Abstract:
The present invention is directed to a traffic simulation algorithm for an asynchronous transfer mode communications (ATM) network. The algorithm recognizes that packets in ATM networks can have interarrival times that are lognormally distributed or lognormally and normally distributed. Lognormal and, in some cases, normal random number generators are used to generate packet interarrival times of a synthetic traffic stream.

Description:
FIELD OF THE INVENTION 
     The present invention is directed generally to traffic simulation in telecommunications networks and specifically to traffic simulation in asynchronous transfer mode networks. 
     BACKGROUND OF THE INVENTION 
     The current public switched telephone network (PSTN) was implemented as a highly reliable, robust, and efficient system for transporting voice traffic. The PSTN has now been burdened with additional types of traffic for which the PSTN was not designed to transport (e.g., Internet, file transfer, video, fax, etc.). The current narrowband synchronous transfer mode (STM) telephony system will have to be replaced by or evolve into a broadband network to preserve the integrity of the system and accommodate the new services. 
     The asynchronous transfer mode (ATM) protocol has been selected as the core switching protocol for emerging broadband networks. ATM is an elegant protocol that has the desirable ability to multiplex voice, video, and data and to transmit information on the same communications channel at very high speeds. As used herein, ATM refers to a connection-oriented protocol in which bandwidth is allocated when the originating end user requests a connection. This allows ATM to efficiently support a network&#39;s aggregate demand by allocating bandwidth on demand based on immediate user need. 
     Problems have been encountered in modeling traffic on an ATM network, which has complicated the design and analysis of ATM networks. For a network to be properly sized and provisioned, the design engineer must thoroughly understand the traffic load and the behavior of that traffic load over time. Traditionally, STM networks were based on the Poisson model. Random number generators were used to produce streams of numbers, representative of real network interarrival times, and which are based on the Poisson model. However, this model is unable to accurately characterize the “bursty” nature of ATM network traffic. Burstiness is present in a traffic process if the arrival points appear to form visual clusters; that is, the packets have runs of several short interarrival times (i.e., the time interval between the receipt of successive packets at a specified destination from a specific source) followed by a relatively long one. As will be appreciated, voice and video packets in ATM networks are typically given a higher priority than data packets in routing or switching the packets for processing. Accordingly, data packets can have significantly longer packet interarrival times than voice or video packets. 
     Other models have been considered in modeling ATM traffic using random number generators, including the Markov Modulated model, the Transform Expand Sample model, the Autoregressive model, the Fluid model, and the Self-similar model. Although these models have been found to have varying degrees of success for modeling Ethernet traffic (which, like ATM networks, uses a packet-based protocol), they have been largely unsuccessful in characterizing the bursty nature of ATM traffic. 
     The failure of these models is in part due to the differences between ATM networks and other type of packet networks. For example, ATM is a connection-oriented protocol with a fixed length packet size. This contrasts with Ethernet which is a connectionless protocol with variable length packet size. Variable packet sizes give rise to a Gaussian (normal) or exponential probilistic distribution of packet interarrival times. 
     SUMMARY OF THE INVENTION 
     These and other needs are addressed by the methods and systems of the present invention. The present invention is premised on the recognitions (a) that interarrival times of packets in ATM networks can have a lognormal probabilistic distribution; (b) that delayed packets on an ATM network can follow a normal probabilistic distribution; and (c) that packet interarrival times in an ATM network corresponding to data packets alone or to data packets and voice and/or video packets typically have bimodal probabilistic distributions. In one configuration, a probabilistic distribution(s) is defined by a normal or self-similar (Gaussian) model and the other probabilistic distribution(s) is defined by a lognormal model. As used herein, a “network” refers to an architecture having two or more computers (e.g., each of which includes a processor and memory) connected by one or more communication paths (e.g., a local area network (LAN) or wide area network (WAN)). In a typical ATM network, short packet interarrival times (i.e., less than a selected value) define a lognormal probabilistic distribution while long packet interarrival times (i.e., more than a selected value) define a normal probabilistic distribution. 
