SIMA Network Traffic Control System

Transcription

1 Quality of Service Analysis for a Simple Integrated Media Access Network Avadora Maicaneanu and Jarmo Haru Tampere University of Technology, Telecommunications Laboratory Abstract In this paper the quality of service requirements are studied in a simulated Simple Integrated Media Access (SIMA) network which is one concrete specification for service differentiation in IP networks. The simulations use measured voice and video traffic traces and computergenerated background load traffic. Both SIMA access and core network nodes are taken into consideration in the study and the choices of values for SIMA parameters are discussed. Simulation results under various conditions are presented and a procedure to determine the SIMA packet drop preference table - based on traffic statistics - is proposed. Keywords : differentiated services, SIMA, simulation Introduction There are a few dominant types of Internet traffic data, audio (voice), video and multimedia. These traffic types differ not only in their patterns (bandwidth, mean rate, variance, peak rate, burstiness, self-similarity) but mostly in their requirements. The various requirements including bounds in terms of delay, delay variation, loss probability, bandwidth, throughput, or relative priority of access to network resources group the traffic in two categories : real-time (RT) traffic (for which the delay and delay variance are more critical than packet loss) and non-real-time (NRT) traffic (for which the packet loss requirement prevails on the delay requirement). The audio, video and multimedia traffic falls in the real-time category while the data traffic falls in the non-real-time category. The traditional Internet service provides all customers with the same level of service (best effort service). The best effort service attempts to deliver as many packets as possible as soon as possible. This approach was effective when the customers had fairly the same expectations, used protocols that behaved similarly and paid similar prices. Once the network became congested and different new expectations for the quality of service emerged it became difficult to meet these heterogeneous expectations with the traditional approach. The Differentiated Services (DS) approach aims to provide a natural evolution path from the current best effort environment to a new environment capable to provide differentiated Quality of Service (QoS) without complicated and resource consuming network functions. Several variants to improve the best effort service have been proposed. In order to provide an effective mechanism for delivering the emerging wide range of services these variants should meet some general requirements besides the capability to assure a highly differentiated QoS: simplicity (of implementation, of management, of charging, etc.) high network utilization flexibility (the capacity to assure a wide range of services both for RT and NRT traffic with rapidly evolving requirements) and continuity (with respect to existing services) fairness (in resource division among users, in charging, etc.) incentivity (both users and providers should be implicitly rewarded for a correct/good behavior and punished for malicious /bad behavior) scalability reliability. The Simple Integrated Media Access (SIMA) [1] is a new approach towards effectively providing differentiated services. While the advantages of the SIMA approach can be extensively argued theoretically [2] the simulation results can provide very useful quantitative comparisons mostly since effective deployment and testing is difficult and very costly. The aim of this work is to analyze the QoS offered under the SIMA concept in a simulation environment using measured real-time traffic (voice and video) and a realistic background traffic load. SIMA concept In the SIMA the customer defines ust two issues: a nominal bit rate (NBR) and a selection between RT and NRT service type. From the provider s point of view the NBR forms the basis of charging and defines how the network capacity is divided among different connections during overload situations. In the meantime the user is not restricted to transmit only below his chosen NBR but can trade the speed for QoS. The domain over which the SIMA concept is implemented consists of access nodes and core nodes. The access nodes provide the interface with other network/customer equipment, perform traffic measurement for every flow and assign the droppreference (DP) bits for every packet. The traffic measurement performed in the access nodes establishes the momentary bit rate (MBR) of each flow at the arrival of each packet. Since the instantaneous bit rate can fluctuate dramatically from one packet to another an averaged measurement principle should be applied to calculate the MBR. For our calculations we used the exponential moving average principle [3] but any feasible MBR evaluation can be used. In the exponential moving average procedure the moving average of the load generated by each flow is calculated at the arrival of each packet using the formula: δ 1, ρ 1 α) ρ + α = ( 1 where ρ -1 and ρ are, respectively, the averaged loads induced by the measured flow at the arrival of packet -1 and (the initial value of the averaged load can be taken ρ 0 =(average bit rate)/c where C is the node forwarding capacity); δ -1, is the interval between the arrival of packets -1 and ; and α is a parameter related to the time

