Flow aware networking for effective quality of service control

Transcription

1 IMA Workshop on Scaling October 1999 Flow aware networking for effective quality of service control Jim Roberts France Télécom - CNET james.roberts@cnet.francetelecom.fr Centre National d'etudes des Télécommunications France Télécom 1999

2 Internet traffic is self-similar Bellcore measurements (1989) ripples, waves, swells, hours confirmations, explanations ( ) VBR video is also self-similar AT&T wavelet analyses (1998, 1999) multi-fractal at small time scales due to TCP asymptotic self-similarity due to heavy tails 10 sec

3 Outline Traffic characteristics Performance modelling QoS of elastic flows Flow aware networking

4 Traffic on a French backbone link traffic intensity is predictable and stationary in the busy hour like telephone traffic overprovisioning is feasible traffic can be modelled as a stationary random process tue wed thu fri sat sun mon 12h 18h 00h 06h

5 Traffic on a US backbone link (Thomson et al, 1997)

6 Internet traffic make up traffic distribution on a backbone link: bytes: 95% TCP, 5% UDP packets: 90% TCP, 10% UDP flows: 80% TCP, 20% UDP applications: Web: 70% mail: 10% file transfer: 10% video/audio: 5% other : 5% trends rapid growth greater proportion of audio/video?

7 Flow based traffic modelling a flow = one instance of a given application a continuous flow of packets basically two kinds of flow, stream and elastic

8 Flow based traffic modelling a flow = one instance of a given application a continuous flow of packets basically two kinds of flow, stream and elastic "stream" flows audio and video, real time and playback rate and duration are intrinsic characteristics QoS negligible loss, delay, jitter

9 Flow based traffic modelling a flow = one instance of a given application a continuous flow of packets basically two kinds of flow, stream and elastic "stream" flows audio and video, real time and playback rate and duration are intrinsic characteristics QoS negligible loss, delay, jitter "elastic" flows digital documents ( Web pages, files,...) rate and duration are measures of performance QoS adequate throughput (response time)

10 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" between 2 pm and 6 pm

11 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" between 2 pm and 6 pm three operating regimes underload, critical load, overload underload critical load overload link capacity

12 Elastic flow size distribution lots of very small documents Pr [size < 1 Kbyte] = 0.95 Pr [size < 1 Mbyte] = log Pr {size = x} heavy tailed size distribution for large x, Pr [size > x] x -β, 1<β<2 infinite variance log x

13 Elastic flow arrival process (Web traffic) Poisson session arrivals non-stationarity a session correlated batch arrivals within session heavy tailed number of flows per session traffic model Weibull renewal process or non-stationary Poisson? (Feldman) or Poisson in busy hour (Nabe)? Web page Web page big file

14 Stream flow characteristics flow rate compressed audio (O[10 3 bps]) and video (O[10 6 bps]) self-similarity of VBR video flow duration heavy-tailed phone durations other stream applications? flow arrival process Poisson for telephone videoconference, audio/video playback?

16 Open loop multiplexing for self-similar traffic example: M/G/1 or M/G/ fluid input queue infinite variance size => infinite expected delay unpredictable performance

17 Open loop multiplexing for self-similar traffic example: M/G/1 or M/G/ fluid input queue infinite variance size => infinite expected delay unpredictable performance the failure of the token bucket (= a finite capacity queue) defining a VBR flow shaping a traffic aggregate r b

18 Open loop multiplexing for self-similar traffic example: M/G/1 or M/G/ fluid input queue infinite variance size => infinite expected delay unpredictable performance the failure of the token bucket (= a finite capacity queue) defining a VBR flow shaping a traffic aggregate an alternative: "bufferless multiplexing" alias rate envelope multiplexing r b

19 Rate envelope multiplexing admission control to ensure Pr {Λ t >c} < ε performance depends only on stationary rate distribution loss rate = E{ (Λ t -c) + } / E{Λ t } insensitive to self-similarity combined input rate Λ t output rate c time

20 Efficiency of rate envelope multiplexing small amplitude of rate variations... peak rate << link rate

21 Efficiency of rate envelope multiplexing small amplitude of rate variations... peak rate << link rate... or low utilisation overall mean rate << link rate