     In a first embodiment of the present invention, a method for modeling or predicting the performance of (or simulating the traffic in) an ATM network is provided. The ATM network will transport or has transported a stream of packets. The method includes the step of generating (e.g., randomly or psuedorandomly) an at least substantially lognormally distributed set of packet interarrival times corresponding to the plurality of packets. By using lognormal number generators, the methodology of the present invention accurately considers the effect of ATM switch characteristics on traffic behavior. The simulated traffic generated by the algorithm compares closely with traffic on an actual ATM network. For this reason, the algorithm has applications in the areas of ATM switch design, ATM traffic simulation tools, and ATM network design and optimization (particularly the derivation of trunking tables, which are used to size and provision switch trunks). 
     In one configuration, the packet stream also includes a second plurality of packets having normally distributed packet interarrival times. In that event, the method would further include generating (e.g., randomly or pseudorandomly) a normally distributed set of packet interarrival times. 
     In another configuration, the method further includes the steps of (i) multiplying (a) a percentage of the packet stream that corresponds to the plurality of packets and (b) the number of packets in the packet stream to provide the number of packets in the plurality of packets and (ii) multiplying (a) a percentage of the packet stream that corresponds to the second plurality of packets and (b) the number of packets in the packet stream to provide the number of packets in the second plurality of packets. This is a typical step used in modeling an existing or planned ATM network. The total number of packets in the packet stream during a selected time interval can be selected using any technique for characterizing traffic in a communications network, such as busy hour, busy day, busy month, peak call rate, committed burst size, and the like. 
     The number generators can be any algorithm providing output defined by the desired probabilistic distribution (e.g., normal or lognormal probabilistic distributions). In one configuration, the number of generators are random or pseudorandom number generators. In one configuration, the number generators require input such as the number of packets in the plurality of packets (or sample size or vector length) and a mean and a variance of a lognormal distribution characterizing (or believed to characterize) packet interarrival times of the plurality of packets (for the lognormal random number generator) or the number of packets in the second plurality of packets (or second sample size) and a mean and a variance of a normal distribution characterizing (or believed to characterize) packet interarrival times of the packets in the second plurality of packets (for the normal random number generator). As will be appreciated, other techniques may be used to generate lognormal or normal distributions of packet interarrival times including artificially constructed ATM packets (comprising a series of ones and zeros, 58 bytes in length) which have lognormal and normal time interval distributions between packets. 
     In another configuration, the second plurality of packets has a bimodal distribution. This is a common occurrence when voice and/or video packets arrive at different times such that the data packets have a wide range of packet interarrival times. In this configuration, (a) a lognormal fraction of packets in the second plurality of packets having at least substantially lognormally distributed packet interarrival times and (b) a normal fraction of packets in the second plurality of packets having at least substantially normally distributed packet interarrival times are determined. The generating step for the packets in the second plurality of packets is applied to the number of packets in the normal fraction of packets. For the number of packets in the lognormal fraction of packets, the step of generating an at least substantially lognormally distributed set of packet interarrival times such as by using a lognormal random or pseudorandom number generator is provided. 
     In yet another embodiment, a system for characterizing traffic on an ATM network is provided. The system includes lognormal number generating means for generating a plurality of at least substantially lognormally distributed values corresponding to the plurality of packets. 
     In yet a further embodiment, a system for characterizing traffic on an ATM network is provided that includes:
         (i) a lognormal number generator for generating a plurality of at least substantially lognormally distributed values corresponding to the first plurality of packets; and   (ii) a normal number generator for generating a plurality of at least substantially normally distributed values corresponding to the second plurality of packets; and   (iii) a combiner, in communication with the lognormal number generator and the normal number generator, for combining the plurality of lognormally distributed values and the plurality of normally distributed values to provide an aggregate stream of values.       

     In yet another embodiment, a method for modeling or predicting packet interarrival times on an ATM network is provided. The method includes the steps of:
         (i) providing (a) a number of packets in a first portion of a plurality of packets that will be transported or have been transported on an ATM network, the packets in the first portion containing at least one of voice and video information and (b) a number of packets in a second portion of the plurality of packets, the packets in the second portion containing information other than the at least one of voice and video information;   (ii) generating with a lognormal number generator a plurality of packet interarrival times values corresponding to at least some of the packets in the first portion; and   (iii) generating with a normal number generator a plurality of packet interarrival times corresponding to at least some of the packets in the second portion. The summation or combination of the output of the two types of number generators provides a synthetic traffic stream that closely resembles the actual behavior of the modeled ATM system.       