2 extension of the averaging process. The averaged value of the momentary bit rate is calculated as : C ln(1 -α) MBR = ln 1-α ρ ( ) The proper value for α depends on the buffer capacity reserved for the respective service class (RT or NRT) and on the traffic burstiness. From Fig. 1 it can be observed that as α decreases the averaging period and the delay with which the calculated MBR follows the instantaneous bit rate (IBR) increase. Fig. 1 Instantaneous bit rate and calculated momentary bit rates for different values of α (average bit rate for the bursty source is about 40Kbytes/s and the node forwarding capacity is 1Mbyte/s) The parameter α should be small enough to prevent unnecessary packet loss during bursts that can be accommodated by the buffer of the respective service. On the other hand α should be large enough so that during a long burst of sustained peak bit rate the calculated MBR reaches the instantaneous bit rate before the buffer reserved for the respective service is filled. Since the RT and NRT traffic have different requirements with respect to packet loss and delay, different burstiness and different buffer sizes, α should be chosen differently for the two types of traffic: RT traffic with limited burstiness and a small associated buffer is better suited by a rather large α - so that the calculated MBR follows closely the IBR - favoring early packet discarding during bursts and limited delay NRT traffic with high burstiness but also with a large buffer to accommodate the bursts is better suited by a small α - favoring late/reduced packet discarding during bursts but also increased delay It should be mentioned that α could be given different values for flows requiring the same service type if they have significantly different burstiness [4]. Once the MBR is calculated the DP value for the th packet is obtained from : DP = 0 if x <=0 int(x ) if 0<x <6 6 if x >=6 where x =.5 [ ln( MBR /NBR)] ln(2) 4 The DP values range from 0 (for MBR >16*NBR) to 6 (for MBR<0.35*NBR) and correspond to 7 different levels of service quality. Besides these, one supplementary level is used for non-sima services with resource reservation. Since the absolute service quality offered by each quality level depends on network dimensioning and on actual traffic the quality levels can only be approximately described as follows : 0 unusable during busy hours but suitable for best effort traffic during non-busy hours 1 suitable for best effort traffic during busy hours 2 satisfactory quality - from time to time very high packet loss ratio 3 moderate quality - usually small packet loss ratio except during busy hours 4 good quality - small packet loss ratio even during busy hours, corresponds to MBR NBR 5 high quality - packet losses only during exceptional traffic peaks 6 excellent quality - negligible packet loss ratio 7 reserved for non-sima services with resource reservation Besides the three bits used for DP a fourth bit in the type-of-service (ToS) field of the packet is used to indicate the user selection of RT or NRT service i.e. delay indication (DI) bit. Once the DP and DI bits are placed in the header of the packet all subsequent traffic control functions performed by the SIMA core nodes are based ust on these bits. Since the node capacity partition must be based solely on the MBR/NBR ratio for each flow and on the overall load, the discarding decision, made in the SIMA core nodes, is taken irrespective of the delay indication. The drop decision is based on a comparison of DP with the accepted drop preference (adp). When the packet has been accepted it is inserted in the corresponding queue based on the value of the DI bit (0 for NRT and 1 for RT). In order to obtain a minimum delay for the RT packets the policy in treating the two FIFO queues is that a packet is taken from the NRT queue only if the RT queue is empty. This policy generates the threat that the RT traffic blocks the NRT one. To exclude NRT traffic blocking the adp is derived from the occupancy of both the RT and NRT buffers (overall buffer load). It should be noted that the adp could be calculated differently depending on the service-type required by the processed packet. Only the adp calculation for RT packet processing should consider the occupancy of both buffers while the adp calculation for NRT packet processing could take into consideration ust the NRT buffer occupancy, thus decreasing the NRT packet loss probability. However, for simplicity, we considered that the adp calculation procedure is indiscriminate of the service type required by the processed packet and takes into account the overall load. The sizes of the buffers used to implement the two FIFO queues depend on the aimed QoS, on the traffic burstiness and on the node forwarding capacity. The RT buffer should be dimensioned so that in the worst case (when the buffer is full) the delay remains below the maximum accepted delay. Consequently the RT buffer length should be small since the maximum accepted delay

