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Engineering for QoS and the limits of service differentiation

IWQoS June 2000. Engineering for QoS and the limits of service differentiation. Jim Roberts (james.roberts@francetelecom.fr). The central role of QoS. feasible technology. quality of service transparency response time accessibility. service model resource sharing

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Engineering for QoS and the limits of service differentiation

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  1. IWQoS June 2000 Engineering for QoS and the limits of service differentiation Jim Roberts (james.roberts@francetelecom.fr)

  2. The central role of QoS feasible technology • quality of service • transparency • response time • accessibility • service model • resource sharing • priorities,... • network engineering • provisioning • routing,... a viable business model

  3. Engineering for QoS: a probabilistic point of view • statistical characterization of traffic • notions of expected demand and random processes • for packets, bursts, flows, aggregates • QoS in statistical terms • transparency: Pr [packet loss], mean delay, Pr [delay > x],... • response time: E [response time],... • accessibility: Pr [blocking],... • QoS engineering, based on a three-way relationship: demand performance capacity

  4. Outline • traffic characteristics • QoS engineering for streaming flows • QoS engineering for elastic traffic • service differentiation

  5. Internet traffic is self-similar • a self-similar process • variability at all time scales • due to: • infinite variance of flow size • TCP induced burstiness • a practical consequence • difficult to characterize a traffic aggregate Ethernet traffic, Bellcore 1989

  6. Traffic on a US backbone link (Thomson et al, 1997) • traffic intensity is predictable ... • ... and stationary in the busy hour

  7. Traffic on a French backbone link • traffic intensity is predictable ... • ... and stationary in the busy hour tue wed thu fri sat sun mon 12h 18h 00h 06h

  8. IP flows • a flow = one instance of a given application • a "continuous flow" of packets • basically two kinds of flow, streaming and elastic

  9. IP flows • a flow = one instance of a given application • a "continuous flow" of packets • basically two kinds of flow, streaming and elastic • streaming flows • audio and video, real time and playback • rate and duration are intrinsic characteristics • not rate adaptive (an assumption) • QoS  negligible loss, delay, jitter

  10. IP flows • a flow = one instance of a given application • a "continuous flow" of packets • basically two kinds of flow, streaming and elastic • streaming flows • audio and video, real time and playback • rate and duration are intrinsic characteristics • not rate adaptive (an assumption) • QoS  negligible loss, delay, jitter • elastic flows • digital documents ( Web pages, files, ...) • rate and duration are measures of performance • QoS  adequate throughput (response time)

  11. variable rate video Flow traffic characteristics • streaming flows • constant or variable rate • compressed audio (O[103 bps]) and video (O[106 bps]) • highly variable duration • a Poisson flow arrival process (?)

  12. Flow traffic characteristics • streaming flows • constant or variable rate • compressed audio (O[103 bps]) and video (O[106 bps]) • highly variable duration • a Poisson flow arrival process (?) • elastic flows • infinite variance size distribution • rate adaptive • a Poisson flow arrival process (??) variable rate video

  13. Modelling traffic demand • stream traffic demand • arrival rate x bit rate x duration • elastic traffic demand • arrival rate x size • a stationary process in the "busy hour" • eg, Poisson flow arrivals, independent flow size traffic demand Mbit/s busy hour time of day

  14. Outline • traffic characteristics • QoS engineering for streaming flows • QoS engineering for elastic traffic • service differentiation

  15. Open loop control for streaming traffic • a "traffic contract" • QoS guarantees rely on • traffic descriptors + admission control + policing • time scale decomposition for performance analysis • packet scale • burst scale • flow scale user-network interface user-network interface network-network interface

  16. Packet scale: a superposition of constant rate flows • constant rate flows • packet size/inter-packet interval = flow rate • maximum packet size = MTU

  17. buffer size log Pr [saturation] Packet scale: a superposition of constant rate flows • constant rate flows • packet size/inter-packet interval = flow rate • maximum packet size = MTU • buffer size for negligible overflow? • over all phase alignments... • ...assuming independence between flows