     The foregoing description of the various embodiments of the present invention is intended to be neither complete nor exhaustive. Those of ordinary skill in the art will appreciate that numerous other embodiments can be envisioned using one or more of the components set forth above. For example, a variety of systems can be envisioned for performing the method steps noted above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram of an ATM switching architecture; 
         FIG. 2  is a flow diagram of a modeling architecture according to an embodiment of the present invention; 
         FIG. 3  is a flow schematic of software according to another embodiment of the present invention; 
         FIGS. 4A and 4B  are plots of number of packet arrivals (vertical axis) against packet interarrival time (horizontal axis) for an ATM network; 
         FIG. 5  is a plot of number of packet arrivals (vertical axis) against packet interarrival time (horizontal axis) for synthetic data for an ATM network; 
         FIG. 6  is a plot of number of packet arrivals (vertical axis) against packet interarrival time (horizontal axis) for an ATM network; and 
         FIG. 7  is another plot of number of packet arrivals (vertical axis) against packet interarrival time (horizontal axis) for synthetic data for an ATM network. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , a typical ATM switching architecture is depicted. The ATM switch  10  includes a switch buffer  14  and switch controller  18 . Although an ATM cell or packet in the traffic  22  entering the switch  10  contain bits for discard priority, it is preferable to slow the traffic  22  down rather than degrade the level of service by discarding packets. Flow-control mechanisms in the switch controller  18  limit the arrival rate when the distination buffers become full. In other words, packets which arrive after the buffer is full must wait until buffer space is made available by departing packets. The overflow traffic can thus result in a virtual overflow buffer  26  whose size depends on the transmission speed and buffer size of the switch. As cells are drained away from the buffer  14 , cells waiting in the virtual overflow buffer  26  are admitted to the buffer  14 . 
     A very desirable feature of ATM switches is that they are priority-based and policy-based. Priority-based refers to an ATM switch&#39;s capability to assign an admission and transmission priority to an ATM cell based on the type of information it caries (voice, video, or data). Policy-based refers to an ATM switch&#39;s capability to assign admission and transmission priority to an ATM cell based on both the type of information it is carrying and the time of day. For example, voice usually has a higher priority than data. However, a switch administrator might want to give data the highest priority for certain hours of the day for example, late at night. Thus, an ATM based network gives network administrators much control over shaping the characteristics of traffic on their networks. 
     Packets passing through the switch can have a broad range of packet interarrival times at their respective destinations. Because voice and video packets have higher admission priority to the switch buffer  14  and much higher sensitivity to delay, packets containing such information typically have short packet interarrival times. In contrast, packets containing information other than voice or video have a lower admission priority to the buffer  14  and will typically have a wide range of packet interarrival times ranging from short interarrival times to long interarrival times, depending on the volume of higher priority packets received by the switch. 
     The broad range of packet interarrival times defines a bimodal probabilistic distribution. The packets having shorter interarrival times define a substantially lognormal probabilistic distribution while those having longer interarrival times define a substantially normal or self-similar probabilistic distribution. As will be appreciated, a lognormal distribution is a continuous distribution of a random variable whose logarithm is normally distributed. It typically resembles a positively or negatively skewed curve. The typical probability density function of a random variable X having Λ(μδ 2)  is: 
         P   ⁡     (   x   )       =     {                       x   ≥   0                 1       2   ⁢   πσ         ⁢     ⅇ         -       (     x   -   μ     )     2       /   2     ⁢     σ   2                                             x   ≤   0                 
 
where, μ is the mean δ the standard deviation and δ 2  the variance. A typical probability density function for a normal or Gaussian distribution is: 
         P   ⁡     (   x   )       =     {                         -   ∞     ≤   x   ≥   ∞                 1       2   ⁢   πσ         ⁢     ⅇ         -       (     x   -   μ     )     2       /   2     ⁢     σ   2                                                               
 
where X is a random variable, μ is the mean, δ the standard deviation, and δ 2  the variance.