3 is small and the node forwarding capacity is limited. The NRT buffer length should be calculated so that it enables the accommodation of a NRT burst at peak bit rate during the longest interval of exclusive RT packet forwarding. Consequently the NRT buffer should be significantly longer than the RT buffer. Various formulas for overall buffer load calculation can be taken into consideration : a) x = x_rt + x_nrt b) x = max { x_rt, x_nrt } c) x = 2 x _ RT + x _ NRT where x_rt = RT buffer occupancy x_nrt = NRT buffer occupancy Several adp(x) functions giving values in the 0-6 range are possible depending on how was the overall buffer load calculated. The condition for accepting the packets is that their DP is greater than the momentary value of adp : DP >adp(at th packet arrival). It can be seen that the drop decision procedure excludes NRT traffic blocking by RT traffic. If there is constantly at least one packet in the RT buffer the NRT buffer fills up so that x increases, the adp increases and more RT packets will be dropped since the acceptance condition will be less likely satisfied. Eventually the RT buffer will be emptied due to this process releasing the NRT packet processing. Even if the RT traffic exceeds the link capacity a similar decrease in acceptance condition likelihood (due to RT buffer filling) will ultimately prevent complete NRT traffic blocking. A theoretical analysis of the SIMA concept reveals that all the general requirements to provide effective DS are met to a reasonable extent. SIMA obviously offers simplicity in charging (that can be based ust on the NBR), in implementation and in management. SIMA aims at the maximization of network resources exploitation : all flows with different QoS requirements share the total capacity of every link in a controlled manner; the network attempts to avoid any unnecessary packet discarding; no flow signaling is required and the flow level blocking is avoided. The user doesn t have to know many technical details but ust has to pay a low price (low NBR) initially and gradually increase the paid price until a satisfactory QoS is obtained. Furthermore, as mentioned, the user can dynamically trade speed (bandwidth) for QoS by adusting the MBR/NBR ratio. On the operator side in order to adust the network capacity the delay and packet loss ratio should be monitored and the capacity should be increased in the bottleneck regions where the delay and packet loss ratio systematically exceed the intended values. It is interesting to observe that the SIMA built-in incentivity offers a self-regulatory environment. The primary self-regulatory mechanism is related to the fact that the users behaving nicely (i.e. maintaining an average traffic within the limits of the purchased NBR) are rewarded by a superior QoS while those behaving maliciously (i.e. continuously and consistently surpassing their NBR) are punished by a lower QoS. Thus, for example, the benefits of maliciously sending redundant traffic during busy hours in order to obtain a better QoS are completely eliminated [5]. 2 Besides regulating the user behavior SIMA also regulates the differences between the provided QoS levels. Let s suppose that the users perceive that the QoS differences between different purchased NBRs do not ustify the difference in prices. This will induce many users to migrate towards smaller prices (and smaller purchased NBR). Since they probably will maintain similar traffic characteristics, the lower DP levels will become more loaded (due to increased MBR/NBR) and the QoS offered to them will decrease. The process will stabilize when the users perceive that the differences in the paid prices are ustified by the difference in QoS. The buffer dimensions together with the packet discarding policy provides incentive for requesting the right service type. While obviously there is no benefit from submitting RT traffic as NRT traffic because of the high delay it is not so obvious whether submitting NRT traffic as RT traffic would be beneficial or not. Although treating NRT traffic as RT will produce shorter delays, the small RT buffer will induce a high discard rate for bursty traffic. Consequently, in order to obtain the same QoS (throughput) for the same bursty NRT traffic, the user must pay for a significantly higher NBR when the traffic is incorrectly submitted as RT. In this respect RT service is more expensive than NRT service for the same source and the same packet loss ratio even if the RT and NRT tariffs for the same NBR are the same. However, if the traffic variations are small enough the user may always select a RT service since this will not induce a difference in packet-loss ratio. While the SIMA doesn t enable the operator to guarantee the continuous availability of NBR it still assures fairness in the sense that all flows with the same MBR/NBR ratio experience similar QoS. It has to be observed however that the fairness is assured only to service requests that are active at the same time. During a longer period fairness could be assured by a charging scheme that considers the length of each user s active periods. Furthermore the average QoS delivered to users with the same NBR and active period length will be different depending on whether the users requested service during busy or non-busy hours. A superior QoS for the same price and same active period will reward the users that were active more during the non-busy hours providing another incentive for nice user behavior. Simulation arrangement The general simulation scheme we have