Network simulation and simulators. Lecturer: Dmitri A. Moltchanov

Transcription

1 Network simulation and simulators Lecturer: Dmitri A. Moltchanov

2 OUTLINE Theoretical aspects why do we need network simulation? structure of a simulator discrete-event simulation data collection and analysis simulation with a given accuracy variance reduction techniques Practical aspects: ns2 ns2 overview; ns2 architecture; simulation procedure; support tools: nam and xgraph; examples: bottleneck, routing and WLANs. Lecture: Network simulation 2

3 1. Why do we need network simulations? Analyze communication networks: two ways analytical analysis; simulation studies. 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 3

4 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); single server; FCFS queuing discipline. Lecture: Network simulation 4

5 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 (λ µ); unloaded GI/G/1 system (λ << µ); 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. Lecture: Network simulation 5

6 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; simulation run; collection of data; statistical analysis of obtained data; drawing conclusions. Differences between analytical analysis and simulations: incorporation of simulation execution; incorporation of data collection and analysis. Lecture: Network simulation 6

7 1.3. Advantages/shortcoming of simulations Advantages: does not require restrictive assumption or require less of them; model structure, algorithms and variable may be changed quickly; models of traffic and service times can be used as is just from measurements; may provide results which are not obtainable using analytical techniques. Shortcomings: time consuming, it may take much more time that analytic; computationally expensive; validation of results take additional time; results may be inaccurate when time of analysis is not sufficient; simulations may be of higher complexity than required; relationship between variables is hard to visualize and explain; sensitivity analysis is difficult. Lecture: Network simulation 7

8 2. Structure of a simulator We have the following components: simulation engine (core); a collection of models (link, buffer, etc.); frontend (interface); preprocessing tools (hidden for a user); data postprocessing tools. Engine is the most important: specifies how to handle simulations; everything is handled in uniform way; does not matter what you simulate. Lecture: Network simulation 8

9 2.1. Types of simulations Continuous simulations: real time is used: time increments as fine as possible: to capture all state changes. Discrete simulations: system is observed at discrete times t 0, t 0 + t, t t,.... 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 9

10 Discrete-event t 0 t 1 t 3 Discrete t 0 t 1 t 2 t 3 t 4 t 5 Continuous t 0 t 1 t 2 t 3 t 4 t 5 t 6 t 7... Figure 2: Illustration of different types of simulations. Lecture: Network simulation 10

11 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 Lecture: Network simulation 11

12 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. +: very flexible languages; : there are no simulation specific 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 specific functionality; : one more language to learn. Lecture: Network simulation 12

13 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. system state: state of the stochastic process. simulation clock: tells the occurrence time of the next event. event list: holder for events: consider it as a two dimensional vector: time and event. Lecture: Network simulation 13

14 The idea of the event list is illustrated in the following figure: T i, i = 1, 2,..., are event times; E i, i = 1, 2,..., are corresponding events. E 1 T 1 E 2 T 2 E 3 T 3... E i T i... Figure 3: The idea of the event list. Events are identified by event time and event type. There are two general types of events: basic events: these events modify the state of the system: arrivals/departures of customers. additional events: these are events needed by additional tasks of simulation: run, stoppage, collection of data. Lecture: Network simulation 14

15 General algorithm: initialization procedure: 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 first (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. Lecture: Network simulation 15

16 3.1. Time advance methods Time advance methods in discrete-event simulations: event-advance method; unit-advance method. unit-time advance: t 0 t 1 t 2 t 3 t 4 t 5 event advance: t 0 t 1 t 3 Figure 4: Time advance techniques in discrete-event simulations. Lecture: Network simulation 16

17 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. Lecture: Network simulation 17

18 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. Lecture: Network simulation 18

19 How to find the next event: locate the smallest value in array of N elements. Example: find the smallest element and its index in array E[i], i = 0, 1,..., N 1: variable smallest returns the smallest element; variable index returns the index of the smallest element. smallest = E[0]; index = 0; for (i=1; i<n; i++) { if (E[i] < smallest) { smallest = E[i]; index = i; } } Lecture: Network simulation 19

20 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! Advantages: insertion and deletion are made very easily; location of the next event depend on the number of event types: complexity is linear in time. Shortcoming: if the number of event types is large location is time consuming; in this case different organization of the future event list should be considered. Lecture: Network simulation 20

