# Network Traffic #5. Traffic Characterization

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1 Network #5 Section 4.7.1, Characterization Goals to: Understand the nature of what is transported over communications networks. Use that understanding to improve network design Characterization describes the user demands for network resources How often a customer: Requests a web page Down loads an MP3 Makes a phone call Size/length (how long you hold network resources) Web page Song Phone call 2

2 Characterization Customers request information Rate of requests = λ requests/sec Calls/sec Packets/sec mp3 s/hour The volume of information requested Length of the phone call (sec/call) Length of movie (Bytes) Size of picture (Bytes) 3 Characterization Voice : Aggregate Voice : Individual voice sources Packet Voice Digital Video Data 4

3 Voice : Aggregate Arrival Rate = λ Number of requests/time unit Calls/sec Holding Time, length of time the request will use the network resources Min/call Average Holding Time = 5 Voice : Aggregate Intensity (load) Product of the average holding time and the arrival rate Units of Intensity: ρ is in Erlangs intensity is specified for the 'Busy Hour' DNHR=Dynamic Non- hierarchical routing Network protocol that leverages the nature of traffic 6

4 Voice : Aggregate A telephone line busy 100% of the time = 1 Erlang A telephone busy 6 min/hour is how much traffic 0.1 Erlang 100 telephones busy 10% of the time is how much traffic 10 Erlangs 7 Voice : Aggregate is Random Holding time (length of a phone call) Interarrival time (time between calls) Common assumptions for probability density function (pdf) for Holding time ~ exponential Interarrival time ~ exponential Section and A.1.1 8

5 Voice : Aggregate Probability Holding Time is < t sec = P [ T h < t ] = 1 - e - µ t for t > 0 and 0 for t < 0 1 T h = Service rate = µ µ Probability Interarrival Time is < t sec = P [T I < t ] = 1 - e -λ t for t > 0 and 0 for t < 0 T I = Interarrival time Average Interarrival Time = 1/λ 9 Voice : Aggregate Interarrival Histogram 10

6 Voice : Aggregate Source 1 T I Time T h T I T h Time Time Source 2 Time 11 Voice : Individual voice source Speech inactivity factor Talkspurt Talkspurt Talkspurt Silence Silence time Talkspurt duration Random Average duration ---> s to 1.3 s Exponentially distributed Silence period Random Average duration --->.58s to 1.6s Exponentially distributed 12

7 Voice : Individual voice source Digital Speech Interpolation (DSI) (Section 5.7.2) Uses silence detection Multiplex at the talkspurt level View as call set up at talkspurt level ~Doubles the capacity Analog version called Time Assignment and Speech Interpolation (TASI) Packet Voice with silence detection effectively does DSI Effectively VoIP does DSI 13 Voice : Individual voice source Signal redundancies Voice coding Pulse code modulation (G.711) PCM 8000 samples/sec 64kb/s Adaptive Differential PCM 32kb/s Linear Predictive 2.4 to 16 kb/s For Voice over IP: rate < 8kb/s G is emerging as a popular coding choice. G.723 is an algorithm for compressed digital audio over telephone lines. 14

8 Comparison of popular CODECs Compression scheme Compressed rate (Kbps) Required CPU resources Resultant voice quality G.711 PCM 64 (no compression) Not required Excellent N/A G.723 MP-MLQ 6.4/5.3 Moderate Good (6.4) Fair (5.3) G.726 ADPCM 40/32/24 Low Good (40) Fair (24) G.728 LD-CELP 16 Very high Good Low G.729 CS-ACELP 8 High Good Low Added delay High Very low There is no "right CODEC". The choice of what compression scheme to use depends on what parameters are more important for a specific installation. In practice, G.723 and G.729 are more popular that G.726 and G.728. For details of other VoIP Codecs see: 15 Voice : Individual voice source Example: How many calls can be supported on a system with the following parameters? TDM Coding rate/voice channel = 32 Kb/s DSI Line rate = Mb/s (note a T1/DS1 line is Mb/s) Number of ADPCM channels = (1.563 Mb/s)/(32 Kb/s) = 48 With DSI you get 2 calls/channel = 96 16

