MODELLING VOICE COMMUNICATIONS USING OPNET

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1 MODELLING VOICE COMMUNICATIONS USING OPNET Uma Jain, Mustafa Kocaturk, and Arvind Kumar Ericsson, Inc. 655 N. Glennville Dr., Mailstop J 04, Richardson, TX, U. S. A. {Uma.Jain,Mustafa.Kocaturk,Arvind.Kumar}@Ericsson.com ABSTRACT Voice over IP and Voice over ATM networks are the rage in telephony discussions these days. Traditional suppliers of telephony equipment are adapting/changing their product lines as fast as possible to compete in the new telecom world dominated by integrated voice/data networks. There has been a concurrent proportional increase in the research activity to find ways to model the new products/networks, with an aim to devise easy to use engineering guidelines for configuring these products/networks. This paper outlines the different criteria that characterize voice transmission carried over the traditional wireline or wireless circuit switched networks and the emerging integrated packet networks. We then describe our experiences in using OPNET to model the emerging communications scenarios. INTRODUCTION Up to the present time, human speech has been transmitted on a real time conversational basis, primarily using circuit switched networks, where a circuit or connection is dedicated to each conversation for the duration of the conversation. Voice communication systems have been around for more than a century and have been evolving toward integration with data networks through a variety of technological, economic and user requirement factors via modem technologies, Integrated Services Digital Network (ISDN), digital carriers and more recently via integrated services Asynchronous Transfer Mode (ATM) and Frame Relay (FR) backbones on the one hand, and the Internet and corporate intranets on the other. Traditional suppliers of telecom equipment for the public and the private/enterprise networks are naturally endeavouring to keep up with the changes taking place by supplying to their clients both a newer/changed set of products as well as the associated guidelines/explanations on how to evolve their respective networks. With the generally accepted predictions of the voice traffic becoming an insignificant fraction of the data traffic over the next few years, it goes without saying that voice traffic will be increasingly carried over data networks. These may be based on FR or ATM (for enterprise networks), or packetized voice over IP (VoIP) carried over a local area netwok (LAN), wide area network (WAN), or Internet. In this paper we outline some of the results of our recent attempts to model packetized VoIP using OPNET. It should be pointed out at the outset that even though we are discussing primarily VoIP (because that is what we have studied using OPNET thus far), there is in fact a need to generalize this discussion to all "Real Time Services over IP", including packetized audio/video, electronic commerce, teleconferencing, Web searching, multi media Internet Collaboration/Whiteboard Sharing etc. Much if not all of the topics covered in this paper apply in fact to all such "real time services over IP". PARAMETERS OF VOICE TRANSMISSION Parameters and criteria that characterize voice transmission have been defined in standards bodies by the telephone industry and administrations, keeping in mind the needs of a telephone subscriber (the customer) wishing to place a call and carry on a conversation. For the purposes of this discussion, these parameters will be called quality of service (QoS) parameters. One major QoS parameter is the probability that a call attempt will succeed. Alternatively, blocking probability can be used, which is one s complement of the former. Another important parameter is the quality of the telephone conversation, expressed using a standard metric such as the perceived speech quality (PSQM), or the mean opinion score (MOS) that indicates the intelligibility of speech. PSQM is obtained by objective measurements and evaluated according to ITU T Recommendation P.860. MOS is obtained by subjective listening tests, where a group of listeners with different linguistic backgrounds evaluate a series of speech samples by a score from 1 (unintelligible) to 5 (perfect). While both of the above QoS parameters are mentioned together, their values depend on relatively disjoint set of processes, hence there is little, if any, correlation between them. The boundary between unacceptable speech quality and a lost call is quite subjective and arbitrary, and depends on many factors, including the cost per minute of the call, level of interactivity, and status of the caller, among others [1]. Furthermore, a mapping between lost calls and lost subscribers is also possible, as described in the outage index methodology [2] developed by ANSI Working Group T1A1.2, which specializes on the Survivability Performance of 1