21 3.4. Linked list The idea: to access data elements in correct order we store data element; we store pointer to the next element. Definitions: pointer is referred to as link; data element and the link(s) are referred to as node. Example: node consists of two elements: one is data elements, the next one is link; link F points to the first element; link of the last node is set to zero. Figure 6: Using linked list as a future event list. Lecture: Network simulation 21

22 Implementation example: arrange integer numbers in T in ascending order: create new array P of the same dimension; the content of P (i) is the link to location in T containing element larger than T (i); example: P (1) = 7, next larger after T (1) is T (7)... Notes: F (= 5) is the first element; node: location in T and corresponding location in P. Figure 7: Locating values in a singly linked list. Lecture: Network simulation 22

23 Locating element: we want to locate number 10 in T : using pointer F we check the value stored in the first node: (T (5), P (5)); using pointer P (5) we locate the second node: (T (3), P (3)); using pointer P (3) we locate the third node: (T (1), P (1)); pointer P (1) contains the address we are looking for; general: if we know address of the first node we can locate any element. Note: we can only move forward using singly linked link! Figure 8: Deletion of elements in a singly linked list. Lecture: Network simulation 23

24 Deletion: we want to delete number 10: change the value of the pointer of the previous node; previous node: (T (1), P (1)) = (5, 4); node containing 10 has the pointer P (7) = 4; to delete: set P (1) = P (7) = 4. Note: information is not physically deleted, just not accessible! Figure 9: Deletion of elements in a singly linked list. Lecture: Network simulation 24

25 Insertion: we want to insert number 6: locate two nodes between which we should put 6; starting from the first node we find them as: (5, 7) and (10, 4) (see previous slide); get unused location in T : T (2); set P (1) = 2 to go from T (1) = 5 to T (2) = 6; set T (2) = 6, P (2) = 7 to go from T (2) = 6 to T (7) = 10. Figure 10: Insertion of elements in a singly linked list. Lecture: Network simulation 25

26 3.5. Implementation of linked lists We have to be able: organize data elements and pointers into nodes; access node using pointer; create and delete nodes. There are two ways: use built-in commands to carry these operations (if any); set-up your own storage scheme. Notes: must be long enough to accommodate all events that might be generated; all unused nodes must be linked together to form a pool of free nodes. Lecture: Network simulation 26

27 3.6. Event list using linked list General notes: nodes are organized such that CL i < CL j < < CL n ; the next event is given by pointer F (first node); when the event occurred and processed it should be deleted; if conditional events are generated they are place in linked list. Advantages: location and deletion is made easily: using linked lists in simulation we have to delete only the first node! Shortcomings we have to use location procedure to insert an event in a linked list; search of the linked lists might be time consuming if the number of event types is large; solution: use better location procedure (e.g. use pointer pointing to the middle of the list)! Lecture: Network simulation 27

28 3.7. Doubly linked lists The main problem of the singly linked lists: we can go only forward! Doubly linked lists: link two successive node with two pointers; we can go forward and backward! Note: there are some advantages for specific applications. Figure 11: Example of the doubly linked list. Lecture: Network simulation 28

29 4. Data collection How to carry out a simulation: set the initial state; start the simulation; obtain simulation results for some time... We distinguish between: transient simulations (results for warm-up period); steady-state simulation (results for steady-state period). N(t) warm-up steady-state T W T t Lecture: Network simulation 29

30 4.1. Transient simulations What is the reason: analyze problems associated with a specific initial state; T W is a function of the initial conditions; this might give some hints how to start a certain system... example: which initial state faster leads to steady-state. analyze transient state itself: a certain system may not have a steady-state at all; example: M/M/1 queuing system with λ/µ = ρ > 1. N(t), number of jobs M/M/1 0 1 lambda/mu Lecture: Network simulation 30

31 4.2. Steady-state simulations: initial condition What is the reason: typical application of simulations; simulation has to run long enough to get away from the transient state (T W ). Note: T W is a function of the initial conditions. How to choose initial condition: assume no activities in the system prior to time 0; no particular reasons for that; as good as any other choice of the initial state. set to some typical state of the system: should be as representative as possible; a-priori knowledge of the system is required; advantage: may significantly shorten the length of the transient period, T W. Lecture: Network simulation 31

32 4.3. Steady-state simulation: effect of the transient period Transient period: may deteriorate statistics: how to remove transient period; how to determine the length of the transient period. What are approaches to deal with: use very long simulation runs such that (meanwhile, how long is long enough?): the amount of data in transient period is small relative to those of steady-state; time-consuming approach. no data collection during transient period: simulate till the steady-state is reached; clear all data accumulated up to this point; continue to get enough statistical data. Hint for both approaches: set the initial state to what is expected in the long run. Lecture: Network simulation 32