9 Voice : Packet Voice Example: Parameters for a packet voice system 1 source Sample rate = 8000 samples/sec (ITU G.711) 8 bits/sample (1 byte/sample) 8 ms/packet Critical parameter Packet size (bytes/packet) = (8ms/packet)*(8000 bytes/sec)=64 Bytes [assuming no overhead bytes] Link rate = 10 Mb/s Clocking time/packet (or Holding time/packet)= (64bytes/packet)*8bits/byte)/(10 Mb/s)= 51.2us 17 Voice : Packet Voice 51.2 us Transmit at a Constant Bit Rate (CBR) 8 ms 8 ms time 51.2 us Receive with variable interpacket arrival times X ms time X not equal 8ms because of network delays If X is too big packet may arrive too late for play out 18

10 Voice : Packet Voice Packet voice looks like a steady flow or Constant Bit Rate (CBR) traffic However, voice can be Variable Bit Rate or VBR silence detection Variable rate coding Problem: After going through the network the packets will not arrive equally spaced in time. Thus playback of packet voice must deal with variable network delays 19 Voice : Packet Voice Assume network delay is uniformly distributed between [25 ms, 75 ms] Same as having a fixed propagation delay of 25 ms with a random network delay uniformly distributed between [0 ms, 50 ms] Note receiver will run out of bytes to play back after 8 ms. Solution: Buffer 50 ms (or 8 packets or 2.8 Kbits) Worst case, receiver will run out of data just as a new packet arrives 20

11 Voice : Packet Voice New problem: networks delays are unknown and maybe unbounded A voice packet may arrive at 85 ms and be too late to be played back Late packets are dropped Last packet may be played out in dead time Packet voice (video) schemes must be able to deal with variable delay and packet loss (Should voice packets be retransmitted?) 21 VoIP Quality ITU-T Recommendation G.114-One-way transmission time, May

12 Voice : Packet Voice G is a voice coding standard, linear prediction compression algorithm From: Performance Evaluation of the Architecture for End-to-End Quality-of-Service Provisioning, Katsuyoshi Iida, Kenji Kawahara, Tetsuya Takine, and Yuji Oie, IEEE Communications Magizine, April VoIP- Delay budget Factors in End to End Delay Assumption: maximum delay from mouth-to-ear needs to be on the order of ms ITU G < 150 ms acceptable for most applications - [150ms, 400 ms] acceptable for international - > 400 ms unacceptable From: 24

13 VoIP- Delay budget Factors in End to End Delay Example: Delay Budget (depends on assumptions) Formation of VoIP packet at TX ~ 30 ms 20ms of voice/packet is default for Cisco 7960 router Other VoIP packet processing ~70 ms (see: Propagation ~10 ms Network Delays ~10 ms Extraction of VoIP packet at Receiver ~30 ms Jitter Buffer ~ 100 ms Compensates for variable network delay Total 250 ms Possible trade-offs: Jitter Buffer vs voice packet loss VoIP packet size vs length of jitter buffer Ref: 25 Video: Analog video Bandwidth ~ 4 Mhz Uncompressed rate 64 Mb/s Components of the signal Luminance Chrominance Audio Synchronization 26

14 Digital Video: MPEG Moving Pictures Experts Group Compresses moving pictures taking advantage of frame-to-frame redundancies MPEG Initial Target: VHS quality on a CD-ROM (320 x CD 1.5 Mbits/sec) 27 Digital Video: MPEG Converts a sequence of frames into a compressed format of three frame types I Frames (intrapicture) P frames (predicted picture) B frames (bidirectional predicted picture) 28

15 Digital Video: MPEG Exploits frame to frame redundancies 29 Frame sizes for action video. Frame sizes for talking head video. Each frame would be transported using multiple packets From:Transmission of MPEG-2 Video Streams over ATM Steven Gringeri, et.al, IEEE Multimedia,

