Realtime ATM Traffic Generation

Transcription

1 Proceedings of the 3 rd International Workshop on Protocols for Multimedia Systems (PROMS 96) Oktober 1996, Madrid Realtime ATM Traffic Generation Christian Schuler <schuler@fokus.gmd.de> Research Institute for Open Communication Systems GMD FOKUS, Berlin, Germany Abstract The evolution of new networking protocols has shown, that the conventional traffic models like Poisson are in many cases not suited for simulating real network behaviour [4]. This is especially true for ATM networks, because they will show a high complexity in their data rate dynamic due to statistical multiplexing of data, audio and video traffic. On the other hand realistic traffic models are necessary for many network engineering tasks like buffer dimensioning, quality of service estimation and resource planning. In this paper we propose an advanced approach to ATM traffic generation. We make extensive use of parallel processing in order to apply more realistic traffic models in realtime. The main components of the implementation concept are a standard Sun Sparc Station and a custom built ATM hardware. Our concept enables the user to change or modify the traffic model without having to deal with details of the ATM protocol processing or cell generation. Two examples of realtime traffic generators for source and link level traffic are demonstrated: an MPEG cell stream generator and a generator for self-similar ATM traffic. 1. Introduction An important analysis method for the development and testing of high speed network equipment is the use of traffic generators in offline simulations and realtime measurements. It helps to understand the high dynamics and the internal mechanisms of high bitrate transmission protocols. Engineering decisions like buffer dimensioning, needed processing power, clock frequency or hardware/software partitioning need a reliable basis. In many cases this can rather be obtained by extensive simulation than by formal methods.

2 C. Schuler The simulation and even more the realtime generation of ATM cell streams is only possible with a very high expense of processing power, because at a data rate of 155 MBit/ s about 3. cells need to be computed per second. In contrast to the more common simulation approach we make use of custom built ATM hardware. It is able to generate ATM traffic according to any desired deterministic or statistic model. In chapter 2 we will describe the outlines of the implemented hard- and software concepts. The traffic characteristic of the expected network input load is an important factor for the overall system performance. In a future ATM network the network traffic will consist of a mixture of data, video and audio traffic running different protocols and service classes. This new composition will probably lead to new traffic models, which can be devided into two main classes: source level models and link level models. The first class describes the emitted traffic of a single source, which could be e.g. a video codec, a workstation or a gateway to an Ethernet LAN. Our definition of source in this case means an entry point to the ATM network as defined by the ATM Forum UNI specification. The second class describes the aggregated traffic inside the network, i.e. the traffic on the links between the switching nodes. In chapter 3 we will exploit two examples, which represent the two classes of source and link level models. 2. System Architecture Today there are many more or less accurate mathematical models for network traffic, but in many cases they are not suitable for realtime traffic generation because of their complexity. In ATM the time between two cells is in the order of 3 µs on a 155 MBit/s link, and this time interval is too short to calculate the next cell departure time with a complex algorithm. One solution to this problem is to generate the cell stream in advance, but this approach would run into the problem of handling very large data files. For ATM with a line data rate of 155 MBit/s we would need up to 17.5 MByte to store the cell stream of only one second. A specialized hardware would be needed for the playback of the cell stream taking into consideration the calculated cell departure times. A second major problem arises from the fact, that it is common practice in ATM measurements to install a loop back of the test cells at the far end of the ATM connection. Therefore the test generator must additionally be able to generate test cells with sequence number and timestamp in less than 3 us. The accuracy of the cell departure time should be of the same order of magnitude. The returned cells are analyzed to determine cell loss and round trip delay. This analysis must be performed in parallel to the traffic generation. It represents an important difference to common load generators, which are used to saturate the system under test with background traffic. To perform the described tasks in realtime, we used the workstation based ATM test system described in figure 1. The hardware has been developed during the BATES project at GMD-FOKUS [2] and consists of four external boards: the Host Interface, the Transmitter, the Receiver and the Physical Layer. The connection to the host Sun workstation is supplied by an SBus adapter card with DMA access to the host s memory. Each board contains a TI32C4 Transputer and additional hardware like XILINX FPGAs, Content