33 4.4. Steady-state simulation: detecting transient period There are three methods to detect T W : method of trials; moving-average method; blind guess using time-series... Method of trials: try out different transient periods: T 1, T 2,..., T k, T 1 < T 2 <..., < T k ; compile steady-state statistics for each run; choose T i such that for all j > i statistics do not change significantly. Moving-average method: compute moving average as time progresses: R i = i j=i r N j; where N j is some metric of interest, r is the range of moving averages. steady-state when R i no longer changes significantly over time. Lecture: Network simulation 33

34 4.5. Estimating metrics How to estimate the mean: use point estimator; estimate confidence intervals. Confidence interval for the mean: { } s P r Ê[X] z α/2 µ Ê[X] + z s α/2 1 α, (1) N N Note: interval [Ê[X] z α/2 s N, Ê[X] + z α/2 s N ] is 100(1 α)% confidence interval for µ. a 2 a 2 Lecture: Network simulation 34

35 What is the problem with the following expression? { } s P r Ê[X] z α/2 µ Ê[X] + z s α/2 1 α, (2) N N we have to estimate sample mean and sample variance; point estimator of the variance is given by: ŝ 2 = 1 N 1 Ê[X] = 1 N N (X i Ê[X])2, X i, i = 1, 2,..., N must be independent for ŝ 2 to be unbiased! quite often observations are dependent (correlated, in particular): i=0 N X i, (3) i=0 example: waiting time of the packet i depends on the waiting time of packet (i 1)! The main problem! how to remove dependence between observations? Lecture: Network simulation 35

36 The following approaches have been proposed: use of autocorrelation function: tries to explicitly take correlation into account. batch means method; tries to remove correlation from observations. replication method; tries to remove correlation from observations. regenerative method. tries to remove correlation from observations. Note the following: when sample size is really large the effect of dependence can be negligible; use any of above methods when sample size is not really huge. Lecture: Network simulation 36

37 4.6. Use of ACF How to estimate the variance: idea: take into account the effect of correlation explicitly (σ 2 X+Y = σ2 X + σ2 Y + 2Cov XY ); get the sample X 1, X 2,..., X N ; compute autocorrelation coefficients R(i), i = 0, 1,..., k; the variance can now be estimated as: ŝ 2 = ŝ 2 X N/4 k=1 (1 kn ) R(k). (4) ŝ 2 X is the point estimator of the variance assuming no serial correlation: ŝ 2 X = 1 N 1 N (X i Ê[X])2. (5) i=1 Lecture: Network simulation 37

38 4.7. Batch means method N(t) correlation batch 1 batch 2 batch 3... batch k T W time Figure 12: Correlation between successive batches. Do the following: let Ê[X i] be the sample mean of the batch i, i = 1, 2,..., k; if b is large: Ê[X 1 ], Ê[X 2],..., Ê[X k] are approximately uncorrelated and normally distributed. Lecture: Network simulation 38

39 Given that Ê[X 1], Ê[X 2],..., Ê[X k] are uncorrelated and normally distributed: Ê[X] = 1 k ŝ 2 = 1 k 1 k Ê[X i ], i=1 k (Ê[X i] Ê[X])2. (6) i=1 Thus, for large k, k > 30 we get the following confidential intervals: ( Ê[X] 1.96 ŝ ) ŝ, Ê[X] (7) k k construct confidential intervals using t distribution. Important notes: if b is not large enough, this computation gives biased estimates; estimate of b: plot ACF of X 1, X 2,..., X N : b should be approximately 5 times greater than the last significant correlation. Lecture: Network simulation 39

40 4.8. Method of replications The idea: replicate simulation run several times: make n runs each resulting in m observations: (X 11, X 12,..., X 1m ), (X 21, X 22,..., X 2m ),..., (X n1, X n2,..., X nm ). (8) for each sample compute the sample mean using point estimator of the mean: Ê[X i ] = 1 m n x (9) treat sample means as a sample of independent observations: j=1 Ê[X] = 1 n n i=1 Ê[X i ], ŝ 2 = 1 n 1 n (Ê[X i] Ê[X])2. (10) i=1 Classic problems: determine the length of each simulation run; determine the length of the transient period. Lecture: Network simulation 40