used contains two IP-Phones and two videoconference sources treated as RT traffic sources. They were connected to a SIMA access node that is also loaded by background traffic that was treated as NRT. Since the voice source packet length and delay depends very much on the processing algorithms (coding and packetization) the voice traces were collected directly at packet level using an IP protocol analyzer. The voice sources were two conversations using Selsius IP-based telephones that support G.711/G.723 audio compression for low bandwidth requirements. The measured packet inter-arrival time takes mainly two values (0.014s and 0.03s) and the packet length was found to have a constant value of 294 bytes. The average bit rate for the two voice sources was about 10Kbytes/s. Significantly more voice

4 traffic data (from different source types and various conversations) would provide a better basis for a statistical evaluation of voice traces and would enable more realistic simulations. Fig. 2 Overall simulation scheme The video traces were obtained from two videoconference sessions and were collected also using the IP analyzer. The sending equipment was a video camera using a Bitfield H261 video compression board (VCB) that digitizes and compresses according to the ITU-TSS recommendation H.261. The compression - done on the VCB is made using an algorithm designed for real-time two-way videoconferencing at low bit rates. The average length of the video packets was about 550 bytes and the average bit rate was about 26Kbytes/s. Many WWW studies conclude that the aggregated traffic exhibits self-similarity [6] [7]. The studies also argue that the self-similarity of Web traffic is not a machine or protocol induced artifact but is rather related to the nature of exchanged information and the request/send patterns [7] [8]. Therefore it could be expected that machine evolution or changes in protocol are not likely to remove the self-similarity. Furthermore it was observed that the higher the load the higher the traffic self-similarity [9] [10]. The long range correlation of self similar traffic has important consequences for network performances. Most of the consequences arise from the fact that unlike in the classical traffic models merging several self-similar traffic sources does not smooth the aggregate traffic which remains a bursty aggregate stream. Scale-invariant burstiness (i.e. self-similarity) introduces new complexities into network performance optimization and makes the task of providing QoS together with achieving high utilization difficult. The delay-bandwidth product problem arising from the combination of high-bandwidth traffic with QoS issues adds further complexities to the problem of performance optimization. The Fractional Gaussian Noise (FGN) method [11] was used to obtain a self-similar trace of arrived packets (N k ) in successive bin time periods. The mean number of packet arrivals was taken 225 and the bin time period (T) was considered 0.1s. Within each bin period the interarrival times were considered to have a Poisson distribution with the mean value equal to T/N k. The packet length was considered to have a geometrical distribution with mean value 500 bytes leading to an average bit rate of about 1.073Mbytes/s. The Hurst parameter describing the degree of self-similarity was taken 0.7 in accordance with the Hurst parameter value fitted to real aggregate traffic measurements [6] [7]. Although the aggregate background traffic should have been considered as a combination of NRT and RT traffic, we assumed it to be entirely NRT traffic - to have a clearer picture of the QoS offered to the four RT sources used in simulation. While the background traffic is an aggregate traffic the DP levels cannot be calculated with the described procedure because there is no way to establish the MBR values for the different flows in the aggregate. Instead a statistical DP distribution in the aggregate flow was assumed. The DP level probabilities were considered to be 0.75, 0.09, 0.06, 0.05, 0.025, 0.015, , for DP levels from 0 to 7 respectively. In order to analyze the QoS offered under congestion the SIMA core node forwarding speed to the outgoing link was taken 0.8Mbytes/s less than the average overall load of about 1.143Mbytes/s). We have considered that an overall delay of 0.1s is acceptable for RT service since it would imply ust a delay of a few video frames and since a round-trip delay of 0.2s was reported to induce negligible inconvenience in voice traffic [12]. We also assumed at most 8 hops on the path, giving an acceptable RT delay per hop of s. Consequently the RT buffer size was taken about 10Kbytes (20 packet slots) to assure a maximum delay per hop of less than s. An effective NRT buffer dimensioning should be correlated with the Hurst parameter and with the parameter α, which control buffer loading under congestion. However, since the worst case buffer dimensions are manageable, a worst case dimensioning was used. The NRT buffer size was calculated to accommodate a burst at the peak link rate (taken 10 times the core node forwarding speed) during the longest interval of exclusive RT packet processing (0.0125s). Consequently the NRT buffer size was taken to be about 100Kbytes (200 packet slots). Simulation results For nice RT user behavior (i.e. NBR