2 telecommunications networks. Blocking probability is usually calculated based on the assumption that calls are lost when all resources (lines) leading to the destination are busy and that they do not return. The number of callers is assumed to be infinite, but the total offered traffic intensity is finite. This is known as Erlang s Loss model. Other models can be better approximations to reality, where lost calls return, are held (as are talkspurts in time assignment speech interpolation TASI), or the number of sources is finite, but they lead to more complicated math. In a typical landline telephone service, one blocked call in 1000 (within an exchange) is considered acceptable and exchanges are dimensioned (engineered) accordingly. In mobile telephony, the allowed blocking probability at the air interface can be as high as 0.01 (1 percent). Traffic engineering or dimensioning are terms used to describe the processes and methods for determining the number of resources allocated for each component in the telephone network, based on the models mentioned above and the allowed probabilities of loss or delay in serving (setting up) calls. Typical requirements towards a circuit switched telephone network impose limits on call blocking probability at various network/node components and dial tone delay. There are also reliability requirements limiting down time for a node, a link, or the entire network. Speech quality is affected by impairments such as errors resulting in loss of speech samples, end to end or round trip delay, presence/delay/strength of echo, and clipping (due to TASI or silence suppression). The effect of delay on speech quality deserves attention: In an echo free highly interactive conversation, round trip delays exceeding ms can be acceptable, whereas 600 ms or more becomes annoying for those unaccustomed to it [1]. CIRCUIT SWITCHING VS. PACKET SWITCHING The two QoS domains mentioned above (blocking probability and speech quality), which are distinct in traditional (circuit switched) telephony, merge into a continuum of capacity and voice quality trade offs in packetized voice telephony. Traditionally, telephone network engineering has been based on circuit switching concepts, where a constant stream of bits is allocated to a call for the entire duration of the conversation. Economy is obtained by sharing pooled resources among randomly timed bids. Not all pools have a queue, and a certain amount of blocking (loss) of bids is considered normal. Occasional loss of small segments of speech can also be tolerated. Delays of even a fraction of a second, however, become a problem, to the extent that human interaction is involved. Data transmission, on the other hand, is more tolerable towards delay, and less tolerable towards error. Machines can wait and retry until an error free unit (packet) of data is transferred. Economy is achieved by spreading the load across the least loaded routes on a per packet basis, without worrying about the delay, as in the Internet Protocol (IP). When transmitting voice (or any other real time traffic) over IP, the strong error control mechanisms developed for data transmission become a hindrance for voice transmission. Errors can be tolerated in digitally coded voice communication, and any error recovery mechanism introduces intolerable delays. For instance, the automatic retransmission queue (ARQ) that is a part of the transmission control protocol (TCP) makes it unsuitable for voice communication. Instead, user datagram protocol (UDP), augmented by a mechanism that ensures an adequate, but continuous and timely flow of packets is preferable. Such a protocol is designed to meet maximum delay requirements for the speech payload, and is allowed to discard late packets as having been lost. Thus, it is preferable to play speech back within a given delay constraint, even though this may be at the cost of losing some speech packets. The lost speech can be interpolated, thus mostly eliminating the effect of loss. Subjective evaluation, by the authors, of recorded, GSM full rate encoded speech with simulated random frame loss patterns has shown that up to 10% of frames can be lost without significant loss of intelligibility. This observation is in agreement with the QoS mapping for unidirectional delay and loss depicted in [9], and the mean opinion score degradation of similar codecs operating under packet loss [10]. The work described in this paper uses the QoS parameters mentioned above, to answer questions such as: How many subscribers can be supported by a typical operator s VoIP network? How many routers, switches, or hubs should be used? What bandwidth should be used on each link? What is the processing power required at the nodes? MOBILE VS. LANDLINE TELEPHONY In landline circuit switched telephony, the delays introduced into a voice connection by links and network elements are small. Delay is typically not a problem by itself, but is a contributing factor to effects of echo impairments. Propagation delay would dominate in landline, especially in long distance terrestrial or satellite links. Delays in switching equipment can be 2

3 made as small as 125 microseconds (duration of one speech sample in PCM). In mobile telephony, mobility and additional impairments introduced by the air interface need to be considered. For analogue mobile telephones, the delay introduced by the air interface is comparable to a local land line. Digital mobile telephones, which are increasingly being used to add capacity and reliability to existing systems, however, add two major delay components: low bit rate voice encoding, which introduces a ms delay, and interleaving to combat air interface impairments such as fading, which introduces a comparable amount of delay. Therefore, added delays must be taken into account. A delay comparable to the voice encoding delay is also introduced when PCM voice is packetized. When both voice encoding and packetization are used within the same node, these processes can overlap (be pipelined) to avoid additional delays. MODEL DESCRIPTION AND RESULTS A large number of packetized speech traffic models exist in literature [3 6]. The accuracy of these models is determined by their ability to predict the ten following events which are relevant to speech patterns: talkspurt, pause, doubletalk, mutual silence, alternative