Producing simulation sequences using a Java-based generalized framework

Producing simulation sequences by use of
a Java-based generalized framework

D. Gianni, A. D’Ambrogio, G. Iazeolla, A. Pieroni
Dept. Of Computer Science
University of Rome TorVergata

presented by
Daniele Gianni
gianni@info.uniroma2.it

European Modelling Symposium 2008
Liverpool, United Kingdom, September 8 – 10, 2008 1

Presentation Overview

 Random numbers in simulation
 Problem statement
 Motivations
 jRand: generalised framework for sequences
 Example Application
 Preliminary performance assessment

Liverpool, United Kingdom, September 8 – 10, 2008
2

Random Numbers in Simulation

The modelling of physical systems is based on:
 Identification of system components (static and
dynamic properties)
 Identification of their relationships

Random numbers: grouping components into
macro components with analogous properties
3

Random Numbers in Simulation

In computer systems random variable are emulated
with predetermined and finite sequences of
numbers
The sequences need to be properly designed to
guarantee that they present good statistical
qualities
Several sequences are often needed for a single
simulation experiment European Modelling Symposium 2008
4

Problem Statement

Observation (O1):
The pattern of model logic is independent from the input
sequence

How can we improve the deployment of input
sequences in a simulator?

5

Motivations

 Large models present wide numbers of random
variables
 They are also complex in terms of model logic
and wide number of components

 Simulator developers need not be concerned
with input sequence details when implementing
the model logic
6

Introducing jRand

 We introduce jRand a Java component-based framework
to produce sequences of numbers
 jRand increases the flexibility in the configuration of the
input sequences
 jRand raises the simulation system developers from their
details while designing and coding simulation software,
therefore sequences can be designed and applied with
less effort for the investigated scenarios

7

jRand Core Concept
Producer/Consumer Pattern

Sequence Sequence
Producer Consumer

Random generators Simulation software

8

jRand Main Concepts

 Object Stream (base interface):
• getNext(): Object
• goTo(Integer i): Void

 Stream Classified according to three properties:
a. dependences between values in a sequence (Random, Pseudo-
Random, Deterministic)
b. the type of values (Objects, Numbers, etc.)
c. the structural details of the objects producers (Transformations, e.g.:
composition of two of more streams)
a. Round robin selection of one stream out of a set of a streams
b. Shuffled Stream – shuffles a stream with a numeric sequence
9

jRand Class Diagram
«interface»
ObjectStream
+getNext() : Object
+goTo(in n : long long(idl))

«interface» «interface»
«interface» «interface» «interface»
NumericStream StreamTrasformation
DeterministicStream PseudoRandomStream RandomStream
+getNext() : Number
+goTo()

RoundRobinStreamTransformation
«interface» «interface» «interface»
DeterministicNumericStream PseudoRandomNumericStream RandomNumericStream
ShuffledStreamTransformation

SystemNanoSeconds
«interface» «interface»
CoreGenerators DistributionGenerators NumericStreamTrasformation

Exponential Poisson

LinearTranformation
Gaussian K-Erlang

BoundedNumericStream
Coxian

IntegerizedStream

10

jRand Numeric Stream
 Specialises ObjectStream with the semantics of
numbers
 Interface:
• getNextNumber(): Number
• goTo(Integer i): Void

 Properties a., b., and c. are now evaluated with
the “specialised” semantic:
a. Statistical tests
b. Real, Integer
c. Arithmetical operations (Linear combination, mod, etc.)
11

Example of Use
Core generator :
JavasimGenerator():
Y[i+1] = Y[i] * (55 mod 226);
Y[0] = 772531 (period 224)

Shuffled with a LCG

#1 NumericStream numericStream;
#2 CoreGenerator cg = new JavasimGenerator();
#3 numericStream = new Exponential(cg, lambda);

#4 Number n numericStream.getNextNumber();
12

Example of Use
jRand in Computer Network Simulation

WAN

(protocol functions of (protocol functions of
Host A Gateway 1 Gateway 2 OSI levels 2 and 1)
OSI levels 2 and 1)
get

Packet source LAN 1 LAN 2
...
(protocol functions Disassembler Fragmentor Transfer Reassembler Fragmentor
OSI levels 6 and 5) (protocol functions of
OSI levels 4 and 3)

Send/
Acknowledgment
through WAN Host B
(protocol functions of OSI
levels 6 and 5)

Packet Sink
Release (protocol functions of
OSI levels 4 and 3)

Ack Sink
...
Acknowledger Reassembler

Flow Control
(Pool of Tokens)

13

Preliminary Performance Assessment

14

Conclusions

 Designing input sequences for simulation studies is a
critical task because the quality and representativeness
of simulation results heavily depend on it
 Accurate results require systematic investigations in
multiple scenarios and diverse sequences
 The complete and easy control of the sequences is
fundamental to effectively carry out such studies
 We presented jRand, a flexible framework for the
representation of such sequences that can be modified
or substituted with very little effort
 Beside that, jRand also raises system developers from
the details of the input sequences and contributes to
ease the implementation of model logic
15

Producing simulation sequences using a Java-based generalized framework

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