Cell‐level/call‐level ATM switch simulator

Telecommunication Systems 14 (2000) 291–309 291

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, KoreaE-mail: [email protected]

b 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 ofthe 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 simulatorusing 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 interms of cell delay, throughput, and cell loss probability. On the other hand, the call-levelsimulator uses call-level traffic models and evaluates the call blocking rate as a call-levelquality 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 switchingsystem has been developed as a virtual channel (VC)/virtual path (VP) switch, andhas been tested in the National Information Superhighway testbed. An ATM switchsimulator also has been developed to evaluate cell-level/call-level performances as wellas traffic control schemes.

The performance of ATM switching systems can be evaluated through directmeasurements of real systems, analytical models [3,6,9–11,14,15] 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 thoughanalytical models may represent only simple ATM switches, they have limitationsin representing more complex ATM switches such as shared-buffer ATM switchesand in dealing with complex input traffic sources. Simulation models can representcomplex ATM switches in detail, including various input traffic, detailed switch el-ements, and traffic control schemes. However, they may take a long time to collectdata.∗ This study was supported in part by the Electronics and Telecommunications Research Institute, Korea.

J.C. Baltzer AG, Science Publishers

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

A large number of ATM switch architectures, which include input bufferedswitches [3,10], output buffered switches [10,11], and shared buffer switches [1,4,7,9,12,13,18,19,21], have been proposed. However, there have been only a few stud-ies on ATM switch simulators [8,20]. A cell-level simulation tool was developed toevaluate ATM switching system with various topologies [20]. A discrete-event sim-ulator for ATM switching systems was limited to evaluating nonblocking switchesand Banyan-type switches [8]. Chiussi et al. [5] also evaluated the performance ofshared-memory switches through cell-level simulations.

All the ATM simulators proposed until now correspond to cell-level node sim-ulators, by which various cell-level performances of the target ATM switches can beevaluated in terms of cell loss probability, throughput, cell transfer delay, cell delayvariation, etc. A call-level simulator is newly needed to obtain call related statistics,such as call blocking rate and link utilization. For example, for a constant bit rate(CBR) call with a peak rate of 1 Mbps, a cell-level simulator generates approximately2600 cells/s during a call. If all input calls are assumed to be homogeneous CBRservices and the offered load is assumed to be 0.7, then the mean number of calls ateach 155 Mbps input port is approximately 100. Thus, it is needed to generate andprocess approximately 2.6 × 105 cells/s for a single input port. This corresponds togenerating 2.6 × 107 cells/s for a 100 × 100 switching system. Since it is difficultto obtain call-level statistics from cell-level simulations, a new call-level simulationscheme is needed.

In this paper, cell-level and call-level traffic models are introduced, and a cell-level/call-level ATM switch simulator is developed for an ATM switching system.The cell-level simulator models various cell-level switching functions such as prioritycontrol and multicast, and evaluates various cell-level performance indices of the ATMswitch in terms of cell delay, throughput and cell loss probability. The proper valuesof various system parameters can be determined through the cell-level simulation.For example, it is possible to determine the size of shared buffer memory under aspecified QoS. The call-level simulator provides call-level traffic models and evaluatescall blocking rate as a call-level QoS. The call-level simulator is useful to examine thefairness of a routing algorithm, the effectiveness of a bandwidth management algorithm,and the validity of a call acceptance algorithm. This simulator is developed utilizingthe OPNET (OPtimized Network Engineering Tools) [17], which is a simulation toolfor modeling and evaluating communication systems, protocols, and networks.

The rest of this paper is organized as follows. Section 2 briefly describes thearchitecture of an ATM switching system. Section 3 introduces input traffic models forcell-level and call-level simulations. Section 4 describes overall features of a cell-levelsimulator and shows several examples of cell-level performance evaluations. Section 5introduces a call-level simulator. Finally, section 6 draws a conclusion.

