An Introduction to Network Simulation Using Ptolemy Software Tool
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1 An Introduction to Network Simulation Using Ptolemy Software Tool Nazy Alborz Communication Networks Laboratory Simon Fraser University 1
2 Road Map: History Introduction to Ptolemy, its architecture and its applications Ptolemy environment Running a Ptolemy application (Leaky bucket mechanism) Simulation results 2
3 History: Created by Department of Computer and Electrical Engineering, U.C. Berkeley Named after the second century Greek astronomer and mathematician, Ptolemaeus Since 1990, seven C++ based versions, 0.1 to 0.7 released In 1999 new Java based version (Ptolemy II) was released Now four Java based versions, 0.1 to 0.4 are released 3
4 What is Ptolemy? Flexible foundation upon which many prototyping environments can be built. Examples: - Graphical programming for signal processing - Modeling of communication networks - Circuit design - Design assistance for hardware/software co design Capable of combining theses environments for modeling and simulation of complex heterogeneous systems Uses Object Oriented programming methodology to model and integrate these subsystems of the complex systems. 4
5 An example of a complex system: Video transmission through ATM network CONTROL (Setup, Routing, Flow control) Video Source Compression Cell Assembly Cell Transport Dummy Traffic Display Decompression Cell Disassembly These subsystems are interacting together: Video compression (Signal processing) Transport (Networking) Control (Software) 5
6 Ptolemy architecture: Ptolemy consists of a core (kernel) upon which special design environments (domains) are built. Kernel: A family of object-oriented classes that makes some assumptions about the system. Domain: Is defined by new object oriented classes based on Kernel classes.each domain is an appropriate computational model for a particular type of subsystem. Domains can interact with one another. Use of object-oriented software technology permits the domains to interact with one another without having knowledge about eachother Domains can model each subsystem of a complex, heterogeneous system in an efficient manner These different subsystems can be nested to form a tree of subsystems 6
7 Some existing domains in Ptolemy: Simulation domains: Interpreters that run an excecutable specification of a system on a local workstation DDF (Dynamic Data Flow) Special case of data flow model For graphs that flow of control is predictable at compile time SDF (Synchronous Data Flow) Used for dynamic (run-time) scheduling FSM (Finite State Machine) Used for describing control oriented systems DE (Discreet Event) Use event-driven computational model Particles carry time stamps to show events at a particular time Events happen in chronological order 7
8 Some existing domains in Ptolemy: Code generation domains: Translator domain that translates the specifications into some programming languages. CG (Code generation): Base for all other CG domains, Codes are generated, compiled and executed. CGC (Code generation in C) VHDL (code generation in VHDL) 8
9 Ptolemy Interfaces: Textual Interface (ptcl) Works on dumb terminals Accepts input commands from keyboard Based on Tcl (Tool command language), Extends Tcl by new adding new commands Graphical Interface (pigi) Graphical editor based on Berkeley CAD tools. Applications are constructed graphically by interconnecting icons 9
10 Running pigi: Vem console (upper left) Icon screen (Palette) representing icon directories (middle) Message window Welcome (lower right) 10
11 Ptolemy Objects: Block: The base class in Kernel for Stars, Galaxies and Universes. Star: Lowest level block, has functionality and defined in C++. Galaxy: A block containing a network of other blocks. Universe: An entire Ptolemy application. A type of Galaxy. Palette: An icon containing a library of other icons. Scheduler: Associated with each domain to determine the order of execution of Blocks inside the domain. Target: Manages the execution of a simulation or code generation process. 11
12 A complete Ptolemy application(universe): XStar XStar Galaxy XStar XStar XStar Galaxy XStar 12
13 Leaky bucket controller: Arriving packets Source node Queue of packets with permit Size = W A flow control rate mechanism. Arriving packets get a permit to enter network, else wait in queue. Permits generated at a constant rate and stored in a queue (bucket). Buckets have a max. capacity = W. Packets arriving while bucket size = W are discarded. Keep packet flow within a bound. Arriving permit : Mean arrival rate 13
14 Leaky Bucket Universe 14
15 Leaky Bucket Galaxy 15
16 Updown Galaxy 16
17 Updown1 Galaxy: SDF in DE Domain 17
18 Bucket Length vs. time Bucket Length Time 18
19 Packet loss vs. time Packet Loss Time 19
20 Conclusion Helps design of complex systems. Decreases complexity by modeling each subsystem individually. Different aspects of subsystems can be modeled in detail. Can be used in different design groups. Efforts of groups can be merged together. 20
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