41 4.9. Regenerative method Comparison of methods: batch means: obtain approximately independent samples from a single run. method of replications: obtain strictly independent samples from multiples runs; regenerative method: obtain strictly independent samples from a single run; application is limited to systems with specific behavior. Where it is used: simulation of queue-related problems. Lecture: Network simulation 41

42 4.10. Estimation for transient simulations Two cases are possible: trajectories are all random: estimation may have no sense; trajectories are somewhat similar. How to estimate: the transient state depends on initial conditions; the only way to get independent realizations of X is to use method of replications; Repeating experiments: must start with the same initial condition; must use different random numbers (different seeds); we have to know the length of transient period. Lecture: Network simulation 42

43 5. Simulation with a given accuracy Two inverse tasks: we considered: what are the confidence intervals given N observation: is it OK to say that mean is 50 ± 30? what we are asked: provide a kind of assurance: say what would be the values with confidence ±3... question: how many experiments are needed to achieve that? General notes: recall, width of confidence intervals is proportional to 1/ N; ( ) ŝ Ê[X] z α/2, Ê[X] + z ŝ α/2. (11) N N N: number of iid observations; the larger N the smaller is the interval. to halve the confidence interval: increase N four times. Lecture: Network simulation 43

44 Observe that: we do not know how many observations are needed: what accuracy may N experiments give? e.g. how many observations are needed to get 50 ± 3? Two techniques: Solution 1: use of pilot experiments: aim 1: to get overall idea how things go; aim 2: obtain rough estimate of N providing required accuracy. Solution 2: sequential in-simulation checking: test is carried out periodically to check whether required accuracy is achieved. Can be applied to: batch mean method; method of replications. Lecture: Network simulation 44

45 5.1. Example: replications + pilot experiments Use of pilot experiments: we want to estimate statistics a with the intervals ±0.1â: pilot experiments is carried out to collect N 1 replications; let â 1 be a point estimator of a; let 1 be the width of confidence intervals; check the following: if 1 0.1â 1 we stop; if 1 > 0.1â 1 we carry out main simulation with N 2 = ( 1 /0.1â 1 ) 2 N 1 replications; we obtain new â 2 and 2 : they may not be exactly what we wanted (0.1â 2 ) due to randomness. if 1 > 0.1â 1 carry out new attempt. Lecture: Network simulation 45

46 5.2. Example: batch means + in-simulation checking Sequential in-simulations checking: we want to estimate statistics a with the intervals ±0.1â: gather k batches; estimate â 1 and 1 ; check the following: if 1 0.1â 1 we stop; if 1 > 0.1â 1 gather k additional batches. calculate new â 2 and 2 based on total 2k batches; check the following: if 2 0.1â 2 we stop; if 2 > 0.1â 2 gather k additional batches. repeat... Lecture: Network simulation 46

47 6. Variance reduction techniques Suppose we have to estimate mean given N iid observations: point estimate of the mean is: Ê[X] = 1 N N X i, (12) i=1 confidence intervals for the mean are given by: ( ) ŝ Ê[X] z α/2, Ê[X] + z ŝ α/2. (13) N N where ŝ 2 is the estimate of the variance. How to shorten the confidence intervals: accuracy of an estimate can be increased by increasing the number of observations; shortcoming: may require very long simulations. accuracy can also be achieved by reducing variance. Lecture: Network simulation 47

48 Lecture: Network simulation 48

49 7. Network simulators Free simulators ns2/ns3: OMNET++: QualNet: ITGuru: Commercial: OPNET: a plenty of others... Note: ITGuru is an academic edition of OPNET. Lecture: Network simulation 49

50 8. ns2 Overview Characteristics: discrete-event engine; network simulator; basically TCP/IP networks; wired and wireless components included. Intended usage: research; development; education. Developing: research institutes and universities; freely distributed and open source. Lecture: Network simulation 50

51 8.1. History of developing Briefly: 1989: REAL simulator by UCB; 1990: ns1: LBL; 1995: ns2 DARPA VINT project (Virtual InterNet Testbed) 2011: ns3. Current status: ns2 runs on: almost all UNIX and Linux and Win 95/98/2000/XP. last stable version: 2.35 released in Nov. 4, 2011 roughly 1 release in 6 months + daily snapshots. or Page > line of codes; > 1000 institutions; > users; > 400 pages of short manual; ns3 project started in July 2006, Lecture: Network simulation 51