silence, pause in isolation, solitary talkspurt, interruption, speech after interruption, and speech before interruption. The six state Markov Chain model, known as Brady model [6], is well known for its excellent predictive ability of the ten events mentioned. However, simpler models exist, which are easier to represent and even though they can not predict all the ten events, they still yield accurate talkspurt and silence lengths. In our work, we have represented the voice source by a two state process a talk spurt, averaging 1 sec in length, and a silence interval, averaging 1.5 sec in length. The voice activity factor, which is the probability that the speaker is active, is therefore 0.4. The two states are assumed to be exponentially distributed in length. As mentioned before, additional states have been proposed in the literature, but this complicates the representation. During the talk spurt, a periodic stream of packets will be generated. The arrival rate of the packets and the number of bits per packet will be determined by the encoding scheme. For GSM full rate, the payload is 40 bytes (260 bits + 60 bits padding inserted by the transcoder, since only four FR channels can be multiplexed into one DS0) generated every 20 ms. In our earlier works, we built a voice source using OPNET modeler. At the same time, MIL3 was working on designing a voice application to be released with their version 6.0. We were part of the VoIP committee, which provided MIL3 with the feedback on what features we would like to incorporate in the voice application, features that were relevant for GSM, for example. The results presented here are all obtained using the version 6.0. As mentioned before, the end to end delay experienced by the voice packets will affect the speech quality. The delay consists of two parts: a fixed part arising out of encoding, packetization, and processing speeds and a variable part arising out of the interaction with the data traffic. It is in estimating this variable part that the simulations are most useful. We realize that, with the nature of Internet traffic being so unpredictable, it is not possible to run simulations for every possible scenario. However, we have tried to formulate guidelines and rules of thumb, wherever possible, based on our simulation results. Recently, there have been some papers published [4] on the impact of data traffic on voice but most of them assume the voice traffic to be prioritized. We investigated what would happen if the voice traffic were not prioritized, particularly on a LAN and on point to point links between two LANs. Fig. 1. Probability that voice packet end to end delay is less than value seconds, for varying data traffic on a LAN. Data traffic modeling is complex, consisting of several types of services, such as telnet, ftp, web browsing etc., each with its own characteristics. Bursty sources have been used as models for data traffic file transfers and image transmission. Also, statistical measurements on 3

4 both LAN and WAN traffic indicate that it is self similar or fractal like in nature [7,8]. However the age old Poisson model is still useful in that it provides us with powerful insights into the performance of networks and lends itself easily to mathematical analysis [3]. In the results presented here, Poisson data arrival is assumed, for the sake of simplicity. In our future work, we intend to use sampled real traffic data. Fig. 1 plots the cumulative probability distributions of the voice packet end to end delay with varying data traffic on a 10 Mbps Ethernet shared LAN. Forty nodes were considered in a star shaped configuration with a hub in the center, with the voice traffic load kept constant at 0.7 Mbps (7%). Data arrival was assumed to be Poisson, with the size kept constant at 1400 bytes (since the maximum transmission unit for ethernet is 1500 bytes). voice packet end to end delay for two different voice data proportions, although the total traffic load is kept constant at 72%. Fig. 2. Probability that voice packet end to end delay is less than value seconds for varying voice and data load proportions keeping the total LAN load constant. Similar figures for different voice loads (not shown here) are generated and these figures can be used to directly engineer new installation of VoIP products. For example, Fig. 1 tells us that if we desire that not less than 90% of the packets arrive within 8 ms, the load conditions must be such that the voice traffic does not exceed 0.7 Mbps and the data traffic does not exceed 7.4 Mbps. Even if the total traffic load is kept constant, the delay experienced by the voice packets increases as the proportion of data traffic increases. This is illustrated in Fig. 2 which plots the cumulative distributions of the Fig. 3. Probability that voice packet end to end delay on E1 link is less than value seconds, with varying data traffic. Fig. 3 illustrates the effect of data traffic on voice on an E1 link. All curves are for a total load of 62.5% link utilization. The leftmost curve is for traffic consisting of voice traffic alone without any data. At 62.5% load, there is very little waiting delay at the queue, and the end to end delay is mostly the transmission time of one packet plus the processing delay at the nodes. The waiting delay observed agrees with the theoretical estimate obtained using M/D/1 queue analysis. The two curves on the right of Fig. 3 are for when the voice and data are present in equal proportion. Here the effect of data packet size is evident. The rightmost curve with larger delay is for 1000 byte packets whereas the middle curve with the smaller delay is for 500 byte packets. This emphasizes the fact that applications with smaller data packet sizes are more suitable for mixing with voice traffic than applications with large packets such as ftp and web browsing. The delay figures for continuous data streams are significantly lower than those for bursty data streams, even though the average utilizations may be the same in both cases or may even be lower for the bursty data, as is illustrated in the figs. 4 and 5. Two cases are considered. The voice traffic consists of just two sources, thus forming a negligible part of the 4

