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Network simulation and simulators
Lecturer: Dmitri A. Moltchanov
E-mail: moltchancs.tut.Network simulation techniques D.Moltchanov, TUT, 2012
why do we need network simulation?
structure of a simulator
data collection and analysis
simulation with a given accuracy
variance reduction techniques
Practical aspects: ns2
support tools: nam and xgraph;
examples: bottleneck, routing and 802.11 WLANs.
Lecture: Network simulation 2Network simulation techniques D.Moltchanov, TUT, 2012
1. Why do we need network simulations?
Analyze communication networks: two ways
Example: analysis of queuing system:
modeling of arrival process as a stochastic process;
modeling of service time are a stochastic process;
representation of interactions of these processes as an another process.
What is important for analytical analysis:
often analytical analysis is too complicated or even not possible;
even if possible, it requires restrictive assumptions.
Lecture: Network simulation 3Network simulation techniques D.Moltchanov, TUT, 2012
1.1. Simple example
GI/G/1 queuing system.
arrivals unlimited of waiting positions server
Figure 1: Graphical representation of GI/G/1 queuing system.
System is characterized by:
arbitrary i.i.d. distributed interarrival times (GI);
arbitrary i.i.d. distributed service times (G);
FCFS queuing discipline.
Lecture: Network simulation 4Network simulation techniques D.Moltchanov, TUT, 2012
What should be noted:
there are no analytical solution for GI/G/1 queue;
what should we do?
Approximate solution can be derived for:
overloaded GI/G/1 system ();
unloaded GI/G/1 system ();
results in terms of bounds.
Notes on GI/G/1 queue:
GI/G/1 does not capture correlation between arrivals;
arrival processes in real networks are often correlated;
simulation is the only suitable way in this case.
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1.2. Simulations vs. analytic: queue example
Simulation of a queue consists of:
representation of the arrival process as a stochastic one;
representation of service time as a stochastic process;
representation of interoperation as a stochastic process;
collection of data;
statistical analysis of obtained data;
Dierences between analytical analysis and simulations:
incorporation of simulation execution;
incorporation of data collection and analysis.
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1.3. Advantages/shortcoming of simulations
does not require restrictive assumption or require less of them;
model structure, algorithms and variable may be changed quickly;
models of trac and service times can be used as is just from measurements;
may provide results which are not obtainable using analytical techniques.
time consuming, it may take much more time that analytic;
validation of results take additional time;
results may be inaccurate when time of analysis is not sucient;
simulations may be of higher complexity than required;
relationship between variables is hard to visualize and explain;
sensitivity analysis is dicult.
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2. Structure of a simulator
We have the following components:
simulation engine (core);
a collection of models (link, buer, etc.);
preprocessing tools (hidden for a user);
data postprocessing tools.
Engine is the most important:
species how to handle simulations;
everything is handled in uniform way;
does not matter what you simulate.
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2.1. Types of simulations
real time is used: time increments as ne as possible: to capture all state changes.
system is observed at discrete times t ;t + t;t + 2t;::: .
0 0 0
Discrete event simulations:
we focus on system changes only at event times;
after processing the current event, the system clock is forwarded to another event
simulation moves from the current system state to the event occurring next;
processing of the event may create additional events;
event list which is updated for the system.
Lecture: Network simulation 9Network simulation techniques D.Moltchanov, TUT, 2012
t t t
0 1 3
t t t t t t
0 1 2 3 4 5
t t t t t t t t
0 1 2 3 4 5 6 7
Figure 2: Illustration of dierent types of simulations.
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2.2. The simulation procedure
Define the problem Debug
Analyze data Validate model
Formulate submodels Design experiments
Combine submodels Run the simulator
Collect data Analyze the results
Write the program Implement results
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2.3. The simulation program
Simulation program can be implemented using:
general purpose programming languages:
C: suitable for one purpose simulations;
C++: suitable for writing multiple purposes simulations.
: there are no simulation specic functions and error detection.
simulation oriented programming languages:
OPNET: all networks;
OMNET++: all networks;
Qualnet: very good for cellular networks;
ns2: all networks:
+: reliable approach;
: sometimes not fast (incorporate a lot of functionality;)
: sometimes limited to specic functionality;
: one more language to learn.
Lecture: Network simulation 12Network simulation techniques D.Moltchanov, TUT, 2012
3. Discrete event simulation
The basic idea:
only events change the state of the system;
no need to track state of the system between events.
The whole system consists of the following components:
system under consideration:
process under considerations: product of other processes and RVs.
state of the stochastic process.
tells the occurrence time of the next event.
holder for events: consider it as a two dimensional vector: time and event.
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The idea of the event list is illustrated in the following gure:
T , i = 1; 2;::: , are event times;
E , i = 1; 2;::: , are corresponding events.
E E E E
1 2 3 i
T T T T
1 2 3 i
Figure 3: The idea of the event list.
Events are identied by event time and event type.
There are two general types of events:
these events modify the state of the system: arrivals/departures of customers.
these are events needed by additional tasks of simulation: run, stoppage, collection of data.
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system clock should be set to zero;
system state is assigned an initial value;
generate list of the event and place the next event to this list.
handling of events during a simulation:
system clock should be set to the occurrence time of the rst (next) event in event list;
handle the event making all appropriate actions associated with the event;
update the system state.
stop of simulation.
Note that the following is not included:
storing of statistical data;
statistical analysis of obtained data.
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3.1. Time advance methods
Time advance methods in discrete-event simulations:
t t t t t t
0 1 2 3 4 5
t t t
0 1 3
Figure 4: Time advance techniques in discrete-event simulations.
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3.2. Event list
Future event list: collection of events that occur in the future.
What type of operation are performed in a list:
locating the next event and its type:
required when advancing the time.
deleting the event:
required when the events has already been treated.
inserting the event in a list:
required when generating new event;
required when basic event generates conditional events.
Basically, there are two ways of organizing a list:
using sequential array;
using linked list.
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3.3. Sequential arrays
The approach: all future event times are stored sequentially in array.
How to implement:
associate each event type with a certain integer i;
clock associated with this event is stored in the ith position in array.
Example: we need N positions in array:
clock value of the type 1 event is stored in 1st position;
clock value of the type 2 event is stored in 2nd position;
clock value of the type N event is store in Nth position;
Figure 5: Using sequential array as a future event list.
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How to nd the next event:
locate the smallest value in array of N elements.
Example: nd the smallest element and its index in array Ei, i = 0; 1;:::;N 1:
variable smallest returns the smallest element;
variable index returns the index of the smallest element.
smallest = E0;
index = 0;
for (i=1; iN; i++)
if (Ei smallest)
smallest = Ei;
index = i;
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How to deal with other functions:
deletion: set the value of the clock associated with event of type i to very big one;
not deleted physically
insertion: update the value of the clock associated with event of type i.
not inserted physically
insertion and deletion are made very easily;
location of the next event depend on the number of event types:
complexity is linear in time.
if the number of event types is large location is time consuming;
in this case dierent organization of the future event list should be considered.
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