52 8.2. Components ns2 distribution consists of: ns2 itself; nam: Network AniMator visualizing ns2 output; GUI for simple scenarios. Mandatory support tools: tcl/tk: script language otcl: object-oriented tcl; TclCL: tcl library. Pre-processing tools: traffic, topology generators, converters. Post-processing tools: trace analysis: awk, sed, perl, tcl. very simple and not recommended. Lecture: Network simulation 52

53 8.3. Installation Differs for: Unix/Linux; Windows. Two types are available: Via compilation; get all-in-one package ; get pieces and then compile. Obtaining binaries. easiest way to run on Win platform. Hints and notes: to compile on Windows you need Cygwin (Unix emulation); Size of all-in-one is around 320Mb (v2.29). Lecture: Network simulation 53

54 Building from pieces (Unix/Linux): get Otcl, TclCL and ns2; unpack in some temp folder at the same level; cd into the OTcl directory; run./configure; run make; cd into the TclCL directory; run./configure; run make; cd into the ns directory; run./configure; run make; Verify that it built correctly and runs by running./validate. Note: ns2 all-in-one contains install script (just run it). Lecture: Network simulation 54

55 8.4. Support Documentation: mailing list: to get in send message to the address with subscribe ns-users in the body; to browse archive go to ns manual: available at is only a short reference guide. tutorials: a number is available on FAQ: de-facto tutorial: installation and bug fixes: E. Altman, T. Jimenez: Lecture: Network simulation 55

56 9. Ns 2 architecture Object-oriented structure: advantage: code reuse; shortcomings: performance. Software structure: uses two languages to separate control and processing: C++: packet processing; Otcl: control. Packet processing: C++ makes it fast and detailed; C++ makes it scalable. Control: Otcl makes it easy to create scenarios; Otcl makes it easy to understand third party scripts. Lecture: Network simulation 56

57 9.1. Scalability and extensibility Scalability: per-packet processing must be fast; separating control and packet handling. Extensibility: must be easy to add new objects; object trees to understand hierarchy: in C++; in Otcl. C++ and Otcl trees are split: if not needed nothing have to be changed at a certain level; helps a lot! Otcl class hierarchy: C++ class hierarchy: Lecture: Network simulation 57

58 9.2. Simple script Lecture: Network simulation 58

59 9.3. Tcl basics Tcl language: semantics is similar to perl; one can check tutorial at: real programming language to create network topology; tcl is used by Otcl to construct advanced objects. Tcl contains: lists, arrays, associative arrays etc.; procedures and functions; flow controls: if, while, for, etc. Lecture: Network simulation 59

60 Lecture: Network simulation 60

61 9.4. Otcl basics codes can be reused (for example, different version of TCP). Lecture: Network simulation 61

62 9.5. Viewing source code Where to find: C++: /whereyouinstalled/ns-(ver)/ where (ver) is your version of ns2 (e.g. 2.1b9a). Otcl: /whereyouinstalled/ns-(ver)/tcl/lib/ ns-default.tcl: default values for ns2 objects; other Otcl definitions. /whereyouinstalled/ns-(ver)/tcl/ specialized objects in subdirs. Lecture: Network simulation 62

63 10. Simulation procedure Basic steps: Create the simulation environment; event list, scheduler, etc.; Create the network: nodes and links between them. Create connections: TCP, UDP (in some sense). Create applications: CBR flow, FTP, WWW traffic. Trace network elements: trace queue, trace flow. Lecture: Network simulation 63

64 10.1. Creating environment The following are the common commands: Create event scheduler: set ns [new Simulator]; Schedule events: $ns at <time> <event>; Start scheduler: $ns run. Stop and close everything: $ns at $x exit. where $x is some instant of time. Note: before closing you must take additional actions: flush all traces to files; close all files. Lecture: Network simulation 64

65 10.2. Generating RVs Create new generator: set rng [new RNG]; $rng seed 0. RNs from other distributions: using class RNG: uniform: $rng uniform a b. using class RandomVariable: distributions: uniform, exponential, hyperexponential, Pareto; Lecture: Network simulation 65

66 10.3. Creating the network Nodes: set n0 [$ns node]. Links and queuing: $ns duplex-link $n0 $n1 <bandwidth> <delay> <queue type>; <queue type>: DropTail, RED, CBQ, FQ, SFQ, DRR; example: link with 10 Mbps, 10 ms delay, buffer size 100, RED buffer control Lecture: Network simulation 66

