The Reality and Mythology of QoS and H.323

Overview H. 323 bounds testing QoS models Implications of applying models Engineering to need.

Test Motivation Abilene is trying to provide DiffServ EF Is H.323 suitable candidate for APS? DiffServ lacks hard bounds, it is totally probabilistic. What really would help? What are the performance bounds?

Video Artifacts Spatial Augmentation – Video artifacts are added to the picture. Objects appear that are not in the captured video such as video tiles. Spatial Depreciation – Parts of the picture or objects in the picture are missing. Temporal Distortion – Over time the “flow” of an event is distorted by missing data, in mild cases resulting in an inter-frame jerkiness. In more severe cases resulting in video freezing.

Video Artifacts Audio Augmentation – Audio artifacts added to audio stream such as pops, clicks and hiss. Audio Depreciation – Parts of the audio are missing.

Scope of H.323 Bounds Testing –What network conditions can be mapped to certain qualities of video. – It can be highly subjective. – We did not desire to engage in a Cognitive Science experiment. – Needed simple reproducible test procedure.

Test Procedure Still office scene, count the number of defects over a 60 second sample. Motion in scene and count the number of seconds needed to recover. Tested in a variety of setups – Point-to-point –MCU –Cascaded MCUs – Isolated Latency, Loss and Jitter

Network Emulator Operating System: Linux Mandrake 7.2 Kernel recompiled and optimized for the device to be a router. CPU: Pentium III 733Mhz Memory: 256 MB. Motherboard: Asus CUSLC2-C AGP4X NICS: Intel Etherpro 10/100. Emulator Software: Nistnet 2.1.0

Used to test H.323 Verified Nistnet system prior to test. –Tested platform with SmartBits. –All parameters were met with in a +/- 1 msec (Actual resolution ~.5msec) –With SmartBits we could verify switches etc. to further validate our findings. Worst case is total accuracy within +/- 3msec.

Point-to-Point tests Latency does not matter. (holds true for all scenarios)

End-to-end Delay Components Compression Delay Transmission Delay Electronic Delay Propagation Delay Processing Delay Queuing Delay Resynchronization Delay Decompression Delay Presentation Delay SENDER SIDENETWORKRECEIVER SIDE

Delay Values Transmission Delay + Electronic Delay: Modem delay = 40ms Transmission delay = 10 chars over 56Kbps = 80/56000bps = 1.4ms Switch Propagation Delay: <2ms Presentation Delay = 17ms

Encode and Decode Latency MCU SWITCH END POINT 1 END POINT 2 METRONOME (PULSE GENERATOR) MIC I/PAUDIO O/P OSCILLOSCOPE AB SCOPE I/P A: METRONOME I/P SCOPE I/P B: ENDPOINT 2 AUDIO O/P

Oscilloscope Waveforms

Experiment and Results Dialing Speeds: 256K, 384K, 512K, 768K Metronome setting: 113 Propagation delay + Switch delay ~ 0 Encode + Decode delay ~ 240ms (independent of dialing speed) Delay through MCU ~120ms to ~200ms (delay increasing with dialing speed)

Network Requirements Latency – users may find annoying but the it does not break the protocol. Loss – Can tolerate some loss, must be below 1% in p-2-p and 0.75% in MCU Jitter – Very jitter intolerant. For 30 Fps must be lower than ~33 msec. Seems very intolerant in cascaded MCU scenario.

Network Calculus 101 All functions are cumulative distribution functions, i.e. wide-sense increasing. Uses min-plus Algebra. Uses classes of primitive functions to describe various network behaviors Employs convolution and deconvolution with primitives to arrive at meaningful conclusions.

Models IntServ – Has the necessary per flow state but is not here yet. – It also probably has many unforeseen maintenance and administrative issues. (see next section). – Experience from ATM SVCs suggests many scalability issues. Possible solutions include MPLS or Policy routing.

Models Any E-2-E solution has scalability problem in the sense that in packet switched networks the solution vector is more than number of hops and delay etc. x -> +α -> In other words it is also a function of topology. (More in DiffServ).. Source: Network Calculus: A Theory of Deterministic Queuing Systems for the Internet by Jean-Yeves Le Boudec & Patrick Thriran, Springer-Verlog, Berlin Heidelberg, 2001.

Models DiffServ lacks the per flow state necessary for tight performance bounds because….. β * 1 (t) = [β(t)- α 2 (t)] Where β is the rate-latency function. β R,T (t) = R[t-T] + i.e. Service Curve. b * 1 = b 1 + r 1 T +r 1 (b 2 +r 2 T/R-r 2 ) Where b is a component of the Affine function γ r,b (t) = b+rt if t>0. Source: Network Calculus: A Theory of Deterministic Queuing Systems for the Internet by Jean-Yeves Le Boudec & Patrick Thriran, Springer-Verlog, Berlin Heidelberg, 2001.

Models V ~ for bounded delay so when v 0 converges to V the latency bound explodes to infinity. For v l = Σ iЭm r i /C l. Where v = link utilization, i=flow, r = rate and C = service rate. Source: Network Calculus: A Theory of Deterministic Queuing Systems for the Internet by Jean-Yeves Le Boudec & Patrick Thriran, Springer-Verlog, Berlin Heidelberg, 2001.

Engineering to the need What realistically can we do? –It depends on ones network. –Appropriate queuing for congested links for maybe a single to only a few flows. –Packet shaping on receiver with a Greedy Packet Shaper. GPS will not increase latency or buffering requirements if and only if network was previously lossless.