Simulation of ATM Switches

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Telecommunication Systems 14 (2000) 291309 291

Cell-level/call-level ATM switch simulator

Jeong Won Heo a, Sung Hyuk Byun a, Ju Yong Lee a,

Dan Keun Sung a and Soo Jong Lee b

a Department of Electrical Engineering, KAIST, Taejon 305-701, Korea

E-mail: [email protected] Department of Quality Assurance, ETRI, Taejon 305-350, Korea

A B-ISDN national project in Korea has been carried out to develop a National Infor-

mation Superhighway since 1992. An ATM switching system has been developed as one of

the most important parts in the project, and has been tested in the National Information Su-perhighway testbed. In this paper, we develop a cell-level/call-level ATM switch simulator

using cell-level and call-level input traffic models for evaluating the ATM switching system.

The cell-level simulator models various cell-level switching functions such as priority con-

trol and multicast, and evaluates the cell-level performance indices of the ATM switch in

terms of cell delay, throughput, and cell loss probability. On the other hand, the call-level

simulator uses call-level traffic models and evaluates the call blocking rate as a call-level

quality of service (QoS).

1. Introduction

A B-ISDN national project has been carried out to develop an ATM switch-

ing system, broadband network terminations (B-NT), ATM terminal equipments,10 Gbps/100 Gbps optical transmission systems, etc. since 1992. The ATM switching

system has been developed as a virtual channel (VC)/virtual path (VP) switch, and

has been tested in the National Information Superhighway testbed. An ATM switch

simulator also has been developed to evaluate cell-level/call-level performances as well

as traffic control schemes.

The performance of ATM switching systems can be evaluated through direct

measurements of real systems, analytical models [3,6,911,14,15] and simulations

[5,8,20]. Although direct measurements of real ATM switching systems are accu-

rate, they are availiable only after the implementation of real systems. Even though

analytical models may represent only simple ATM switches, they have limitations

in representing more complex ATM switches such as shared-buffer ATM switchesand in dealing with complex input traffic sources. Simulation models can represent

complex ATM switches in detail, including various input traffic, detailed switch el-

ements, and traffic control schemes. However, they may take a long time to collect

data.

This study was supported in part by the Electronics and Telecommunications Research Institute, Korea.

J.C. Baltzer AG, Science Publishers


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J.W. Heo et al. / Cell-level/call-level ATM switch simulator 293

2. ATM switch architecture

2.1. ATM switching system

Figure 1 illustrates an ATM switching system. It consists of ATM local switching

subsystem (ALS) and ATM central switching subsystem (ACS) for interconnections

between ALSs. Each ALS has Subscriber Interface Modules (SIM), Link Interface

Modules (LIM), a Subscriber Call Processor (SCP), and an Access Switch Network

Module (ASNM). The ACS contains LIM, an Operation and Maintenance Processor

(OMP), an Interconnection Switch Network Module (ISNM). ASNM and ISNM consist

of shared-buffer type switch elements in ALS and ACS, respectively. If an incoming

cell in an ALS is destined to the output port of another ALS, then it is switched through

the ACS to its corresponding output. If incoming cells are to be multicast, they are

copied by a cell-splitting copy mechanism.

2.2. Switch element

Figure 2 shows a switch element of the ATM switching system. The operation of

the switch element is as follows. After incoming cells are converted into parallel bit

data for reducing the internal speed of the switch element, the cells are sequentially

multiplexed in time by a multiplexer (MUX). Each cell is stored in the shared buffer

Figure 1. ATM switching system.


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294 J.W. Heo et al. / Cell-level/call-level ATM switch simulator

Figure 2. Switch element of shared buffer architecture.

memory (SBM), and its header is sent to the priority control and routing block. The

priority control and routing block writes the SBM address of the incoming cell in

address first-in-first-out (AFIFO) buffer. An idle address for each incoming cell is

provided by the idle address pool (IAP) which holds all the empty addresses of the

SBM. The addresses stored in AFIFO buffers are used to read out cells from the SBM,

and these cells are sent to their corresponding output ports through a demultiplexer

(DMUX) and a parallel/serial converter.

