Part 7: Network Simulation

1
Part 7 Network Simulation

• Overview
• fundamentals of discrete event simulation
• analyzing simulation outputs
• ns-2 simulation

• Motivation
• Learn fundamentals of evaluating network
  performance via simulation

2
What is simulation?
system boundary
system under study (has deterministic rules
governing its behavior)
exogenous inputs to system (the environment)
real life
observer
3
Why Simulation?

• goal study system performance, operation
• real-system not available, is complex/costly or
  dangerous (eg space simulations, flight
  simulations)
• quickly evaluate design alternatives (eg
  different system configurations)
• evaluate complex functions for which closed form
  formulas or numerical techniques not available

4
Simulation advantages/drawbacks

• advantages
• save lives, money
• find bugs (in design) in advance
• generality over analytic/numerical techniques
• detail can simulate system details at arbitrary
  level
• drawbacks
• caution does model reflect reality
• large scale systems lots of resources to
  simulate (especially acurrately simulate)
• may be slow (computationally expensive 1 min
  real time could be hours of simulated time)
• art determining right level of model complexity
• statistical uncertainty in results

5
The evaluation spectrum

• Numerical models
• Simulation
• Emulation
• Prototype
• Operational system

6
Programming a simulation

• What s in a simulation program?
• simulated time internal (to simulation program)
  variable that keeps track of simualted time
• system state variables maintained by
  simulation program define system state
• e.g., may track number (possibly order) of
  packets in queue, current value of retransmission
  timer
• events points in time when system changes state
• each event has associate event time
• e.g., arrival of packet to queue, departure from
  queue
• precisely at these points in time that simulation
  must take action (change state and may cause new
  future events)
• model for time between events (probabilistic)
  caused by external environment

7
Discrete Event Simulation

• simulation program maintains and updates list of
  future events event list
• simulator structure

initialize event list
get next (nearest future) event from event list

• Need
• well defined set of events
• for each event simulated system action, updating
  of event list

time event time
process event (change state values, add/delete
future events from event list
update statistics
n
done?
8
Simulation example

• processes arrive (avg. interrarrival time 1/ l)
  for execution (avg. execution time 1/m) on
  dual-processor system
• arriving customer joins CPU queue with smallest
  number of waiting processes

m1
l
m2

• state of system size of each queue
• system events
• job arrivals
• service time completions
• define performance measure to be gathered

9
Simulation example

• Simulator actions on arrival event
• if a queue empty place job in service (pick
  one of empty queues), determine its service time
  (a random number drawn from service time
  distribution) add future event onto event list
  for job completion, set number of jobs in queue
  to 1
• if both queues full increment dropped
  packets, ignore arrival
• else find which queue to place job in
  (shortest, with ties broken randomly), increment
  number in queue where queued
• create event for next arrival (generate
  interarrival time) stick event on event list

10
Simulation example

• Simulator actions on departure event
• Remove event, update simulation time, update
  performance statistics
• decrement counter of number of jobs in queue
• If (number of jobs in queue gt 0) put next job
  into service schedule a completion event
  (generate service time for job)

11
Simulation example

• Simulator initialization
• event list
• state variables

Code walkthrough
12
Gathering Performance Statistics

• avg delay at queue i record Dij delay of
  customer j at queue i. Let Ni be customers
  passing through queue i

• throughput at queue i, gi

• Average queue length at i

Littles Law
13
Analyzing Output Results

• Each time we run simulation, (using different
  random number streams), we will get different
  output results!

distribution of random numbers to be used during
simulation (interarrival, service times)



14
Analyzing Output Results

• W2,n delay of nth departing customer from queue
  2

15
Analyzing Output Results

• each run shows variation in customer delay
• one run different from next
• statistical characterization of delay must be
  made
• expected delay of nth customer
• behavior as n approaches infinity
• average of n customers

16
Transient Behavior

• simulation outputs that depend on initial
  condition (i.e., output value changes when
  initial conditions change) are called transient
  characteristics
• early part of simulation
• later part of simulation less dependent on
  initial conditions
• Example of transient characteristics?

17
Effect of initial conditions

• Histogram of delay of 20th customer, given
  initially empty (1000 runs)

• Histogram of delay of 20th customer, given
  non-empty conditions

18
Steady state behavior

• Output results may converge to limiting steady
  state value if simulation run long enough

avg delay of packets n, n10
avg of 5 simulations

• want to discard statistics gathered during
  transient phase, e.g., ignore first n0
  measurements of delay at queue 2

• Pick n0 so that statistic is approximately
• the same for different random number
• streams and remains same as n increases

19
Confidence Intervals

• run simulation get estimate V1 as estimate of
  performance metrics of interest
• repeat simulation M times (each with new set of
  random numbers), get V2, VM all different!
• which of V1, VM is right?