  18. Packet scale: a superposition of constant rate flows • constant rate flows • packet size/inter-packet interval = flow rate • maximum packet size = MTU • buffer size for negligible overflow? • over all phase alignments... • ...assuming independence between flows • worst case assumptions: • many low rate flows • MTU-sized packets buffer size increasing number, increasing pkt size log Pr [saturation]

  19. Packet scale: a superposition of constant rate flows • constant rate flows • packet size/inter-packet interval = flow rate • maximum packet size = MTU • buffer size for negligible overflow? • over all phase alignments... • ...assuming independence between flows • worst case assumptions: • many low rate flows • MTU-sized packets •  buffer sizing for M/DMTU/1 queue • Pr [queue > x] ~ C e -r x buffer size M/DMTU/1 increasing number, increasing pkt size log Pr [saturation]

  20. The "negligible jitter conjecture" • constant rate flows acquire jitter • notably in multiplexer queues

  21. The "negligible jitter conjecture" • constant rate flows acquire jitter • notably in multiplexer queues • conjecture: • if all flows are initially CBR and in all queues: S flow rates < service rate • they never acquire sufficient jitter to become worse for performance than a Poisson stream of MTU packets

  22. The "negligible jitter conjecture" • constant rate flows acquire jitter • notably in multiplexer queues • conjecture: • if all flows are initially CBR and in all queues: S flow rates < service rate • they never acquire sufficient jitter to become worse for performance than a Poisson stream of MTU packets • M/DMTU/1 buffer sizing remains conservative

  23. bursts Burst scale: fluid queueing models • assume flows have an intantaneous rate • eg, rate of on/off sources packets arrival rate

  24. packets bursts arrival rate Burst scale: fluid queueing models • assume flows have an intantaneous rate • eg, rate of on/off sources • bufferless or buffered multiplexing? • Pr [arrival rate < service rate] < e • E [arrival rate] < service rate

  25. buffer size 0 0 log Pr [saturation] Buffered multiplexing performance: impact of burst parameters Pr [rate overload]

  26. buffer size 0 0 log Pr [saturation] Buffered multiplexing performance: impact of burst parameters longer burst length shorter

  27. buffer size 0 0 log Pr [saturation] Buffered multiplexing performance: impact of burst parameters more variable burst length less variable

  28. buffer size 0 0 log Pr [saturation] Buffered multiplexing performance: impact of burst parameters long range dependence burst length short range dependence

  29. Choice of token bucket parameters? • the token bucket is a virtual queue • service rate r • buffer size b r b

  30. b b' non- conformance probability Choice of token bucket parameters? • the token bucket is a virtual queue • service rate r • buffer size b • non-conformance depends on • burst size and variability • and long range dependence r b

  31. Choice of token bucket parameters? • the token bucket is a virtual queue • service rate r • buffer size b • non-conformance depends on • burst size and variability • and long range dependence • a difficult choice for conformance • r >> mean rate... • ...or b very large b b' non- conformance probability r b

  32. time Bufferless multiplexing: alias "rate envelope multiplexing" • provisioning and/or admission control to ensure Pr [Lt>C] < e • performance depends only on stationary rate distribution • loss rate  E [(Lt -C)+] / E [Lt] • insensitivity to self-similarity output rate C combined input rate Lt

  33. Efficiency of bufferless multiplexing • small amplitude of rate variations ... • peak rate << link rate (eg, 1%)

  34. Efficiency of bufferless multiplexing • small amplitude of rate variations ... • peak rate << link rate (eg, 1%) • ... or low utilisation • overall mean rate << link rate

  35. Efficiency of bufferless multiplexing • small amplitude of rate variations ... • peak rate << link rate (eg, 1%) • ... or low utilisation • overall mean rate << link rate • we may have both in an integrated network • priority to streaming traffic • residue shared by elastic flows

  36. Flow scale: admission control • accept new flow only if transparency preserved • given flow traffic descriptor • current link status • no satisfactory solution for buffered multiplexing • (we do not consider deterministic guarantees) • unpredictable statistical performance • measurement-based control for bufferless multiplexing • given flow peak rate • current measured rate (instantaneous rate, mean, variance,...)