The switch element of the ATM switching system employs a partial buffer-sharing

scheme, which is a buffer allocation approach to achieve as much buffer sharing as

possible while maintaining a degree of fairness. Since the scheme uses the finite size

of AFIFO, it limits the maximum number of cells destined for each output port.

There are several types of memory access control schemes for shared buffer

switches: a linked-list, a content addressable memory (CAM) based, and an FIFO-

queue scheme. In the linked-list and the CAM-based control mechanisms, since multi-

cast cells must be separately handled from unicast cells, a multicast queue for multicast

cells is newly added. In addition, a controller is needed to arbitrate between unicast

and multicast queues at the read-out stage. Since these mechanisms are read once,

send to all output ports at one time and only one multicast cell is processed in a single

time slot, the total throughput of multicast channels is limited. As the ratio of multi-

cast calls increases, the throughput of switching system decreases. On the other hand,the FIFO-queue approach handles unicast and multicast cells equally. The addresses

of the multicast cells are copied before enqueueing. Thus, there is no degradation in

throughput with increasing multicast cell ratio [21]. The switch element considered

in this paper employs this FIFO-queue approach for controlling the address of shared

buffer.


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Figure 3. Example of multipath ATM switch (L = 2, k = 3).

2.3. Routing algorithm

Figure 3 shows an example of multipath ATM switch. Link group is a bundle

of links between an ALS and an ACS. If one link group consists of k links and thenumber of link group is L, then each ALS has kL links. The routing algorithm utilizedin the switching system is as follows:

1. If a new incoming call in an ALS is destined to the output port of another ALS,

then a subscriber call processor in the ALS select a candidate link among eachlink groups. The link with the largest available bandwidth in each link group is

selected as a candidate link.

2. The bandwidth of all candidate links is reserved by the required bandwidth in order

to prevent another new call from occupying the bandwidth before the completion

of this call.

3. The identification number of the candidate links is sent to a destination ALS.

4. The destination ALS checks usable links in sequence of available bandwidth. If a

certain link of the destination ALS is available and there is a link candidate which

belongs to the same group, these two links are selected as a connection path of

the incoming call.

5. The bandwidth of the selected link in the destination ALS is reserved by the

required bandwidth of the incoming call. The processor of the destination ALS

sends the identification number of the remaining candidate links.

6. The bandwidth of the remaining candidate links is released.


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3. Input traffic models

3.1. Cell-level input traffic models

Simulation time is divided into cell time slots in the system which operates

synchronously. It is assumed that the traffic of an input port is a multiplexed stream

of many virtual channels (VCs). The cell-level simulator supports various input traffic

models including random or bursty traffic, multicast traffic, uniform or hot-spot traffic,

and prioritized or nonprioritized traffic.

3.1.1. Random or bursty traffic

Random traffic. Cell arrivals at each input port are generated according to a Ber-

noulli process with parameter , 0 1, where is the offered load per eachinput port. The probability that x cells arrive during y time slots is given by

Py(x) =

y

x

x(1 )yx.

Bursty traffic. The offered traffic on each input port is modeled by an Interrupted

Bernoulli Process (IBP) which is a discrete version of Interrupted Poisson Process

(IPP). The state of current time slot is either active or idle at each time slot. When

the current state is active, the state of the next time slot is still active with probability

p, and is changed into the idle state with probability 1p. When the state is idle,the next state is idle with probability q and is changed into the active state withprobability 1 q. The length of active state, X, and the length of idle state, Y,have geometric distributions [16]:

P{X= x}= (1p)px1,

P{Y = y}= (1 q)qy1.

The average ofXand Y are 1/(1p) and 1/(1 q), respectively. If cell generationrate in the active state is , then the average interval of generating cells is writtenas

E[interval of generating cells] =2 p q

(1 q).

Then, p and q can be obtained from the following input parameters: the input traffic

load , the average burst length E[X], and the cell generation rate :

p= 1 1

E[length of active state]= 1

1

E[X],

q=2p

.


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3.1.2. Priority classes

The cell-level simulator supports cell loss priority (CLP) control, in which buffers

store only high priority cells when the queue length exceeds the specified threshold

value. The threshold value and the ratio of high priority cells among all incoming

cells are set through a graphic user interface (GUI) input unit before simulations.

