, NCSU

Data Collection and Simulation Using OPNET Specification Data Collection and Simulation Analysis The cycle is repeated until we achieve the correct objective Copyright Fall 2011, Rudra Dutta, NCSU

Typical Applications Network (LAN/WAN) performance modeling Network planning R & D in communications architectures and protocols Resource sizing … Copyright Fall 2011, Rudra Dutta, NCSU

Model Specification Objective - developing a representation of the system Mirrors the hierarchical structure of real networks/systems/… Primarily three levels of abstraction Network Model (highest level) Entire network, e.g., the entire Internet Node Model Individual devices, e.g., computers, routers, servers, … Process Model (lowest level) Basic level of functionality Typically individual protocols Copyright Fall 2011, Rudra Dutta, NCSU

Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (1-a) Project Editor  Network Model Main work place for creating a network simulation Where you can : Create a network model using models from the Standard Model Library (the highest level of abstraction) Choose statistics to collect Execute a simulation View results Two options Object Palette (use built-in models) Build your own nodes Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (1-b) Corresponds to the network architecture / topology Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (2-a) Node Editor  Node Model 2nd level of abstraction Used to define the behavior of each network object (node/system) Important constituents of node model are Modules They model some internal aspect of node behavior E.g. simple traffic source, processor, … Packet streams (flow of data) Connect modules Two options Use the library Build your own process modules Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (2-b) Corresponds to the internal structure of devices in the network Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (3-a) Process Editor  Process Model The lowest level of abstraction Represented by Finite State Machines (FSMs) State (icons) Transitions (lines) Operations performed in each state or for a transition are described in embedded C/C++ code blocks Copyright Fall 2011, Rudra Dutta, NCSU

Creating Models in OPNET (3-b) Copyright Fall 2011, Rudra Dutta, NCSU

Many Other Editors E.g. link model editor, path editor, … Probe editor Specify the statistics to be collected during simulation, e.g. delay, throughput, utilization, … Two types of statistics Global Local Two ways to collect statistics Straight from the project editor Copyright Fall 2011, Rudra Dutta, NCSU

Simulation & Analysis Simulation execution Analysis using the “configure simulation” tool, or “advanced configure simulation” tool Can specify various attributes, simulation time and other details Analysis Directly from project editor No. of ways to analyze the results: get time-average, peak values, etc. Graphical analysis Copyright Fall 2011, Rudra Dutta, NCSU