  37. Flow scale: admission control • accept new flow only if transparency preserved • given flow traffic descriptor • current link status • no satisfactory solution for buffered multiplexing • (we do not consider deterministic guarantees) • unpredictable statistical performance • measurement-based control for bufferless multiplexing • given flow peak rate • current measured rate (instantaneous rate, mean, variance,...) • uncritical decision threshold if streaming traffic is light • in an integrated network

  38. utilization (r=a/m) for E(m,a) = 0.01 r 0.8 0.6 0.4 0.2 m 0 20 40 60 80 100 Provisioning for negligible blocking • "classical" teletraffic theory; assume • Poisson arrivals, rate l • constant rate per flow r • mean duration 1/m •  mean demand, A = l/m r bits/s • blocking probability for capacity C • B = E(C/r,A/r) • E(m,a) is Erlang's formula: • E(m,a)= •  scale economies

  39. utilization (r=a/m) for E(m,a) = 0.01 r 0.8 0.6 0.4 0.2 m 0 20 40 60 80 100 Provisioning for negligible blocking • "classical" teletraffic theory; assume • Poisson arrivals, rate l • constant rate per flow r • mean duration 1/m •  mean demand, A = l/m r bits/s • blocking probability for capacity C • B = E(C/r,A/r) • E(m,a) is Erlang's formula: • E(m,a)= •  scale economies • generalizations exist: • for different rates • for variable rates

  40. Outline • traffic characteristics • QoS engineering for streaming flows • QoS engineering for elastic traffic • service differentiation

  41. Closed loop control for elastic traffic • reactive control • end-to-end protocols (eg, TCP) • queue management • time scale decomposition for performance analysis • packet scale • flow scale

  42. Packet scale: bandwidth and loss rate • a multi-fractal arrival process

  43. Packet scale: bandwidth and loss rate • a multi-fractal arrival process • but loss and bandwidth related by TCP (cf. Padhye et al.) congestion avoidance loss rate p B(p)

  44. Packet scale: bandwidth and loss rate • a multi-fractal arrival process • but loss and bandwidth related by TCP (cf. Padhye et al.) congestion avoidance loss rate p B(p)

  45. Packet scale: bandwidth and loss rate • a multi-fractal arrival process • but loss and bandwidth related by TCP (cf. Padhye et al.) • thus, p = B-1(p): ie, loss rate depends on bandwidth share congestion avoidance loss rate p B(p)

  46. Packet scale: bandwidth sharing • reactive control (TCP, scheduling) shares bottleneck bandwidth unequally • depending on RTT, protocol implementation, etc. • and differentiated services parameters

  47. Example: a linear network route 0 route 1 route L Packet scale: bandwidth sharing • reactive control (TCP, scheduling) shares bottleneck bandwidth unequally • depending on RTT, protocol implementation, etc. • and differentiated services parameters • optimal sharing in a network: objectives and algorithms... • max-min fairness, proportional fairness, maximal utility,...

  48. Packet scale: bandwidth sharing • reactive control (TCP, scheduling) shares bottleneck bandwidth unequally • depending on RTT, protocol implementation, etc. • and differentiated services parameters • optimal sharing in a network: objectives and algorithms... • max-min fairness, proportional fairness, maximal utility,... • ... but response time depends more on traffic process than the static sharing algorithm! Example: a linear network route 0 route 1 route L

  49. link capacity C fair shares Flow scale: performance of a bottleneck link • assume perfect fair shares • link rate C, n elastic flows  • each flow served at rate C/n

  50. link capacity C fair shares Flow scale: performance of a bottleneck link • assume perfect fair shares • link rate C, n elastic flows  • each flow served at rate C/n • assume Poisson flow arrivals • an M/G/1 processor sharing queue • load, r = arrival rate x size / C  a processor sharing queue

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