3.1.3. Balanced or unbalanced traffic in output ports

Uniform distribution. Let qij denote the probability that a cell from input port i hasits destination output port j. IfN is the switch size, and output port distributionis uniform, then qij = 1/N and all incoming cells are uniformly distributed to alloutput ports.

Hot-spot distribution. Cells from input port i are transferred to a hot-spot output portwith probability h, and the remaining traffic is assumed to be uniformly distributedto all output ports[2]. The transition probability qij is written as

qij =

h+

1 h

N, j =jH,

1 h

N, j =jH,

where h is the hot-spot ratio and jH is the hot-spot output port.

3.1.4. Multicast model

The cell-level simulator provides two types of fanout models for multicast traffic:

a constant fanout model and a truncated geometric distribution (TGM) model. Let Fdenote the fanout of a multicast cell. In the constant fanout model, an original cell is

copied into a constant number of cells, c (2 c N), namely, F= c for all multicastcells. In the truncated geometric distribution model, an original cell is copied into

k cells with the following truncated geometric distribution:

P{F= k} =(1 q)qk2

1 qN1, 2 k N,

where N is the number of output ports. The mean fanout of TGM model is given by

E[F] =1

1 q+

1NqN1

1 qN1.

3.2. Call-level input traffic models

Using cell-level input traffic models described in section 3.1, various cell-level

performances can be evaluated in terms of delay, throughput, and cell loss probability.

However, this cell-level simulator does not provide performance results related to

calls. Thus, a call-level simulation is newly needed in order to evaluate proper routing

algorithms in multipath switches.


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Figure 4. Simple two-state call model.

New input traffic models are proposed here for call-level simulations. Since it is

very difficult to examine the performance of switching system by considering all cells

in switch elements, a call is regarded as an occupation of some link bandwidth in stead

of a group of cells. In the call-level simulation, it is assumed that each switch element

has a sufficiently large buffer and high throughput, and thus, there are no losses of

cells within switch elements. In other words, only the link bandwidth between switch

elements or between input/output ports and switch elements is considered instead of

the operation of calls within switch elements. A peak-bandwidth allocation scheme

is assumed to be used for evaluating a routing algorithm. Thus, there is no overflow

of the link bandwidth. Call blocking rate and link utilization can be obtained under

this assumption. These results can be used to evaluate the effectiveness of routing

algorithms, the fairness among links, and the quality of service (QoS). Link utilization

may be a useful measure in evaluating the effect of incoming variable bit rate (VBR)

traffic.

Figure 4 shows a simple two-state call model with two variable rates: the rateof high state and the rate of low state. The high state means the peak bandwidth of

a call, and the low state corresponds to the minimum cell rate. The duration of a call

has an exponential distribution with parameter , and the length of each state has anexponential distribution, too. If both rates of high state and low state are equal, the

call model corresponds to a constant bit rate (CBR) call. A VBR call model has a

minimum cell rate (MCR) and a peak cell rate (PCR). The average length of each

state and the required bit rate of each state are set through a GUI input unit before

simulations. This call model can be used for both unicast and multicast call.

Figure 5 shows a multiplexing scheme of multiple call connections into one

link. It is assumed that the interarrival time of calls has an exponential distribution

with parameter . Namely, call arrivals follow a Poisson distribution. Unicast andmulticast calls are multiplexed into a single integrated traffic stream. Since each link

accommodates unicast and multicast call connections, output traffic load is used instead

of input traffic load. The total offered output traffic load t is the sum of unicast load uand multicast load m.

u= (1m)t, m =mt,


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Figure 5. Multiplexing of multiple call connections in one link.

Figure 6. Example of link bandwidth management.

where m is the multicast ratio, 0 m 1. Unicast and multicast traffic loads aredefined as follows:

u= Pu(1/u)C(1/u)

= PuuCu

,

m=Pm(1/m)E[F]

C(1/m)=PmmE[F]

Cm,

where subscripts u and m denote unicast and multicast call connections, respectively,

Pu the peak rates, Pm the bandwidths of high state, u and m the call arrival rates,1u and

1m the mean call durations, and C the total bandwidth of each link. Thus,

the average interarrivals of unicast and multicast calls in figure 5 are expressed as

1

u=Pu(1/u)

Cu=

PuCuu

,

1m= Pm(1/m)E[F]

Cm= PmE[F]

Cmm.