5 total traffic. Fig. 4 compares the link utilizations for the two cases. In the first, a continuous data stream with 50% link (E1) utilization is assumed, and in the second, a bursty data stream with 20% activity factor is assumed, with the momentary utilization during the burst reaching 100%. Thus the average utilization for the bursty data is only 20%, less than that for the first case, but the delays are much larger as shown in Fig. 5. Almost all the voice packets that arrive during the burst are heavily delayed, some by more than 100 ms. One solution for this is to shape the data traffic, using a leaky bucket [3], for example, before it is mixed with the voice traffic. cause longer delay. Thus, applications with smaller sized packets are more suitable for mixing with voice packets. c. The burstiness of the data packets. This is in fact related to the size of the packets. A large file transfer, say of 20 Kbytes, will make the traffic bursty. Since the burstiness of the data traffic is hard to predict, one solution to this could be to shape the data traffic using an algorithm such as leaky bucket. Fig. 4. The E1 link utilization for two cases; continuous data stream and bursty data stream. Our conclusions, based on the simulation results, can be summarized as follows. 1. On point to point links, if the voice packets are prioritized, the average delay and the percentile delayed beyond a certain value can be analytically estimated from a knowledge of the voice traffic load and the link speed. 2. On a LAN, or on point to point links between two LANS, where the voice packets are not prioritized, the delay, apart from depending on voice load and the link speed, is also sensitive to: a. The data traffic load. Even when the total data plus voice traffic load remains constant, the delay increases as the proportion of data to the total load increases. b. The size of the data packets. Bigger size packets Fig. 5. Comparison of the probability that voice packet end to end delay is less than value seconds, for two types of data streams, continuous and bursty. FUTURE WORK AND RECOMMENDATIONS The work we have outlined in this paper can serve as the basis for a general network design methodology for packetized voice. This involves setting up topology, link parameters and system parameters to evaluate system performance and costs using simulation and analytical tools. Changes are made systematically in topology as well as parameters and various iterations run, in order to select a configuration that satisfies all design criteria while minimizing costs. The following suggestions are being made to enhance OPNET as a tool of choice in this work. OPNET has a comprehensive set of primitives that support packet switched network simulation. A similar framework would be desirable for simulating circuit switched networks, taking into account the probability of blocking at every stage, up to the destination 5

6 OPNET has a very rich library of contributed models for packet switched data transmission. There is a need for models that can also help detect the bottlenecks in a circuit switched network based on criteria developed using an Erlang loss model CONCLUSIONS In this paper we have presented our approach to modeling packetized voice using OPNET. Packetized voice differs from other packetized data traffic (such as e mail and telnet) in several fundamental aspects including regularity of input traffic, more complex performance criteria (smoothness and error tolerance), performance criteria imposed on a worst case and end to end basis, and a different set of protocols. OPNET simulation results like those presented here can be used to create traffic engineering tables and a network design methodology allowing the network administrators to decide on the network topology (number and location of various types of nodes), processing power required at each node, bandwidth required on each link in the network, and the maximum number of VoIP subscribers that can be allowed in a given network. Some suggestions to enhance OPNET have also been included. REFERENCES 1. Telecommunications Research Associates, Understanding IP and Voice over IP TM, Coursebook, 2. T1A1.2 Working Group, Report No. 24A, A Technical Report on Network Survivability Performance (Supplement to Technical Report No. 24), ftp://ftp.t1.org/pub/techrpts/tr.0/tr_24a.pdf 3. Schwartz, M., Broadband Integrated Networks, Prentice Hall, Minoli, D. and Minoli, M., Delivering Voice over IP Networks, John Wiley and Sons, Stern, T. E., Proc. IEEE Globecom 83, (Dec 1983) Brady, P. T., Bell System tech. J., 48 ( Sept. 1969) pp Paxson, V., and Floyd, S., Proc. SIGCOMM 94, (Aug. 1994) pp Leland, W. E., et.al., IEEE/ACM Trans. On Networking, 2, (Feb. 1994) pp Kostas, T. J., et.al., "Real Time Voice over Packet Switched Networks", IEEE/ACM Trans. On Networking, Jan./Feb. 1998, pp Thorpe, L., "Subjective Evaluation of Frame Erasure Concealment (FEC) Techniques", Contribution T1A1.7/ to ANSI Working Group T1A1.7 ftp://ftp.t1.org/pub/t1a1/t1a1.7/9a17026.pdf 6