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 switchingsubsystem (ALS) and ATM central switching subsystem (ACS) for interconnectionsbetween ALSs. Each ALS has Subscriber Interface Modules (SIM), Link InterfaceModules (LIM), a Subscriber Call Processor (SCP), and an Access Switch NetworkModule (ASNM). The ACS contains LIM, an Operation and Maintenance Processor(OMP), an Interconnection Switch Network Module (ISNM). ASNM and ISNM consistof shared-buffer type switch elements in ALS and ACS, respectively. If an incomingcell in an ALS is destined to the output port of another ALS, then it is switched throughthe ACS to its corresponding output. If incoming cells are to be multicast, they arecopied by a cell-splitting copy mechanism.

2.2. Switch element

Figure 2 shows a switch element of the ATM switching system. The operation ofthe switch element is as follows. After incoming cells are converted into parallel bitdata for reducing the internal speed of the switch element, the cells are sequentiallymultiplexed in time by a multiplexer (MUX). Each cell is stored in the shared buffer

Figure 1. ATM switching system.


Figure 2. Switch element of shared buffer architecture.

memory (SBM), and its header is sent to the priority control and routing block. Thepriority control and routing block writes the SBM address of the incoming cell inaddress first-in-first-out (AFIFO) buffer. An idle address for each incoming cell isprovided by the idle address pool (IAP) which holds all the empty addresses of theSBM. 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-sharingscheme, which is a buffer allocation approach to achieve as much buffer sharing aspossible while maintaining a degree of fairness. Since the scheme uses the finite sizeof AFIFO, it limits the maximum number of cells destined for each output port.

There are several types of memory access control schemes for shared bufferswitches: 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 multicastcells is newly added. In addition, a controller is needed to arbitrate between unicastand 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 singletime 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 addressesof the multicast cells are copied before enqueueing. Thus, there is no degradation inthroughput with increasing multicast cell ratio [21]. The switch element consideredin this paper employs this FIFO-queue approach for controlling the address of sharedbuffer.


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 bundleof 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 isselected as a candidate link.

2. The bandwidth of all candidate links is reserved by the required bandwidth in orderto prevent another new call from occupying the bandwidth before the completionof 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 acertain link of the destination ALS is available and there is a link candidate whichbelongs to the same group, these two links are selected as a connection path ofthe incoming call.

5. The bandwidth of the selected link in the destination ALS is reserved by therequired bandwidth of the incoming call. The processor of the destination ALSsends the identification number of the remaining candidate links.

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


3. Input traffic models

3.1. Cell-level input traffic models

Simulation time is divided into cell time slots in the system which operatessynchronously. It is assumed that the traffic of an input port is a multiplexed streamof many virtual channels (VCs). The cell-level simulator supports various input trafficmodels 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 ρ, 0 6 ρ 6 1, where ρ is the offered load per eachinput port. The probability that x cells arrive during y time slots is given by

Py(x) =

(y

x

)ρx(1− ρ)y−x.

• Bursty traffic. The offered traffic on each input port is modeled by an InterruptedBernoulli 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. Whenthe current state is active, the state of the next time slot is still active with probabilityp, and is changed into the idle state with probability 1− p. 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} = (1− p)px−1,

P{Y = y} = (1− q)qy−1.

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

E[interval of generating cells] =2− p− qα(1− q)

.

Then, p and q can be obtained from the following input parameters: the input trafficload ρ, the average burst length E[X], and the cell generation rate α:

p= 1− 1E[length of active state]

= 1− 1E[X]

,

q=2ρ− pρ− α

ρ− α .


3.1.2. Priority classesThe cell-level simulator supports cell loss priority (CLP) control, in which buffers

store only high priority cells when the queue length exceeds the specified thresholdvalue. The threshold value and the ratio of high priority cells among all incomingcells 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 has

its destination output port j. If N 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− hN

, j = jH,

1− hN

, j 6= jH,

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

3.1.4. Multicast modelThe 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 iscopied into a constant number of cells, c (2 6 c 6 N ), namely, F = c for all multicastcells. In the truncated geometric distribution model, an original cell is copied intok cells with the following truncated geometric distribution:

P{F = k} =(1− q)qk−2

1− qN−1 , 2 6 k 6 N ,

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

E[F ] =1

1− q +1−NqN−1

1− qN−1 .