3 Realtime ATM Traffic Generation Addressable Memories, various PLDs and FIFOs. The communication between the boards is realized via the transputer links and an 8 bit wide data path interface. By this concept the firmware and the XILINX designs can be downloaded during the system boot procedure within 5 to 1 seconds. R E C E I V E R 8 Receive CPU Host Interface 32 Interface CPU Physical CPU Physical Layer Interface 8 8 Transmit CPU T R A N S M I T E R 8 Fig. 1. System Hardware Concept Figure 2 shows the corresponding software concept for the traffic generator. In our realization the actual traffic modeling is separated from the ATM cell generation by calculating most of the parameters in advance before starting the actual simulation run. While the modeling software is realized in form of C programs on the SparcStation, the ATM cell generation is performed on the specific hardware platform by TMS32C4 transputers. To prepare the traffic description data for use in the firmware, an intermediate format in form of a table is generated on the host computer. This synthesis table is transferred to the target TMS32C4 processor on the transmitter board by SBus DMA and a transputer link communication protocol. The analysis of the returned test cells is done on the BATES receiver board, which evaluates the timestamps and sequence numbers to calculate the occured cell loss and delay values. In the following we concentrate on the traffic generation and assume a working analysis hard- and software to obtain the desired measurement results.

4 C. Schuler Traffic Modeling Software Host Software Table Generation BATES Firmware Realtime Cell Generation Fig. 2. Traffic Generator Software Concept The firmware part of the traffic generator emitts cells similar to the forthcoming O.191 standard [1], which is currently under discussion at the ITU. The suggested test cell layout is displayed in figure 3. This type of cell can be generated by the firmware with only 4 instructions on the BATES transmitter board. The resolution of the timestamp is 1 ns. This does not correspond to the actual measurement resolution, which is less accurate in most cases. For the BATES system it is better than 2 µs. This is less than the minimum cell interarrival time on a 155 MBit/s link and will be sufficient for most measurements. ATM Header Seq. Nr. Timestamp Pad xaa 5 Byte 3 Byte 4 Byte 41 Byte Fig. 3. Test Cell Structure To capture all desired traffic parameters, there are 3 types of synthesis tables, which correspond to different firmware traffic generators: the multichannel generator, the equidistant interarrival generator and the burst generator. The appropriate generator is selected with respect to the desired traffic characteristics. The maximum table size is limited by the size of the local RAM on the transmitter board, which allows to store table entries. The tables are repeated periodically, while the actual length of the table period depends on the parameter settings. This fact plays an important role when analyzing low cell loss values, which is the case in the second example of chapter 3.

5 2.1 Multichannel Generator Realtime ATM Traffic Generation This format contains cell header and interarrival time for each cell. It is well suited for traffic consisting of many different VPI/VCI combinations. The major disadvantage of this generator is the fact, that the traffic pattern is repeated after cells. This could be a very short period for higher data rates. 2.2 Equidistant Interarrival Generator The next two generators devide the time axis in short bin periods of constant size, e.g. 1 ms. The table format contains the interarrival time and the number of cells for each bin. The desired number of subsequent cells with the corresponding interarrival time is generated. The cell header is constant. The cells are equally distributed over the bin period: Cells c i = 8 t bin Time Fig. 4. Equidistant Interarrival Algorithm Input parameters are a list of N cell counts c i (generated by the external program on the host) and the length of the bin period t bin. Depending on the mean cell count value c the mean cell rate λ can be derived by the formula: λ = c, with t c 1 = --- c N i - bin N i = Burst Generator The table contains the interburst time and the number of cells. The desired number of subsequent cells with the intra-burst interarrival time is generated. Then the generator waits for the interburst time before sending the next burst. The cell header and the interarrival time during the burst are fixed parameters. The table size corresponds to the total number of bursts. Figure 5 explains the relation of the parameters:

6 C. Schuler Cells c i = 8 t bin Interburst Time Time Fig. 5. Burst Interarrival Algorithm The algorithm expects a list of N cell counts c i (generated by the external program on the host) and the length of the bin period t bin, but additionally a burst rate parameter can be defined. The realtime traffic generator emitts the input cell count with the burst rate at the start of a bin period. During the interburst time no cells are generated. An error occurs, if the burst at the desired rate would be longer than a bin period. 3. Examples Now we will take a look at two examples for realtime traffic generation, one for source level and one for link level modeling. Both examples are implemented as external traffic generators. This means that a standard program written in an arbitrary language can be used to generate a sequence of cell counts, which is read by the table generator. 3.1 Source Level Traffic Model: MPEG Cell Stream Generator The first example for a realtime traffic generator allows the generation of cell streams, as they would be created by a VBR MPEG codec using AAL5. The generator makes use of existing MPEG statistic tools, which are available in various places in the internet. After the frame analysis of the MPEG coded data contained in a *.mpg file, the frame data is read by the mpeg_gen program and converted to cell counts. Under the assumption, that one MPEG frame corresponds to one AAL5 PDU (8 byte AAL5 trailer, 48 byte ATM payload), the exact cell count is calculated by the formula: c = ceil( ( ceil( b 8) + 8) 48) with b - frame size in bits c - frame size in cells While the external program produces the desired number of cell counts each corresponding to an MPEG frame, the bin period described above is determined by the frame rate of the video. E.g. for 3 frames/s the bin period is set to 33 ms. The MPEG generator

7 Realtime ATM Traffic Generation can be used with both equidistance and burst mode. The two modes would correspond to the presence or absence of traffic shaping at the output of the video-to-atm codec. The burst rate could be imagined as maximum possible rate, at which a codec is able to encode the frames. The two examples show the recorded ATM traffic at the output of the traffic generator. The bandwidth/time display in figure 6 (a) shows a regular structure caused by intra-frame compressed I frames and inter-frame compressed B and P frames. It should be noticed, that this structure is not an inherent part of the MPEG standard. E.g. some codecs might generate data streams containing only I frames. The measured ATM bandwidth distribution shows, as expected, the same shape as the input video frame size distribution. Bandwidth [Mb/s] Time [s] P Bandwidth [Mbit/s] Fig. 6. MPEG Cell Stream, (a) Time Sample (b) Bandwidth Distribution With the time scale of figure 6 the difference between equidistance and burst mode is not visible, but of course it could have strong effects on the switch behaviour. Figure 7 shows a measurement with the higher resolution of 1 ms. The bursts in the first graph are sent with a burst data rate of 1 MBit/s and contain exactly the same MPEG frames as the second example. In the burst mode the size of the frame determines the length of the burst.

8 C. Schuler 2 Bandwidth [Mbit/s] Time [s] 12 Bandwidth [Mbit/s] Time [s] Fig. 7. MPEG Cell Stream, (a) Burst Mode (b) Equidistance Mode 3.2 Link Level Traffic Model: Self-Similar ATM Traffic The second model we present is intended to generate link level ATM traffic generated by a comparatively large number of independent users. Recent network traffic studies [5,8,1] have shown, that real traffic can much more faithfully be modeled by self-similar network arrival processes than by the traditional Poisson processes. One of the most interesting properties of self-similar processes is the long-range dependence (LRD), which can be described by a nonsummable autocorrelation function. A particularly attractive feature of self-similar models is the fact, that the LRD can be described by a single parameter, called the Hurst parameter H. If self-similar models are applied to network traffic, the LRD results in a typical behaviour when the bandwidth is averaged over various time periods. For independent random processes the average bandwidth will converge to a constant value for increasing average periods. In contrast for self-similar network traffic there will remain significant ripples regardless of how long the average period will be.