Figure 6 shows an example of link bandwidth management. The reserved band-

width means the sum of peak cell rates of all calls and its value varies when a new

call arrives or an existing call is completed. The available bandwidth is the bandwidth


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Figure 7. Link occupation of unicast and multicast call connections in the switching system.

which subtracts the reserved bandwidth from the total capacity of a link, C. Theoccupied bandwidth is a currently used bandwidth and its value varies when any the

state of each call varies. A new call is accepted only if the available bandwidth of

link is larger than the requested peak cell rate. Thus, the reserved bandwidth affects

call blocking rate. Link utilization is the ratio of the occupied bandwidth to the total

capacity during a simulation time.

Figure 7 shows a link occupation of unicast and multicast call connections in the

switching system. If a call connection path is determined by a routing algorithm, all

the connected links are reserved with the bandwidth of high state and are occupied

with the bandwidth of the current state.

This call model is rather simple, but it is useful to evaluate the call-level QoS

of ATM switches. The call-level simulation model also provides two types of fanout

models, i.e., the constant fanout model and the TGM-based fanout model, which are

the same as the cell-level simulation model.

4. Cell-level simulator

4.1. Simulation input variables

The cell-level simulator provides cell-level simulations using the proposed input

traffic models. It has many input variables including the size of switch modules, SBM

size, AFIFO size, simulation time, and seed number. These variables are divided into

the following three groups:

The first group is a set of variables that represent the structure of a switch element:

size of switch element,

SBM size,

AFIFO size.


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seed number,

output file name.

The simulator can be used to evaluate various performances for various purposesby setting input variables through a GUI input unit. For example, the simulator can

find the optimal size of AFIFO satisfying the required QoS through simulations, and

can obtain the required size of SBM to guarantee the required cell loss probability. It

can examine the effect of burst traffic, multicast traffic, priority control and hot-spot

traffic.

Figure 8 shows a GUI input unit for setting input parameter values. If the

parameter values are within a proper range, the GUI input unit can initiate a simulation

and can display the output file using a browser function. Otherwise, it shows the

message that data are inappropriate.

4.2. Simulation output

Simulation results are stored in an output file designated before simulation. If

input traffic includes multicast cells or employs priority classes, more information

related to them is collected in the output file. The output file contains the information

related to cell loss, throughput, and delay. The cell loss and the throughput related

information includes the number of total generation cells, the number of total lost

cells, the number of total outgoing cells, the number of lost cells at each component

(e.g., SBM, AFIFO), the number of lost cells at each stage, the number of lost multicast

cells, and the number of lost cells for each priority class. The delay related information

includes the mean delay of total cells, the mean delay of each priority cell, and the

mean delay of multicast cells.

4.3. Examples of performance evaluations

Figure 9 illustrates the cell loss probability versus SBM size for random and

bursty traffic under a condition of = 0.9, M = 32, N = 64, and mean burst size= 10. The cell loss probability decreases slowly with increasing the SBM size up to

a certain point, and if the SBM size exceeds the value, then even a small increase

in SBM size yields a rapid decrease in the cell loss probability. It is also observed

that there is a large difference in the required SBM size for bursty and random traffic.

The required SBM size for random traffic is approximately 200 cell memory capacity,

while that for burst traffic with a mean burst length of 10 and = 1.0 is over 1000cells. This simulator can be used to tune the system parameters such as the size of

SBM, the size of AFIFO, and the threshold value of buffers. This is an exampleof system parameter tunings using the simulator before developing a new switching

system.

Figure 10 shows the throughput versus multicast cell ratio in the switch element

which adopts an FIFO-queue method for controlling memory access. Traffic load here

means the output traffic load which accommodates unicast and multicast cells in the


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Figure 9. Cell loss probability ( = 0.9, M= 32, N= 64, and E[burst length] = 10 cell time).

Figure 10. Throughput (M= 32, N= 32, and SBM = 512).

output link. The result shows that there is no degradation in throughput by increasing

the multicast cell ratio, contrary to other memory control schemes, such as linked-listand CAM-based schemes [21]. Even though the ratio of multicast cells becomes 0.5,

the throughput of the switch element is nearly constant.