 
     Referring to  FIG. 2 , an architecture for modeling or simulating packet interarrival times in an ATM switch is illustrated. The architecture  50  includes inputs  54  and  58 , normal number generator  62  for generating an at least substantially normally distributed set of interarrival times, lognormal number generator  66  for generating an at least substantially lognormally distributed set of interarrival times, and combiner  70 . Input  54  inputs the number of packets (in the packet stream  22  passing through the ATM switch) having normally distributed packet interarrival times and the mean and variance of the corresponding normal distribution into the normal number generator  62 . Input  58  inputs the number of packets (in the packet stream  22  passing through the ATM switch) having lognormally distributed packet interarrival times and the mean and variance of the corresponding lognormal distribution into the lognormal number generator  66 . Although any random or pseudorandom number generator that produces values having the desired probabilistic distribution can be used for the number generator, preferred random or pseudorandom generators are the MATLAB™ lognormal and normal random or pseudorandom number generator programs distributed by The MathWorks, Inc. The combiner  70  combines the outputs  74  and  78  from the generators  62 ,  66 , respectively, to form a synthetic traffic stream  82 . The synthetic traffic stream  82  replicates the distribution of packet interarrival times resulting from the ATM switch  10  and the mixture of packet types in the traffic  22  entering the switch  10 . 
       FIG. 3  is a flow schematic of an embodiment of a method for operating the architecture of  FIG. 2 . In box  100 , the user must determine the traffic mixture. In a typical ATM network, the traffic  22  is characterized or defined in terms of the share or percentage of the packets in the traffic  22  entering the switch that contain voice information, that contain video information, and/or that contain data (information other than voice and/or video information). With this mixture, the number of packets containing each type of information, namely voice, video, and data, can be determined by multiplying the percentages by the total number of packets passing through or routed by the switch during a selected time interval. In some applications, a volumetric range of packets in each category (voice, video, and data) will be determined. 
     In some applications, packets (such as those containing data) will have interarrival times characterized by a bimodal distribution; that is, some of the packets will have interarrival times that are distributed normally and other of the packets will have interarrival times that are distributed lognormally. In such applications, the numbers of packets in each category must be determined. This can be done by assigning a percentage or range of percentages to the portion of the packets having normally distributed interarrival times and/or lognormally distributed interarrival times. These percentages or ranges of percentages can then be multiplied by the total number of packets passing through or routed by the switch in a specified time interval to yield the number of packets in each category (i.e., having normally or lognormally distributed interarrival times). 
     In box  104 , the pertinent input parameters are input into the normal number generator  62  and lognormal number generator  66 . For the normal number generator  62 , the input variables are the mean and variance of the normal distribution of the data packet interarrival times (that are distributed normally) and the total number of data packets of this type passing through the switch during the selected time interval. For the lognormal number generator  66 , the input variables are the mean and variance of the lognormal distribution of the data packet interarrival times (that are distributed lognormally) and the total number of data packets of this type passing through the switch during the selected time interval. 
     In boxes  108  and  112 , number generators each generate and output values that can be a serial stream of packet interarrival times and/or a series of sets of values, e.g., a packet interarrival time and the number of packets corresponding to the packet interarrival time. The total number of values generated by each generator is typically equivalent to the number of data packets having normally distributed interarrival times (for the normal number generator) and to the number of data packets having lognormally distributed interarrival times (for the lognormal number generator). 
     The outputted values from each number generator are combined in a summing step  116  to form a composite traffic stream of data packet interarrival times. 
     In box  120 , parameters are inputted into a lognormal number generator  66  (which may be the same or different from the generator  66  operated in box  108 ) in relation to the packets containing voice and/or video information. The inputted variables include the total number of packets containing voice and/or video information that are routed by the switch during the selected time interval and the mean and variance of the lognormal distribution of the voice and/or video packet interarrival times. In certain applications, the lognormal distributions of voice packets on the one hand and video packets on the other are different. In such situations, separate lognormal number generators  66  can be used to handle the differing input parameters (i.e, the differing numbers of voice and video packets, the differing means and variances of the two distributions, and the like). 