9 Realtime ATM Traffic Generation For self-similar network traffic time samples of e.g..1 s, 1 s, 1 s..., will look quite similar. This can be described statistically by the variance of the momentary bandwidth: in contrast to independent random cell streams the variance will not decrease with increasing average periods by the factor 1/m, where m is the number of elements averaged. The variance of a self-similar sample will fall off by the function: σ 2 ( m) = m β, β = 2( 1 H) where H is the Hurst parameter. Loosely spoken, the cell stream will be bursty over all timescales. This explains the higher cell loss values with increasing Hurst parameter, which can be seen in figure 1. The self-similar cell stream generator we want to introduce, is based on a standard C portation of the Fractional Gaussian Noise (FGN) generator described in [9]. It is one of the fastest known methods and rather easy to implement compared to other algotithms [2,5,7]. While the original program generates a sequence of positive and negative real values, we face the problem of converting these values to appropriate cell count values without destroying the self-similar properties. An unresolved problem in this context is the actual bandwith distribution of real world ATM traffic [4,5,6]. While for a sufficiently large number of users, each occupying a small bandwidth share of the whole link, the central limit theorem will apply. I.e. the distribution will converge to a Gaussian distribution. But the question is 1) whether the number of users will be large enough and 2) the value of the variance σ 2 will probably depend on many factors. The distribution problem can be solved by matching the distribution function of the FGN generator to a measured distribution function by the wellknown Transformation Method [11]. In this paper we will use a Gaussian distribution as an example. The first step is to shift the output values to an appropriate range so that no negative values will occure. In our example we used a mean cell count of 1 cells and a variance of 1, which corresponds to a mean data rate of 42.4 MBit/s for a bin period of 1 ms. For the Equidistance Interarrival Model described in chapter 2 this means, that the data rate will change every millisecond. This parameter is the accuracy of our traffic model. Burstiness in the range below 1 ms will not be captured by this model. The second step is to convert the real values into cell counts. The resulting signal is rounded according to the IEEE754 rounding convention ( C math library function rint). To ensure that the cell stream is still self-similar after both transformations, we generated a variance-time plot, which is shown in figure 8. The graph shows the decrease of the variance with increasing aggregation m after a log1 transformation. Obviously the transformations didn t change the variance or correlation structure and the resulting cell stream still has the self-similar properties of FGN.

10 C. Schuler H =.8 ideal -1 log1(var) -2 independent random noise sample 1 2 log1(m) 8 N Cell Count Fig. 8. Self-Similar Cell Stream, (a) Variance-Time Plot (b) Cell Count Distribution With the integer cell count sequence obtained by the last two steps we are able to progress the same way as in our last example. The cell counts are distributed over constant bin periods either by the equidistant interarrival algorithm or by the burst interarrival algorithm, which are both described in chapter 3. While the mean data rate of the cell stream can be controlled by the mean value of the FGN and the bin period, the overall burstiness is controlled by the variance parameter. The second graph of figure 8 shows the resulting cell count distribution for a mean/variance of 1./1.. This will result in a mean data rate of 42.4 MBit/s for a bin period of 1 ms. In figure 1 we show the measurement results for the test configuration of figure 9. The generated self-similar cell stream is looped back by a FORE ASX-2 switch. The Usage Parameter Control of the VCC connection is set to 1 cells/s CBR with a Cell Celay Variation Tolerance (CDVT) of 1 µs. I.e. for periods shorter than 1 ms a higher bandwidth up to the link rate is allowed.