67 10.4. Creating connections Creating UDP flow In one command: $ns create-connection <src type> <src node> <dst type> <dst node> <packet class>; example: $ns create-connection UDP $n0 Null $n1 1. Other sources: TCP (we will consider); RTP source, RTCP source. TCP for wireless links. Lecture: Network simulation 67

68 Creating TCP flow In one command: $ns create-connection <src type> <src node> <dst type> <dst node> <packet class>; example: $ns create-connection TCP $n0 TCPSink $n1 1. Some included TCP versions: TCP: Tahoe TCP (slow start and AIMD); TCP/Reno: Reno TCP (... + fast retransmit/fast recovery); TCP/NewReno: TCP Reno with improved fast retransmit; TCP/Sack: TCP SACK (Selective ACK). Lecture: Network simulation 68

69 10.5. Creating traffic on top of UDP CBR: Constant bit rate; set src [new Application/Traffic/CBR]. Exponential or Pareto ON/OFF: on/off times are exponentially distributed; set src [new Application/Traffic/Exponential]; set src [new Application/Traffic/Pareto]. Connecting to transport: $udp defined earlier; $src attach-agent $udp. Lecture: Network simulation 69

70 ns2 includes support for traffic traces: What <file> should look like: each record consist of two 32 bit field; inter-packet time (msec) and packet size (in bytes). For example: there is converter for MPEG frame size file to ns2. Lecture: Network simulation 70

71 10.6. Creating traffic on top of TCP FTP: set ftp [new Application/FTP]; attaching to TCP: $ftp attach-agent $tcp. Telnet: set telnet [new Application/Telnet]; attaching to TCP: $telnet attach-agent $tcp. Lecture: Network simulation 71

72 10.7. Starting/stopping traffic sources Starting and stopping times scheduled as events to the scheduler: $ns at <time> <event>. Starting: $ns at 1.0 $ftp start ; sends infinitely long; the same for CBR, telnet and on/off sources. Stopping: $ns at 5.0 $ftp stop ; the same for CBR, telnet and on/off sources. Sending for example 1000 packets: $ns at 7.0 $ftp produce 1000 ; works for FTP only. Lecture: Network simulation 72

73 10.8. Tracing Trace packets on all links of the network: $ns trace-all [open test.out w]. Tracing on specific links: $ns trace-queue $n0 $n1. Lecture: Network simulation 73

74 10.9. Monitoring Queue monitor: set qmon [$ns monitor-queue $n0 $n1]; for packet arrivals and drops: set parr [$qmon set parrivals ]; set drops [$qmon set pdrops ]. Flow monitor: enable flow monitoring: set fmon [$nssim makeflowmon Fid]; $nssim attach-fmon [$nssim link $n0 $n1] $fmon. count arrivals and drops for flow with id xx: set fclassifier [$fmon classifier]; set flow1 [$fclassifier lookup auto 0 0 xx]; set parr [$flow1 set parrivals ]; set pdrops [$flow1 set pdrops ]. Lecture: Network simulation 74

75 Generic methodology Lecture: Network simulation 75

76 Other functionalities ns2 also provides: errors on the data-link layer; LAN and WLAN; Routing; Multicasting; Mobile IP; DiffServ; MPLS;... Lecture: Network simulation 76

77 11. Support tools: nam and xgraph Nam (Network AniMator): packet-level animation; almost integrated with ns2. controls node link packet time Lecture: Network simulation 77

78 Lecture: Network simulation 78

79 Xgraph allows to make graphs: frequently used with ns2; simple visualizer. Lecture: Network simulation 79

80 12. Example: analyzing a bottleneck Environment is the same! # Create a simulator object set ns [new Simulator] # Open the nam trace file set nf [open out.nam w] $ns namtrace-all $nf # Define a 'finish' procedure proc finish {} { global ns nf $ns flush-trace close $nf exec nam out.nam & exit 0 } BODY # Call the finish procedure after 5 seconds simulation time $ns at 5.0 "finish" # Run the simulation $ns run Lecture: Network simulation 80

81 Let s create four nodes: set n0 [$ns node] set n1 [$ns node] set n2 [$ns node] set n3 [$ns node] Duplex links between them with droptail queuing: $ns duplex-link $n0 $n2 1Mb 10ms DropTail $ns duplex-link $n1 $n2 1Mb 10ms DropTail $ns duplex-link $n3 $n2 1Mb 10ms DropTail Re-layout topology for good-looking in nam: $ns duplex-link-op $n0 $n2 orient right-down $ns duplex-link-op $n1 $n2 orient right-up $ns duplex-link-op $n2 $n3 orient right Lecture: Network simulation 81