     In box  124 , a stream of values are generated by the lognormal number generator. As noted, the values can be a serial stream of packet interarrival times and/or a series of sets of values, namely a packet interarrival time and the number of packets corresponding to the packet interarrival time. The number of values outputted by the number generator  66  is typically the same as the total number of voice and/or video packets routed by the ATM switch during the selected time interval. 
     In box  128 , the composite traffic stream of data packet interarrival times (from box  116 ) and the stream of voice and video packet interarrival times (from box  124 ) are combined to produce a synthetic traffic stream  132 . The synthetic traffic stream  132  can be used to design the various components of the ATM network. For example, the traffic stream  132  can be used to determine the required number of buffers and/or buffer capacity, the desired transmission speed of packets, peak delay of traffic stream and optimum traffic mix (e.g., voice, video or data) of an ATM traffic channel. 
     EXPERIMENTAL 
       FIGS. 4A  and B present actual data taken from an ATM network. The network was serviced by a Fujitsu FETEX-150™ multi-service switching platform providing ATM switching services in the network. The host ATM was implemented using self-routing modules in a multi-stage network. It provided switching functions and served as the center for call processing and operations, administration, maintenance, and provisioning. Two broadband remote switching units in the network contained the customer interfaces and performed line concentration functions. Three customer sites were connected to the ATM network in a physical star configuration via Synchronous Optical NETwork (SONET) fiber links operating at 622.08 Mb/s (OC-12 rate). 
     Forty-one files were obtained, fifteen of which were corrupted and unusable. The data was collected during eight data collection sessions on four different days over a four month period. Busy hour sampling was performed because packet interarrival processes were non-stationary. 
     The data files were uncompressed and processed with a statistical analysis program. The statistical analysis program provided a file with the number of data cells, data bursts, interarrival cells, and interarrival bursts in the data. The file also contained the traffic data stream itself represented as a column of integers. The traffic data stream from each file was separated into three files: (i) the complete traffic data stream, (ii) the data cell traffic stream, and (iii) the interarrival cell stream. The data files were input into MATLAB™ for analysis. 
     The individual files within a session were analyzed individually and then concatenated and analyzed collectively. Since the results from the eighth session were representative of the entire body of data and since this was one of the larger data sets, the results from this session will be discussed below. 
       FIGS. 4A  and B are histograms of the interarrival times for this session. As can be seen from  FIGS. 4A  and B, the histogram appears as a mixture of two distributions: a large lognormal distribution  150  for packet interarrival times of about 0.3×10 −4  seconds or less and a much smaller normal distribution  154  for packet interarrival times exceeding about 0.3×10 −4  seconds. The much smaller normal distribution  154  caused by the switch input buffer filling up. These delayed packets form queues which are similar in length and distribution to Ethernet packets (which have normally distributed packet interarrival times). The majority of the interarrival times were very short in length, with the mean interarrival time being approximately 0.3×10 −4  seconds. 
     Model fitting was performed to characterize the curve defining the data in  FIG. 4B . The following model was developed: F(x)=Ψ·Λ(μ a .δ 2   1 ,)+(1−Ψ)·N(μ 2 ,δ 2   2 )
         where the mixing parameter, Ψ, is about 0.97,   μ 1 , the mean of the lognormal distribution  150 , is about −12.0156,   δ 1   2 , the variance of the lognormal distribution  150 , is about 1.3850.   μ 2 , the mean of the normal distribution  154 , is about 6.1293×10 −5 , and   δ 2   2  the variance of the normal distribution  154 , is about 1.6464×10 −5 .       

     Using the means and variances of the model and the sample size of  FIGS. 4A and 4B , the data in  FIG. 5  was generated using lognormal and normal random number generators in MATLAB™. A comparison of  FIGS. 4A and 5  demonstrates the close correlation between the actual and synthetic data. Of course, a simple moment matching model will not perform well in capturing the burst pattern characteristics of the data. An algorithm which synthesizes the buffering and transmission characteristics of the sending and receiving mechanisms would produce burst patterns similar to those of real traffic. 