3.2. Call-level input traffic models

Using cell-level input traffic models described in section 3.1, various cell-levelperformances can be evaluated in terms of delay, throughput, and cell loss probability.However, this cell-level simulator does not provide performance results related tocalls. Thus, a call-level simulation is newly needed in order to evaluate proper routingalgorithms in multipath switches.


Figure 4. Simple two-state call model.

New input traffic models are proposed here for call-level simulations. Since it isvery difficult to examine the performance of switching system by considering all cellsin switch elements, a call is regarded as an occupation of some link bandwidth in steadof a group of cells. In the call-level simulation, it is assumed that each switch elementhas a sufficiently large buffer and high throughput, and thus, there are no losses ofcells within switch elements. In other words, only the link bandwidth between switchelements or between input/output ports and switch elements is considered instead ofthe operation of calls within switch elements. A peak-bandwidth allocation schemeis assumed to be used for evaluating a routing algorithm. Thus, there is no overflowof the link bandwidth. Call blocking rate and link utilization can be obtained underthis assumption. These results can be used to evaluate the effectiveness of routingalgorithms, the fairness among links, and the quality of service (QoS). Link utilizationmay 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 ofa call, and the low state corresponds to the minimum cell rate. The duration of a callhas an exponential distribution with parameter µ, and the length of each state has anexponential distribution, too. If both rates of high state and low state are equal, thecall model corresponds to a constant bit rate (CBR) call. A VBR call model has aminimum cell rate (MCR) and a peak cell rate (PCR). The average length of eachstate and the required bit rate of each state are set through a GUI input unit beforesimulations. This call model can be used for both unicast and multicast call.

Figure 5 shows a multiplexing scheme of multiple call connections into onelink. It is assumed that the interarrival time of calls has an exponential distributionwith parameter λ. Namely, call arrivals follow a Poisson distribution. Unicast andmulticast calls are multiplexed into a single integrated traffic stream. Since each linkaccommodates unicast and multicast call connections, output traffic load is used insteadof input traffic load. The total offered output traffic load ρt is the sum of unicast load ρu

and multicast load ρm.

ρu = (1−m)ρt, ρm = mρt,


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

Figure 6. Example of link bandwidth management.

where m is the multicast ratio, 0 6 m 6 1. Unicast and multicast traffic loads aredefined as follows:

ρu =Pu(1/µu)C(1/λu)

=Puλu

Cµu,

ρm =Pm(1/µm)E[F ]

C(1/λm)=PmλmE[F ]

Cµm,

where subscripts u and m denote unicast and multicast call connections, respectively,Pu the peak rates, Pm the bandwidths of high state, λu and λm the call arrival rates,µ−1

u and µ−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

1λu

=Pu(1/µu)Cρu

=Pu

Cµuρu,

1λm

=Pm(1/µm)E[F ]

Cρm=PmE[F ]Cµmρm

.

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


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 thestate of each call varies. A new call is accepted only if the available bandwidth oflink is larger than the requested peak cell rate. Thus, the reserved bandwidth affectscall blocking rate. Link utilization is the ratio of the occupied bandwidth to the totalcapacity during a simulation time.

Figure 7 shows a link occupation of unicast and multicast call connections in theswitching system. If a call connection path is determined by a routing algorithm, allthe connected links are reserved with the bandwidth of high state and are occupiedwith the bandwidth of the current state.

This call model is rather simple, but it is useful to evaluate the call-level QoSof ATM switches. The call-level simulation model also provides two types of fanoutmodels, i.e., the constant fanout model and the TGM-based fanout model, which arethe 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 inputtraffic models. It has many input variables including the size of switch modules, SBMsize, AFIFO size, simulation time, and seed number. These variables are divided intothe 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.


• The second group has variables that characterize the input traffic model:

– traffic load,

– multicast related variables: multicast ratio,and fanout model,

– priority control related variables: SBM threshold, AFIFO threshold, and ratio ofhigh priority cells,

– input stream pattern related variables: mean burst length α,

– output port distribution: distribution type and hot-spot ratio.

• The final group is related to simulation environment parameters:

– simulation time,

Figure 8. GUI input unit.