82 What we get in nam: What is next: two UDP agents with CBR sources at n0 and n1; null agent at node n3. Lecture: Network simulation 82

83 We use these commands: #Create a UDP agent and attach it to node n0 set udp0 [new Agent/UDP] $ns attach-agent $n0 $udp0 # Create a CBR traffic source and attach it to udp0 set cbr0 [new Application/Traffic/CBR] $cbr0 set packetsize_ 500 $cbr0 set interval_ $cbr0 attach-agent $udp0 #Create a UDP agent and attach it to node n1 set udp1 [new Agent/UDP] $ns attach-agent $n1 $udp1 # Create a CBR traffic source and attach it to udp1 set cbr1 [new Application/Traffic/CBR] $cbr1 set packetsize_ 500 $cbr1 set interval_ $cbr1 attach-agent $udp1 # Create a Null agent and attach it to node n3 set null0 [new Agent/Null] $ns attach-agent $n3 $nul Lecture: Network simulation 83

84 Connect two CBR agents to the Null agent: $ns connect $udp0 $null0 $ns connect $udp1 $null0... Schedule their sending times: $ns at 0.5 "$cbr0 start" $ns at 1.0 "$cbr1 start" $ns at 4.0 "$cbr1 stop" $ns at 4.5 "$cbr0 stop" Classic bottleneck scenario: both sources send 200 pps with size 500 bytes; n0 to n2: 0.8 Mbps, n1 to n2: 0.8 Mbps; link between n2 and n3 is 1 Mbps. Questions: which packets get lost? how many? what is the effect of queuing? Lecture: Network simulation 84

85 12.1. Marking flows Set class to UDP sources first: $udp0 set class_ 1 $udp1 set class_ 2 Set colors to flows: $ns color 1 Blue $ns color 2 Red Lecture: Network simulation 85

86 12.2. Monitoring a queue Add queue monitor: $ns duplex-link-op $n2 $n3 queuepos 0.5 Everything is droptail on that queue! Lecture: Network simulation 86

87 Why not to add some fairness with stochastic fair queuing (SFQ): $ns duplex-link $n3 $n2 1Mb 10ms SFQ Everything is SFQ on that queue! Example: Lecture: Network simulation 87

88 13. Routing Create 8 nodes: for {set i 0} {$i < 7} {incr i} { set n($i) [$ns node] } Create circular topology: for {set i 0} {$i < 7} {incr i} { $ns duplex-link $n($i) $n([expr ($i+1)%7]) 1Mb 10ms DropTail } Lecture: Network simulation 88

89 Lecture: Network simulation 89

90 Send some data from n0 to n3: #Create a UDP agent and attach it to node n(0) set udp0 [new Agent/UDP] $ns attach-agent $n(0) $udp0 # Create a CBR traffic source and attach it to udp0 set cbr0 [new Application/Traffic/CBR] $cbr0 set packetsize_ 500 $cbr0 set interval_ $cbr0 attach-agent $udp0 set null0 [new Agent/Null] $ns attach-agent $n(3) $null0 $ns connect $udp0 $null0 $ns at 0.5 "$cbr0 start" $ns at 4.5 "$cbr0 stop" By default traffic goes over shortest path n0-n1-n2-n3! Lecture: Network simulation 90

91 Introducing link failure: $ns rtmodel-at 1.0 down $n(1) $n(2) $ns rtmodel-at 2.0 up $n(1) $n(2) Lecture: Network simulation 91

92 Introduce destination vector (DV) routing to tackle the problem: $ns rtproto DV. Example: Lecture: Network simulation 92

93 14. Wireless simulations in ns2 What we consider: simple 2-nodes wireless scenario; nodes move within the area 500m 500m; nodes start out initially at two opposite ends of the boundary; nodes move towards each other and then move away; TCP connection is setup between the two nodes; packets are exchanged as nodes come within hearing range of each other; as nodes move away, packets start getting dropped. We start with simple template: just as usual - some must do things; Lecture: Network simulation 93