11 Realtime ATM Traffic Generation BATES ATM Test System Cell Stream Generator ATM Switch Switch UPC Analyzer Fig. 9. Loopback Test Configuration In figure 1 (a) the received bandwidth and the absolute cell loss values are displayed over the complete measurement time of 32 seconds. It must be considered, that the measurement resolution on the receiver side is 5 ms. I.e. the bandwidth is averaged over 5 bin periods, while the lost cells are accumulated. The overall cell loss is determined by the variance parameter and by the Hurst parameter of the FGN. The graphs of figure 1 (b) show the Cell Loss Rate CLR for different cell count variance and Hurst parameter values. A cell count variance of 1 corresponds to a bandwidth standard deviation of 4.24 [MBit/s] for a bin period of 1 ms. For all cases the CLR increases with increasing LRD and bandwidth variance. The horizontal lines for variance values of 5 and 1 show the CLR for independent random bandwidth, which corresponds to an H value of.5. For a variance of 1 or lower no cell loss occurs with independent random generators. These results are particularly interesting for quality of service estimations of ATM connections. Recent studies of network traffic showed, that the Hurst parameter of VBR video cell streams [5] or ethernet traffic [8] will be in the range of.75 to.85. This means, that for the lower variance values cell loss will occure for real traffic, while it would not have been expected with simulations using traditional Poisson models. For the lower loss rates it must be considered, that the maximum period of the self-similar cell stream is 3.2 Mio. cells with the described configuration. Therefore the cell loss rates lower than 1e-6 are very inaccurate, which explains the spikes in the graphs of figure 1 (b).

12 C. Schuler Bandwidth [Mbit/s] lost cells Time [s] -2 V = 1 log loss rate -4-6 V = 5 V = 1 V = Hurst Parameter Fig. 1. (a) Cell Loss with Switch UPC (b) Correlation between CLR and Hurst Parameter 4. Conclusion There are several reasons, which lead to realtime traffic modeling as an attractive solution. One is the exploitation of traffic models, which could not be simulated due to limited CPU time. Especially long range dependencies and rare events like cell loss in ATM networks during normal load periods can be analyzed much faster by the use of realtime traffic generators and analyzers. This method would reduce the needed simulation time by a factor of more than 3. E.g. the simulation of 1 mio. cells with a self-similar traffic model takes about 1 seconds on a Sparc2, while it takes only 3 seconds to send the same cell stream in realtime.

13 Realtime ATM Traffic Generation A second reason to use realtime traffic modeling is the possibility to study the effects of the model parameters in a real network environment. The evaluation of new traffic models on existing testbeds and networks can be used in parallel to simulation and analytical approaches. Real network components can be used and very long measurement periods of hours or even days become possible. Instead of having to model a complete ATM switch or even a cloud of ATM switches with our hardware system we are able to directly use an existing ATM network or testbed. This is an important factor, because modern ATM switches implement many complex algorithms like leaky buckets, policing and shaping functions, early packet discard and overbooking. References [1] ITU-T Draft New Recommendation O.191, Equipment to assess ATM layer cell transfer performance, Geneva, March 1995 [2] D. Elias, BATES - BERKOM ATM Technology Evaluation System, 7th IEEE Workshop on Local and Metropolitan Area Networks, Marathon, 1995 [3] P. Flandrin, Wavelet Analysis And Synthesis of Fractional Brownian Motion, IEEE Transactions on Information Theory, 38(2), pp , March 1992 [4] V.S. Frost, B. Melamed, Traffic Modeling For Telecommunications Networks, IEEE Communications Magazine, March 1994 [5] M. Garrett, W. Willinger, Analysis, Modeling and Generation of Self-Similar VBR Video Traffic, Proceedings of ACM SIGCOMM 94, 1994 [6] D.P.Heyman, Statistical Analysis and Simulation Study of Video Teleconference Traffic in ATM Networks, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 2, No. 1, March 1992 [7] W-C. Lau, A. Erramilli, J. Wang, W. Willinger, Self-Similar Traffic Generation: The Random Midpoint Replacement Algorithm and It s Properties, ICC 95, 1995 [8] W. Leland, M. Taqqu, W. Willinger, D. Wilson, On the Self-Similar Nature of Ethernet Traffic, IEEE/ACM transactions on Networking, February 1994 [9] V. Paxson, Fast Approximation of Self-Similar Network Traffic, Berkeley, 1995 [1] V. Paxson, S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling, Proceedings of ACM SIGCOMM 94, 1994 [11] W.H. Press, B.P Flannery, Numerical Recipes in C, Cambridge, 1988