chosen close to the source average bit rate) only negligible RT packet discarding was observed. The negligible RT discarding was due to the fact that while nice RT traffic got DP levels of about 4, the background traffic DP statistic assumed lower DP levels. Consequently under congestion the discarding was made only at the expense of the best effort traffic and maliciously behaving sources that exceeded their processing capacity quota (i.e. the dropped packets were almost exclusively those with DP=0 therefore either from best effort traffic or from maliciously behaving sources). Following this observation we used the assumption of malicious RT user behavior (i.e. requested NBR well below the actual bit rate) in the example simulation to analyze the QoS under adverse circumstances. Several earlier simulations indicated that the offered QoS (throughput and delay) and the node resource utilization depend on the adp calculation. For example when we used adp=6 [max{x_rt, x_nrt}] in a simulation considering ust best effort NRT background traffic we observed that the NRT buffer occupancy was kept below 16.67% and many NRT packets were

5 needlessly dropped. This was due to the fact that the adp function reserved 1/6 of the buffer for each DP level in increasing order. Once their share of the buffer was occupied (regardless whether other DP-level packets were taking space as well) the packets of a certain level are discarded to reserve the space for superior DP-level packets. To test our assumptions we changed the adp function to adp=6 [max{x_rt, x_nrt}] 2. As a result the maximum NRT buffer occupancy rose to about 40% (due to the quadratic DP-level distribution assumed) together with a significant increase in the NRT throughput. It has to be underlined that the RT service was not affected in the process : the RT throughput remained about the same and the delay was kept in the acceptable limit (imposed by RT buffer dimensioning) although it increased due to increased RT buffer occupancy. This results let us to the conclusion that DP statistics should be taken into consideration when designing the adp function. Furthermore, in order to avoid adp calculus delay, a table giving the adp value depending both on RT and NRT buffer occupancy can be used. The table can be derived assuming that both the RT and NRT buffers are divided into a number of buckets whose number and size can be different for RT and NRT buffers. The number of buckets should be bigger than the number of DP levels (8), and the buckets should be at least as long as the longest packet. An example of such adp table is given in Table 1. It can be observed that the table was generated under the assumption that the DP probability distribution was 0.333, 0.167, 0.083, 0.083, 0.083, 0.083, 0.083, for both types of traffic. RT NRT Table 1 Accepted drop preference table calculated for 12 buckets in both the RT and NRT buffers The delay and throughput information results from the simulation together with the corresponding NBR values for the RT sources are presented in Table 2. NBR Mean Max. Pack. Pack. dropped delay delay sent IP IP VC VC NRT Total Table 2 Simulation parameters (NBR) and simulation results (NBR are given in Kbytes/s, and delays are given in miliseconds) It has to be remarked that all the discarded packets had DP=0. Therefore this simulation showed once more that under congestion the traffic that is affected is either the best effort one or the traffic from maliciously behaving sources. It is obvious that sources IP1 and VC1 behaving more maliciously (i.e. having a higher average MBR/NBR ratio) than their counterparts - IP2 and VC2 - were punished more severely in terms of throughput. Also it can be observed that since the congestion was not acute (i.e. it could be solved by discarding ust DP=0 packets) the VC2 source, whose behaviour led to an average DP level of about 2, was not punished at all. In the meantime the delay parameters were not affected by the source behavior all RT sources experiencing a maximum delay well below the maximum accepted delay. The reduced maximum delay was due to the limited RT buffer occupancy and the differences in RT traffic delay parameters are simply related to the statistical nature of the transfer process. The treatment experienced by the traffic from the different RT sources can be better illustrated by the DP level assignment given to the source packets. Source DP level IP1 IP2 VC1 VC Total Table 3 Packet distribution on the 7 SIMA DP-levels for the RT sources It has to be noted that using a given adp table like the one in Table 1 will ultimately impose the considered statistic on the traffic by the very self-regulatory mechanism provided by SIMA Therefore the network provider could use the adp table to influence the user behaviour. A procedure to generate the adp table from a measured or desired DP-level statistics could be : establish the RT and NRT number of buckets : Nr_RT and Nr_NRT (both should be greater than the number of SIMA DP levels) measure DP-level statistics or define the desired distribution of packets with respect to DP-levels generate first row and column of the DP table by distributing, respectively, the (Nr_RT 8) and (Nr_NRT 8) buckets according to DP-level statistics (each DP level gets a bucket by default) generate the rest of the DP matrix elements using the DP levels in the first row and column for 2 DP 0, example DP[i,]= [ ] [ ] 2 DP i, 0 + The procedure could be used periodically to adust the adp table if the traffic statistics was drastically altered. (Collecting the traffic statistics could also be used to adust the α parameter.) However, even if a such an adapted adp table would induce a better mean QoS for the traffic it was derived from, it is unclear (and difficult