94 Array val() is used to set basic parameters: link layer (LL); interface queue (IfQ); MAC layer; wireless channel nodes transmit and receive signals from... # ====================================================================== # Define options # ====================================================================== set val(chan) Channel/WirelessChannel ;# channel type set val(prop) Propagation/TwoRayGround ;# radio-propagation model set val(ant) Antenna/OmniAntenna ;# Antenna type set val(ll) LL ;# Link layer type set val(ifq) Queue/DropTail/PriQueue ;# Interface queue type set val(ifqlen) 50 ;# max packet in ifq set val(netif) Phy/WirelessPhy ;# network interface type set val(mac) Mac/802_11 ;# MAC type set val(rp) DSDV ;# ad-hoc routing protocol set val(nn) 2 ;# number of mobile nodes Lecture: Network simulation 94

95 Create an instance of the simulator: set ns_ [new Simulator] Setup trace supportand call the procedure trace-all : set tracefd [open simple.tr w] $ns_ trace-all $tracefd Create a topology object that keeps track of movements: set topo [new Topography] Provide the topography object with x and y coordinates: $topo load_flatgrid Next we create the object God (general operations director), as follows: create-god $val(n keeps track of wireless environment. Lecture: Network simulation 95

96 We have to configure nodes before creating them providing e.g.: type of adressing (flat or hierarchial or...); routing protocol, LL, IfQ, MAC, etc. # $ns_ node-config -addressingtype flat or hierarchical or expanded # -adhocrouting DSDV or DSR or TORA # -lltype LL # -mactype Mac/802_11 # -proptype "Propagation/TwoRayGround" # -ifqtype "Queue/DropTail/PriQueue" # -ifqlen 50 # -phytype "Phy/WirelessPhy" # -anttype "Antenna/OmniAntenna" # -channeltype "Channel/WirelessChannel" # -topoinstance $topo # -energymodel "EnergyModel" # -initialenergy (in Joules) # -rxpower (in W) # -txpower (in W) # -agenttrace ON or OFF # -routertrace ON or OFF # -mactrace ON or OFF # -movementtrace ON or OFF Lecture: Network simulation 96

97 We use the following configuration: # Configure nodes $ns_ node-config -adhocrouting $val(rp) \ -lltype $val(ll) \ -mactype $val(mac) \ -ifqtype $val(ifq) \ -ifqlen $val(ifqlen) \ -anttype $val(ant) \ -proptype $val(prop) \ -phytype $val(netif) \ -topoinstance $topo \ -channeltype $val(chan) \ -agenttrace ON \ -routertrace ON \ -mactrace OFF \ -movementtrace OFF Then we create two nodes disabling random movement: for {set i 0} {$i < $val(nn) } {incr i} { set node_($i) [$ns_ node ] $node_($i) random-motion 0 } ;# disable random motion Lecture: Network simulation 97

98 Give initial positions of nodes: # Provide initial (X,Y, for now Z=0) co-ordinates for node_(0) and node_(1) # $node_(0) set X_ 5.0 $node_(0) set Y_ 2.0 $node_(0) set Z_ 0.0 $node_(1) set X_ $node_(1) set Y_ $node_(1) set Z_ 0.0 Setting movement of nodes: # # Node_(1) starts to move towards node_(0) $ns_ at 50.0 "$node_(1) setdest " $ns_ at 10.0 "$node_(0) setdest " # Node_(1) then starts to move away from node_(0) $ns_ at "$node_(1) setdest " ns at 50.0 node (1) setdest : at 50.0s, it moves to (x=25,y=20) at 15m/s. Lecture: Network simulation 98

99 Setup traffic flow between nodes: set tcp [new Agent/TCP] $tcp set class_ 2 set sink [new Agent/TCPSink] $ns_ attach-agent $node_(0) $tcp $ns_ attach-agent $node_(1) $sink $ns_ connect $tcp $sink set ftp [new Application/FTP] $ftp attach-agent $tcp $ns_ at 10.0 "$ftp start" Setting up time when simulation ends and reset to initial states: for {set i 0} {$i < $val(nn) } {incr i} { $ns_ at "$node_($i) reset"; } $ns_ at "stop" $ns_ at "puts \"NS EXITING...\" ; $ns_ halt" proc stop {} { global ns_ tracefd close $tracefd } Lecture: Network simulation 99

100 Example: What actually happens: TCP flow starting at 10.0s from node0; no packets are exchanged as nodes too far apart; around 81.0s the routing info begins to be exchanged between both the nodes; around 100.0s we see the first TCP packet being received by node1; the connection breaks down again around time 116.0s. Note the following: all wireless traces starts with WL in their first field; we enabled DSDV so we have routing messages. Lecture: Network simulation 100