     In the model, the mixture parameter, Ψ, is dependent on (i) the speed at which traffic enters and leaves the switch, (ii) the priority of the traffic, and (iii) the size of the switch input buffers. As the transmission speed and/or buffer size increases, the parameter Ψ tends to 1 and the traffic distribution tends to total lognormality. 
     Another ATM local area network was designed and built for the purpose of investigating the architecture and management algorithms appropriate to the local area. The network architecture is a manageable network, i.e., both the network resources and resource demands made by traffic are identifiable and quantifiable. 
     An ATM camera was set up to transmit 25 frames per second, JPEG compressed, 24 bits per pixel color video from a regular television transmission. The ATM camera transmitted cells to a network port controller which performed the traffic measurements, and from there to a Sun Sparc 10 workstation which displayed the video. The traffic trace is the first 1000000 cells of transmission, which included both action scenes (an explosion) and relatively static portions when credits were rolling on the screen. 
       FIG. 6  is a histogram plot of the camera data. The histogram of traffic interarrival times is heavy tailed and contains a relatively small normal distribution  160  after main lognormal distributions  170   a–c . The peak in the tail  160  is around 0.225 msec, which is nearly four times the magnitude of the peak in the tail  154  of  FIG. 4A  (around 0.06 msec). The input buffers of the ATM switches in both requirements were 128k bytes. The higher egress speed of the architecture in the first experiment allowed the buffers to clear faster, which resulted in less cell delay and a lighter tail distribution. 
     Model fitting was performed to characterize the curve defining the data in  FIG. 6 . The following model was developed for the curve which had three lognormal distributions  170   a–c  and one normal distribution  160 :
 
 F ( x )=(0.20 Ψ)·Λ(μ 1 ,δ 2   1 )+(0.20 Ψ)˜Λ 2 (μ 2 .δ 2   2 )+(0.60 Ψ)˜Λ 3 (μ 3 ,δ 2   3 )+(1−Ψ)· N (μ 4 ,δ 2   4 )
         where the mixing parameter, Ψ, is about 0.98,   μ 1 , the mean of the first lognormal distribution  170   a , is about −11.5784,   δ 1   2 , the variance of the first lognormal distribution  170   a , is about 0.5194,   μ 2 , the mean of the second lognormal distribution  170   b , is about −10.3165,   δ 2   2 , the variance of the second lognormal distribution  170   b , is about 0.1997,   μ 3 , the mean of the third lognormal distribution  170   c , is about −9.3908,   δ 3   2 , the variance of the third lognormal distribution  170   c , is about 0.3095,   μ 4 , the mean of the normal distribution  160 , is about 2.2546×10 −4 , and   δ 4   2 , the variance of the normal distribution  160 , is about 2.1980×10 −5 .       

     The first and second lognormal distributions  170   a  and  b  were each deemed to be 20% of the total lognormal distribution  170   a–c , and the third lognormal distribution  170   c  was deemed to be 60% of the total lognormal distribution  170   a–c.    
       FIG. 7  is a histogram generated with the MATLAB™ lognormal and normal random number generators using the means and variances in the model and the sample size in  FIG. 6 . As in the case of  FIGS. 4A  and B and  5 , the computer generated data in  FIG. 7  closely correlates with the actual data in  FIG. 6 . 
     The foregoing description of the present invention has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit the invention to the form disclosed herein. Consequently, variations and modifications commensurate with the above teachings, and the skill or knowledge of the relevant art, are within the scope of the present invention. By way of example, the architecture of  FIG. 2  could have a number of lognormal and/or normal random number generators operating in parallel on differing portions of the packet stream. This may be the case for data, voice, and video packets or different lognormal distributions within a packet type or among packet types such as those in  FIG. 6 . Alternatively, the lognormally distributed interarrival times for voice, video and data packets can be replicated using a single lognormal random number generator. The embodiments described herein above are further intended to explain best modes known for practicing the invention and to enable others skilled in the art to utilize the invention in such, or other, embodiments and with various modifications required by the particular applications or uses of the present invention. It is intended that the appended claims be construed to include alternative embodiments to the extent permitted by the prior art.