6 to asses by simulation) whether a periodical adp table update would be beneficial on the long term. The simulations made for congestion situations indicate that although the buffer occupancy was significantly improved by an adp table better adapted to the DP-level statistics, the offered QoS did not change as dramatically. This was due to the fact that although the buffer occupancy saturated at higher values the extraction under congestion was determined by the speed at which the packets could be processed. With non-congested traffic or with traffic that allows for processing relaxation (both RT and NRT buffer are emptied frequently) a better adapted adp table might offer a really improved average QoS. obtained by using several delay indication bits, several corresponding RT buffers and a weighted round robin strategy to extract the RT packets from different buffers. The RT buffers dimensioning should in this case take into consideration the round robin procedure besides the node processing capacity. While many of the advantages and disadvantages of the SIMA concept can be theoretically investigated, the quantitative comparisons provided by simulations proved to be useful for a quantitative assessing. However it must be underlined that for a consistent evaluation of the SIMA concept a much more extended analysis (comprising significantly more simulations and the consideration of a more complex and realistic simulation context) is required. Acknowledgement The authors would like to thank Isern Jorge and Jukka Karalainen for providing the measured voice and video traces for our simulations. Fig. 3 Discarded packets per second over a sample 30 second period Fig. 3 presents the number of RT and NRT discarded packets over a sample period of 30 seconds. It has to be observed that the discarded RT packets were mostly isolated packets (rather evenly distributed in time) and only seldom small groups of consecutive packets. Since reported tests have found that random independent packet loss rates of up to 10% have little noticeable impact on G speech transmission and only loss bursts produce noticeable dropouts in the received signal, the SIMA approach is better in this respect than best effort Internet telephony that leeds to highly correlated packet loss [12]. Conclusions From the performed simulations there are also some general conclusions to be drawn. First it was shown that under congestion the DP levels are affected in increasing order thus insulating higher priority levels from traffic variations at lower levels. The throughput is increasing with NBR and as the NBR becomes greater than the actual bit rate the throughput saturates at 1. It is interesting to note that, since the real-time packet delay depends mainly on processing speed, the packet delay actually increases with increasing NBR but remains smaller than the maximum accepted delay for a correctly dimensioned RT buffer. Consequently choosing a NBR value significantly greater than the actual bit rate is useless with respect to obtained QoS. Since the QoS penalties and bonuses for this SIMA implementation were expressed ust in throughput rates it can be argued that the use of several RT delay classes could bring differentiation, bonuses and penalties in this respect as well. Different RT delay classes can be References 1. K. Kilkki, Simple Integrated Media Access, Internet Draft <draft-kalevi-simple-media-access- 01.txt>, Nokia Research Center, June J. Ruutu, K. Kilkki, "Simple Integrated Media Access - a Comprehensive Service for Future Internet", Performance of Information and Communications Systems (PICS'98), Lund, May K. Kilkki, "Simple Integrated Media Access (SIMA)", COST257 TD(97), Espoo, May M.V. Loukola, T. Engdahl, J. Forsten, Traffic Measurements Of the SIMA Access Node Implementation, 7 th International Conference on Telecommunication Systems, Modelling and Analysis, USA, March K. Kilkki, J. Ruutu, O. Strandberg, High Quality and High Utilization Incompatible Obectives for the Internet?, 6 th IEEE/IFIP International Workshop on Quality of Service, USA, May Z. Sahinoglu, S. Tekinay, On Multimedia Networks: Self-Similar Traffic and Network Performance, IEEE Communications Magazine, Jan M.E. Crovella, Self-Similarity in WWW Traffic: Evidence and Possible Causes, IEEE Trans. Networking, vol. 5, No. 6, Dec K. Park, G. Kim, M. Crovella, On the Relation Between File Sizes, Transport Protocols, and Self- Similar Network Traffic, Proc. IEEE Int l. Conf. Network Protocols, Oct W. Stallings, High Speed Networks: TCP/IP ATM Design Principles, Prentice Hall, V.S. Frost, B. Melamed, Traffic Modeling For Telecommunications Networks, IEEE Communications Magazine, March V. Paxson, Fast Approximation of Self-Similar Network Traffic, Bekeley, T.J. Kostas, M.S. Borella, I. Sidhu, G.M. Schuster, J. Grabiec, J. Mahler, Real-Time Voice Over Packet- Switched Networks, IEEE Network, Jan./Feb