20
Confidence Intervals

• Can not get perfect estimate of true mean, m,
  with finite samples
• Look for bounds find c1 and c2 such that
• Probability(c1 lt m lt c2) 1 a
• c1,c2 confidence interval
• 100(1-a) confidence level
• One approach for finding c1, c2 (suppose a.1)
• take k samples (e.g., k independent simulation
  runs)
• sort
• find largest value is smallest 5 -gt c1
• find smallest value in largest 5 -gt c2

21
Confidence Intervals Central Limit Thm

• Central Limit Theorem If samples V1, VM
  independent and from same population with
  population mean m and standard deviation s, then

sample mean
is approximately normally distributed with mean u
and standard deviation
22
Confidence Intervals .. more

• Still dont know population standard deviation.
  So we estimate it using sample (observed)
  standard deviation

• Given we can now find upper and lower
  tails of normal distributions containing a100 of
  the mass

23
Confidence Intervals .. the recipe

• Given samples V1, VM, (e.g., having repeated
  simulation M times), compute

95 confidence interval
24
Interpretation of Confidence Interval

• If we calculate confidence intervals as in
  recipe, 95 of the confidence intervals thus
  computed will contain the true (unknown)
  population mean.

25
ns-2, the network simulator

• Our goal
• flavor of ns simple example, modification,
  execution and trace analysis

• discrete event simulator
• modeling network protocols
• wired, wireless, satellite
• TCP, UDP, multicast, unicast
• web, telnet, ftp
• ad hoc, sensor nets
• infrastructure stats, tracing, error models,
  etc.
• prepackaged protocols and modules, or create your
  own

26
ns components

• ns, the simulator itself (this is all well have
  time for)
• nam, the Network AniMator
• visualize ns (or other) output
• GUI input simple ns scenarios
• pre-processing
• traffic and topology generators
• post-processing
• simple trace analysis, often in Awk, Perl, or Tcl
• tutorial http//www.isi.edu/nsnam/ns/tutorial/ind
  ex.html
• ns by example http//nile.wpi.edu/NS/

27
ns Software Structure C and Otcl

• Uses two languages
• C for packet-processing
• fast to run, detailed, complete control
• OTcl for control our focus
• simulation setup, configuration, occasional
  actions
• fast to write and change

28
Basic ns-2

• Creating the event scheduler
• Creating network
• Computing routes
• Creating connection
• Creating traffic
• Inserting errors
• Tracing

29
Creating Event Scheduler

• Create scheduler
• set ns new Simulator
• Schedule event
• ns at lttimegt lteventgt
• lteventgt any legitimate ns/tcl commands
• Start scheduler
• ns run

30
Creating Network

• Nodes
• set n0 ns node
• set n1 ns node
• Links connect together two nodes
• ns duplex-link n0 n1 ltbandwidthgt ltdelaygt
  ltqueue_typegt
• ltqueue_typegt DropTail, RED, CBQ, FQ, SFQ, DRR

31
Inserting Errors

• Creating Error Module
• set loss_module new ErrorModel
• loss_module set rate_ 0.01
• loss_module unit pkt
• loss_module ranvar new RandomVariable/Uniform
• loss_module drop-target new Agent/Null
• Inserting Error Module
• ns lossmodel loss_module n0 n1

32
Computing routes

• Unicast
• ns rtproto lttypegt
• lttypegt Static, Session, DV, cost, multi-path
• Multicast
• ns multicast (right after new Simulator)
• ns mrtproto lttypegt
• lttypegt CtrMcast, DM, ST, BST

33
Traffic

• simple two layers transport and app
• transports
• TCP, UDP, etc.
• sources, sinks
• transport protocol instances attached to nodes
• applications (agents)
• ftp, telnet, etc.
• application instances attached to transport
  protocol instances

34
Creating Connection UDP

• source and sink
• set usrc new Agent/UDP
• set udst new Agent/NULL
• connect them to nodes, then each other
• ns attach-agent n0 usrc
• ns attach-agent n1 udst
• ns connect usrc udst

35
Creating Connection TCP

• source and sink
• set tsrc new Agent/TCP
• set tdst new Agent/TCPSink
• connect to nodes and each other
• ns attach-agent n0 tsrc
• ns attach-agent n1 tdst
• ns connect tsrc tdst

36
Creating Traffic On Top of TCP

• FTP
• set ftp new Application/FTP
• ftp attach-agent tsrc
• ns at lttimegt ftp start
• Telnet
• set telnet new Application/Telnet
• telnet attach-agent tsrc

37
Creating Traffic On Top of UDP

• CBR
• set src new Application/Traffic/CBR
• Exponential or Pareto on-off
• set src new Application/Traffic/Exponential
• set src new Application/Traffic/Pareto

38
Tracing

• Trace packets on all links into test.out
• ns trace-all open test.out w
• lteventgt lttimegt ltfromgt lttogt ltpktgt ltsizegt--ltflowidgt
  ltsrcgt ltdstgt ltseqnogt ltaseqnogt
• 1 0 2 cbr 210 ------- 0 0.0 3.1 0 0
• - 1 0 2 cbr 210 ------- 0 0.0 3.1 0 0
• r 1.00234 0 2 cbr 210 ------- 0 0.0 3.1 0 0

packet type
packet flags
seq number
time
packet ID

• enqueue
• dequeue
• r receive
• d drop

packet length
flow ID
source dest addresses
nodes involved in this event
39
Tracing

• trace packets on all links in nam-1 format
• ns namtrace-all open test.nam w
• post-process traces (e.g., using AWK) to
  determine performance

40
Walk-through example
ftp/tcp source
ftp/tcp dest
acks
CBR/udp source
CBR/udp source
1 Mbps full duplex 10 msec prop delay 5 packet
buffer