– 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 canfind the optimal size of AFIFO satisfying the required QoS through simulations, andcan obtain the required size of SBM to guarantee the required cell loss probability. Itcan examine the effect of burst traffic, multicast traffic, priority control and hot-spottraffic.

Figure 8 shows a GUI input unit for setting input parameter values. If theparameter values are within a proper range, the GUI input unit can initiate a simulationand can display the output file using a “browser” function. Otherwise, it shows themessage that data are inappropriate.

4.2. Simulation output

Simulation results are stored in an output file designated before simulation. Ifinput traffic includes multicast cells or employs priority classes, more informationrelated to them is collected in the output file. The output file contains the informationrelated to cell loss, throughput, and delay. The cell loss and the throughput relatedinformation includes the number of total generation cells, the number of total lostcells, 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 multicastcells, and the number of lost cells for each priority class. The delay related informationincludes the mean delay of total cells, the mean delay of each priority cell, and themean delay of multicast cells.

4.3. Examples of performance evaluations

Figure 9 illustrates the cell loss probability versus SBM size for random andbursty traffic under a condition of ρ = 0.9, M = 32, N = 64, and mean burst size= 10. The cell loss probability decreases slowly with increasing the SBM size up toa certain point, and if the SBM size exceeds the value, then even a small increasein SBM size yields a rapid decrease in the cell loss probability. It is also observedthat 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 α = 1.0 is over 1000cells. This simulator can be used to tune the system parameters such as the size ofSBM, the size of AFIFO, and the threshold value of buffers. This is an exampleof system parameter tunings using the simulator before developing a new switchingsystem.

Figure 10 shows the throughput versus multicast cell ratio in the switch elementwhich adopts an FIFO-queue method for controlling memory access. Traffic load heremeans the output traffic load which accommodates unicast and multicast cells in the


Figure 9. Cell loss probability (ρ = 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 increasingthe 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 differentbursty input traffics for SBM size = 512, M = 32, N = 64, and α = 1.0. The trafficload refers to the offered load ρ, and the mean delay and the mean burst length of


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

bursty traffic are expressed in cell times. It is observed that mean delay increases withincreasing the offered traffic load and that it increases with increasing the mean burstlength 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.


Figure 12. Example of output file.

5.2. Simulation output

Simulation results are stored in an output file designated by the GUI input. Theoutput file has call blocking related information and link utilization related information.The call blocking related information includes the number of total generated calls, thenumber 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 utilizationat the link sets of each stage.

Figure 12 shows an example of output file which summarizes a simulation resultin terms of the number of total calls, the total number of blocked calls, call blockingrate and the utilization at each link. In this figure, four link sets are described asfollows:

– Link set 0: input links of ALSs.

– Link set 1: interconnection links from ALSs to ACS.


– Link set 2: interconnection links from ACS to ALS’s.

– 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 meanfanout of 5 are considered here. The figure shows the fairness of the routing algorithmadopted in the switching system. The link utilization is nearly constant for all links ina 64×64 ATM switching system. This example shows an application of the call-levelsimulator to evaluating the fairness of the considered routing algorithm.

Figure 14 shows the link utilization versus offered traffic load for multicast trafficwith 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. Theutilization 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 ofoutput links is larger than that of input links. The utilization of output links is fivetimes 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 differentCBR peak bandwidths of 500 kbps, 1 Mbps, and 2 Mbps. CBR call connections with asmaller peak bandwidth yield a lower call blocking rate under the same offered trafficload.

Figure 13. Link utilization at each link.


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 switchingsystem. The cell-level simulator provides various cell-level switching functions suchas 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


is used to evaluate various performances of the ATM switching system in terms ofcell delay, throughput, cell loss probability, etc. It is also used to tune the systemparameters, such as the size of SBM, the size of AFIFO, and the threshold value ofbuffers. 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 linkutilization.

The simulator can be utilized in investigating various ATM traffic controlschemes, such as routing control, resource management, and call admission controlschemes, as a further study. It is also necessary to develop various call-level inputtraffic models as a further study.