Figure 11 shows the mean delay versus offered traffic load for three different

bursty input traffics for SBM size = 512, M= 32, N= 64, and = 1.0. The trafficload refers to the offered load , and the mean delay and the mean burst length of


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Figure 11. Mean delay (SBM = 512, M= 32, N= 64, and = 1.0).

bursty traffic are expressed in cell times. It is observed that mean delay increases with

increasing the offered traffic load and that it increases with increasing the mean burst

length under the same traffic load.

5. Call-level simulator

5.1. Simulation input variables

The call-level simulator has simpler input variables than the cell-level simulator.

Input variables are categorized into the following two groups:

The first group characterizes the input traffic model:

input traffic load,

mean call duration: unicast and multicast,

bandwidth of high state: unicast and multicast,

bandwidth of low state: unicast and multicast,

average length of high state: unicast and multicast,

average length of low state: unicast and multicast.

The second group has simulation environment variables:

simulation time,

seed number,

output file name.


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Figure 12. Example of output file.

5.2. Simulation output

Simulation results are stored in an output file designated by the GUI input. The

output file has call blocking related information and link utilization related information.

The call blocking related information includes the number of total generated calls, the

number of total blocking calls, the number of blocking calls at the link of each stage,

and call blocking rate. The link utilization related information includes the utilization

at the link sets of each stage.

Figure 12 shows an example of output file which summarizes a simulation result

in terms of the number of total calls, the total number of blocked calls, call blocking

rate and the utilization at each link. In this figure, four link sets are described as

follows:

Link set 0: input links of ALSs.

Link set 1: interconnection links from ALSs to ACS.


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Link set 2: interconnection links from ACS to ALSs.

Link set 3: output links of ALSs.

5.3. Examples of performance evaluation

Figure 13 shows the link utilization at each link designated by a link identification

(ID) number for multicast traffic with m = 1.0 and the TGM-based fanout multicastmodel. Homogeneous multicast CBR calls with a bit rate of 1 Mbps and a mean

fanout of 5 are considered here. The figure shows the fairness of the routing algorithm

adopted in the switching system. The link utilization is nearly constant for all links in

a 6464 ATM switching system. This example shows an application of the call-level

simulator to evaluating the fairness of the considered routing algorithm.

Figure 14 shows the link utilization versus offered traffic load for multicast traffic

with m = 1.0 and the TGM-based fanout multicast model. Homogeneous multicastCBR calls with a bit rate of 1 Mbps and a mean fanout of 5 are considered here. The

utilization of each link group linearly increases as the output offered load increases.

Since multicast cells are copied by a copy cell-splitting algorithm, the utilization of

output links is larger than that of input links. The utilization of output links is five

times higher than that of input links, because E[F] = 5 and there is no loss in switchelements.

Figure 15 shows the call blocking rate versus offered traffic load for three different

CBR peak bandwidths of 500 kbps, 1 Mbps, and 2 Mbps. CBR call connections with a

smaller peak bandwidth yield a lower call blocking rate under the same offered traffic

load.

Figure 13. Link utilization at each link.


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Figure 14. Link utilization (CBR call with peak BW = 1 Mbps, m = 1.0, and E[F] = 5).

Figure 15. Call blocking rate (CBR call, m = 1.0, and constant fanout = 5).

6. Conclusion

In this paper, a cell-level/call-level simulator is developed to evaluate the per-

formances of a shared buffer-type ATM switch element as well as an ATM switching

system. The cell-level simulator provides various cell-level switching functions such

as priority control and multicast, and supports various cell-level traffic models includ-

ing random or bursty traffic, and uniform or hot-spot traffic. The cell-level simulator


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is used to evaluate various performances of the ATM switching system in terms of

cell delay, throughput, cell loss probability, etc. It is also used to tune the system

parameters, such as the size of SBM, the size of AFIFO, and the threshold value of

buffers. The call-level simulator is developed to evaluate the call-level QoS (perfor-

mance indices) of the ATM switching system in terms of call blocking rate and link

utilization.

The simulator can be utilized in investigating various ATM traffic control

schemes, such as routing control, resource management, and call admission control

schemes, as a further study. It is also necessary to develop various call-level input

traffic models as a further study.

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Simulation of ATM Switches

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