16 MP3- MPEG Layer 3 Audio MPEG specifies a family of three audio coding schemes, Layer-1,-2,-3, Each Layer has and increasing encoder complexity and performance (sound quality per bitrate) The three codecs are compatible in a hierarchical way, i.e. a Layer-N decoder is able to decode bit stream data encoded in Layer-N and all Layers below N The MP3 compression algorithm is based on a complicated psychoacoustic model The majority of the files available on the Internet are encoded in 128 kbits/s stereo. A high quality file is 12 times smaller than the original CDs can be created that contain over 160 songs and can play for over 14 hours on a PC. Music can be efficiently stored on a hard disk and then directly played from there 31 Digital Video: MPEG Compression ranges: 30-to1 50-to-1 MPEG is evolving MPEG 1 MPEG 2 MPEG 4 MPEG 7 32

17 Digital Video: MPEG-4 Initially for audio-video coding for low bitrate channels, Internet Mobile applications Now used for kb/s to 10 s Mb/s video MPEG-4 is a significant change from MPEG-2 Scalability is a key feature of MPEG-4 MPEG-4 contains a Intellectual Property rights (IPR) management infrastructure 33 Digital Video: MPEG-4 Object based: Audio-visual objects (AVO) AVO are described mathematically and given a position in 2D or 3D space Viewer can change vantage point and update calculations done locally No distinction between natural and synthetic AVOs: treats two in an integrated fashion Each AVO is represented separately and becomes the basis for an independent stream Each AVO is reusable, with the capability to incorporate on-thefly elements under application control Content transport with QoS for each component 34

18 Data : General Characteristics Highly variable Not well known Likely to change as new services and applications evolve. 35 Data : General Characteristics Highly bursty, where one definition of burstyness is: Burstyness = Peak rate Average rate 36

19 Data : General Characteristics Example: During a typical remote login connection over a 19.2kb/s modem a user types at a rate of 1 symbol/sec or 8 bits/sec and then transfers a 100 kbyte file. Assume the total holding time of the connection is 10 min. What is the burstyness of this data session? 37 Data : General Characteristics The time to transfer the file is (800,000 bits)/(19,200 b/s) = 41 sec. So for sec = 559 sec. the data rate is 8 bits/sec or 4,472 bits were transferred in 559 sec. Thus in 600 sec. 4, ,000 bits were transferred, yielding a average rate of: 804,472 bits/600 sec = 1,340 bits/sec. The peak rate was 19.2 Kb/s so the burstyness for this data session was: 19,200/1,340 =

20 Data : General Characteristics Call Arrival Session Interarrivals Call Duration Session Duration VoIP Packet Arrivals VoIP Packet Lengths Packet Interarrivals Packet Lengths 39 Data : General Characteristics Asymmetric Nature of Interactive User Burst Think Time User Burst Idle Time Idle Time Computer Burst Computer Burst This Asymmetric property has lead to asymmetric services 40

21 Data : General Characteristics In Time Division Multiplexing (TDM) user must wait for turn to use link. Statistical Multiplexing (Stat Mux) Note high burstness leads to long idle times By transmitting the bursts on demand the link can be efficiently shared. To help insure fairness break the burst into packets and transmit on a packet basis 41 Data : General Characteristics Element length Message Packet Cell Arrival rate Message/sec Packets/sec Cells/sec 42

22 Data : General Characteristics intensity (< 1 with one server) ρ where = λ T h T h = Average Packet Length in Bits Link Capacity in Bits / sec = L C Average Packet Length in Bits = L Link Capacity in Bits / Sec = C 43 Data : General Characteristics Standard Assumptions Message length has an exponential pdf Interarrival time has an exponential pdf Packet Length Packet Interarrival Time Data was taken from special traces in Data was captured at the Internet Uplink of the University of Auckland by the Wand Research group in the year The tap was installed on an OC-3 link. 44

23 KU/ITTC has collected aggregate traffic data from Sunflower Datavision 45 From the Internet into Datavision Mean = Mb/s. Maximum = Mb/s From Datavision out to the Internet Mean = Mb/s. Maximum = Mb/s 46

24 Data : Conclusions Very bursty Problems with traffic modeling Rapidly evolving applications Complex network interactions Issues: Do models match real traffic flows? Are the performance models based on specific traffic assumption robust 47 Conclusions Network traffic defines the demands for network resources Network traffic is dynamic Changes with the deployment of new application Time of day Models for network traffic are continuing to evolve 48

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