References

[1] I.W. Causey and H.S. Kim, Comparison of buffer allocation schemes in ATM switches: Completesharing, partial sharing, and dedicated allocation, in: ICC-94 (1994) pp. 1164–1168.

[2] D.X. Chen and J.W. Mark, A buffer management scheme for the SCOQ switch under nonuniformtraffic loading, in: IEEE INFOCOM-92 (1992) pp. 145–154.

[3] J.S.-C. Chen and R. Guerin, Performance study of an input queueing packet switch with two priorityclasses, IEEE Transactions on Communications 39 (1991) 117–126.

[4] F.M. Chiussi, J.G. Kneuer and V.P. Kumar, The ATLANTA architecture and chipset, in: ISS-97(1997) pp. 43–52.

[5] F.M. Chiussi, Ye Xia and V.P. Kumar, Performance of shared-memory switches under multicastbursty traffic, IEEE Journal on Selected Areas in Communications 15 (1997) 473–487.

[6] A. Descloux, Stochastic models for ATM switching networks, IEEE Journal on Selected Areas inCommunications 9 (1991) 450–457.

[7] N. Endo, T. Kozaki, T. Ohuchi, H. Kuwahara and S. Gohara, Shared buffer memory switch for anATM exchange, IEEE Transactions on Communications 41 (1993) 237–245.

[8] J. Garcia-Haro, R.M. Sillue and J.M. Moreno, Description of a simulation environment to evaluatehigh performance ATM fast packet switches, in: HPN-94 (1994) pp. 421–436.

[9] M.G. Hluchyj and M.J. Karol, Queueing in high-performance packet switching, IEEE Journal onSelected Areas in Communications 6 (1988) 1587–1597.

[10] M.J. Karol, M.G. Hluchyj and S.P. Morgan, Input versus output queueing on a space-division packetswitch, IEEE Transactions on Communications 35 (1987) 1347–1356.

[11] H.S. Kim, I. Widjaja and A. Leon-Garcia, Performance of output-buffered Banyan networks witharbitrary buffer sizes, in: IEEE INFOCOM-91 (1991) pp. 701–710.

[12] T. Kozaki, N. Endo, Y. Sakurai, O. Matsubara, M. Mizukami and K. Asano, 32× 32 shared buffertype ATM switch VLSIs for B-ISDNs, IEEE Journal on Selected Areas in Communications 9 (1991)1239–1247.

[13] S. Kumar and D.P. Agrawal, A shared-buffer direct-access (SBDA) switch architecture for ATM-based networks, in: ICC-94 (1994) pp. 101–105.

[14] S.-Q. Li, Nonuniform traffic analysis on a nonblocking space-division packet switch, IEEE Trans-actions on Communications 38 (1990) 1085–1096.

[15] S.-Q. Li, Performance of a nonblocking space-division packet switch with correlated input traffic,IEEE Transactions on Communications 40 (1992) 97–108.

[16] R.O. Onvural, Asynchronous Transfer Mode Networks: Performance Issues (Artech House, 1994).[17] Optimized engineering tools (OPNET) M version technical overview, MIL 3 Inc. (1994).[18] K.J. Schultz and P. Glenn Gulak, CAM-based single-chip shared buffer ATM switch, in: ICC-94

(1994) pp. 1190–1195.


[19] Y. Shobatake, M. Motoyama, E. Shobatake, T. Kamitake, S. Shimizu, M. Noda and K. Sakaue,A one-chip scalable 8× 8 ATM switch LSI employing shared buffer architecture, IEEE Journal onSelected Areas in Communications 9 (1991) 1248–1254.

[20] E. Valdimarsson, General purpose simulation tool for analyzing switching systems, in:GLOBECOM-86 (1993) pp. 1358–1362.

[21] H. Yamanaka, H. Saito, H. Kondoh, Y. Sasaki, H. Yamada, M. Tsuzuki, S. Nishio, H. Notani,A. Iwabu, M. Ishiwaki, S. Kohama, Y. Matsuda and K. Oshima, Scalable shared-buffering ATMswitch with a versatile searchable queue, IEEE Journal on Selected Areas in Communications 15(1997) 773–784.

Documents

Cell‐level/call‐level ATM switch simulator