Telecommunication Systems 14 (2000) 291–309
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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] 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 Information 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 Superhighway 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 control 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 switching 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 [3,6,9–11,14,15] and simulations [5,8,20]. Although direct measurements of real ATM switching systems are accurate, 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 switches and in dealing with complex input traffic sources. Simulation models can represent complex ATM switches in detail, including various input traffic, detailed switch elements, 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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A large number of ATM switch architectures, which include input buffered switches [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 studies on ATM switch simulators [8,20]. A cell-level simulation tool was developed to evaluate ATM switching system with various topologies [20]. A discrete-event simulator for ATM switching systems was limited to evaluating nonblocking switches and Banyan-type switches [8]. Chiussi et al. [5] also evaluated the performance of shared-memory switches through cell-level simulations. All the ATM simulators proposed until now correspond to cell-level node simulators, by which various cell-level performances of the target ATM switches can be evaluated in terms of cell loss probability, throughput, cell transfer delay, cell delay variation, 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 approximately 2600 cells/s during a call. If all input calls are assumed to be homogeneous CBR services and the offered load is assumed to be 0.7, then the mean number of calls at each 155 Mbps input port is approximately 100. Thus, it is needed to generate and process approximately 2.6 × 105 cells/s for a single input port. This corresponds to generating 2.6 × 107 cells/s for a 100 × 100 switching system. Since it is difficult to obtain call-level statistics from cell-level simulations, a new call-level simulation scheme is needed. In this paper, cell-level and call-level traffic models are introduced, and a celllevel/call-level ATM switch simulator is developed for an ATM switching system. The cell-level simulator models various cell-level switching functions such as priority control and multicast, and evaluates various cell-level performance indices of the ATM switch in terms of cell delay, throughput and cell loss probability. The proper values of 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 a specified QoS. The call-level simulator provides call-level traffic models and evaluates call blocking rate as a call-level QoS. The call-level simulator is useful to examine the fairness of a routing algorithm, the effectiveness of a bandwidth management algorithm, and the validity of a call acceptance algorithm. This simulator is developed utilizing the OPNET (OPtimized Network Engineering Tools) [17], which is a simulation tool for modeling and evaluating communication systems, protocols, and networks. The rest of this paper is organized as follows. Section 2 briefly describes the architecture of an ATM switching system. Section 3 introduces input traffic models for cell-level and call-level simulations. Section 4 describes overall features of a cell-level simulator and shows several examples of cell-level performance evaluations. Section 5 introduces a call-level simulator. Finally, section 6 draws a conclusion.
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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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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 FIFOqueue scheme. In the linked-list and the CAM-based control mechanisms, since multicast 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 multicast 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 the number of link group is L, then each ALS has kL links. The routing algorithm utilized in 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 each link 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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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 Bernoulli process with parameter ρ, 0 6 ρ 6 1, where ρ is the offered load per each input port. The probability that x cells arrive during y time slots is given by y x ρ (1 − ρ)y−x . Py (x) = x • 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 1 − p. When the state is idle, the next state is idle with probability q and is changed into the active state with probability 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)q y−1 . The average of X and Y are 1/(1 − p) and 1/(1 − q), respectively. If cell generation rate in the active state is α, then the average interval of generating cells is written as E[interval of generating cells] =
2−p−q . α(1 − q)
Then, p and q can be obtained from the following input parameters: the input traffic load ρ, the average burst length E[X], and the cell generation rate α: 1 1 =1− , E[length of active state] E[X] 2ρ − pρ − α q= . ρ−α
p=1−
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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 has its destination output port j. If N is the switch size, and output port distribution is uniform, then qij = 1/N and all incoming cells are uniformly distributed to all output ports. • Hot-spot distribution. Cells from input port i are transferred to a hot-spot output port with probability h, and the remaining traffic is assumed to be uniformly distributed to all output ports[2]. The transition probability qij is written as 1−h h + , j = jH , N qij = 1 − h, j 6= jH , N 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 F denote the fanout of a multicast cell. In the constant fanout model, an original cell is copied into a constant number of cells, c (2 6 c 6 N ), namely, F = c for all multicast cells. 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)q k−2 , 1 − q N −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 − N q N −1 1 + . 1−q 1 − q N −1
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 rate of 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 µ, and the length of each state has an exponential 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 λ. Namely, call arrivals follow a Poisson distribution. Unicast and multicast 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 ρt is the sum of unicast load ρu and multicast load ρm . ρu = (1 − m)ρt ,
ρm = mρt ,
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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 6 m 6 1. Unicast and multicast traffic loads are defined as follows: Pu λu Pu (1/µu ) = , C(1/λu ) Cµu Pm λm E[F ] Pm (1/µm )E[F ] = ρm = , C(1/λm ) Cµm ρu =
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, −1 µ−1 u and µm 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 Pu (1/µu ) Pu 1 = = , λu Cρu Cµuρu Pm (1/µm )E[F ] Pm E[F ] 1 = = . λm Cρm Cµm ρm Figure 6 shows an example of link bandwidth management. The reserved bandwidth 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. The occupied 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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• 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 of high 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.
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– seed number, – output file name. The simulator can be used to evaluate various performances for various purposes by 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 ρ = 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 α = 1.0 is over 1000 cells. 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 example of 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 (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 increasing the multicast cell ratio, contrary to other memory control schemes, such as linked-list and 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 α = 1.0. The traffic load refers to the offered load ρ, 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 α = 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 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 multicast model. 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 64 × 64 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 multicast CBR 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 switch elements. 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 performances 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 including 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 (performance 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. References [1] I.W. Causey and H.S. Kim, Comparison of buffer allocation schemes in ATM switches: Complete sharing, 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 nonuniform traffic 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 priority classes, 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 multicast bursty 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 in Communications 9 (1991) 450–457. [7] N. Endo, T. Kozaki, T. Ohuchi, H. Kuwahara and S. Gohara, Shared buffer memory switch for an ATM 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 evaluate high 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 on Selected 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 packet switch, IEEE Transactions on Communications 35 (1987) 1347–1356. [11] H.S. Kim, I. Widjaja and A. Leon-Garcia, Performance of output-buffered Banyan networks with arbitrary 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 buffer type 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 ATMbased networks, in: ICC-94 (1994) pp. 101–105. [14] S.-Q. Li, Nonuniform traffic analysis on a nonblocking space-division packet switch, IEEE Transactions 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.
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[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 on Selected 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 ATM switch with a versatile searchable queue, IEEE Journal on Selected Areas in Communications 15 (1997) 773–784.