22 Efficiency of rate envelope multiplexing small amplitude of rate variations... peak rate << link rate... or low utilisation overall mean rate << link rate measurement-based admission control to ensure negligible loss and jitter "easy", if peak rate << link rate and overall mean rate << link rate

23 Closed loop multiplexing for elastic flows TCP adjusts flow rate to fit available bandwidth fair sharing is an objective

24 Closed loop multiplexing for elastic flows TCP adjusts flow rate to fit available bandwidth fair sharing is an objective

25 Closed loop multiplexing for elastic flows TCP adjusts flow rate to fit available bandwidth fair sharing is an objective in reality, fairness is imperfect... multiple bottlenecks unequal feedback delays...

26 Closed loop control (e.g., TCP) assume TCP realizes 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, ρ = arrival rate x size / C Poisson arrivals processor sharing

27 Closed loop control (e.g., TCP) assume TCP realizes 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, ρ = arrival rate x size / C Poisson arrivals performance insensitive to size distribution Pr [n transfers] = ρ n (1-ρ) E [response time] = size / C(1-ρ) processor sharing

28 Closed loop control (e.g., TCP) assume TCP realizes 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, ρ = arrival rate x size / C Poisson arrivals performance insensitive to size distribution Pr [n transfers] = ρ n (1-ρ) E [response time] = size / C(1-ρ) processor sharing other performance results are available eg, response time distribution for heavy tailed flow size (cf. Zwart, ITC )

29 Closed loop control (e.g., TCP) assume TCP realizes 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, ρ = arrival rate x size / C performance insensitive to size distribution Pr [n transfers] = ρ n (1-ρ) E [response time] = size / C(1-ρ) other performance results are available eg, response time distribution for heavy tailed flow size (cf. Zwart, ITC ) generalizations limited access rate, min rate non-poisson arrivals Poisson arrivals processor sharing infinite server instability if ρ > 1 ie, unbounded response time

31 Elastic flow quality of service some scope for differentiated services throughput = C (1-ρ), if ρ < 1 but flow throughput is limited (by access rate,...) C throughput access rate ρ

32 Elastic flow quality of service some scope for differentiated services throughput = C (1-ρ), if ρ < 1 but flow throughput is limited (by access rate,...) for large networks (eg, C > 100 Mb/s), if ρ<1, QoS is very good if ρ>1, QoS is very bad! limited potential for service differentiation C throughput access rate ρ

33 Elastic flow quality of service some scope for differentiated services throughput = C (1-ρ), if ρ < 1 but flow throughput is limited (by access rate,...) for large networks (eg, C > 100 Mb/s), if ρ<1, QoS is very good if ρ>1, QoS is very bad! limited potential for service differentiation for good response times provisioning and routing to ensure ρ<1 avoid congestion collapse when ρ> by admission control C access rate 0 0 throughput 1 ρ

34 Impact of discriminatory sharing assume class 1 flows receive twice bandwidth of class 2 flows by WFQ weights {φ 1, φ 2 } or due to different feedback delays a "discriminatory processor sharing" queue cf. Fayolle et al, 1980

35 Response time discrimination response time size 3.5 class 2 exponential size distribution, mean = class 1 size 2 classes: φ 1 =2, φ 2 =1; ρ 1 = ρ 2 = 1/3

36 Impact of Pareto flow sizes (simulation) response time size ρ = size

37 Flow size discrimination serving shorter flows first improves their response time without degrading that of longer flows

38 Flow size discrimination serving shorter flows first improves their response time without degrading that of longer flows

39 Flow size discrimination serving shorter flows first improves their response time without degrading that of longer flows in fact, "shortest remaining processing time" first is optimal cf. Massoulié & Roberts, Crovella et al.

40 Shortest remaining processing time link serves exclusively shortest document pre-emptive resume service cf. Shrage and Miller, 1966 response time size size exponential Pareto 1.0

41 Admission control for elastic flows to prevent congestion collapse for ρ >1 M/G/1/N processor sharing system min bandwidth = C/N Pr [blocking] = ρ N (1 - ρ)/(1 - ρ N+1 ) (1-1/ρ), for ρ >1 1.8 Blocking probability 300 E [Response time]/size N N

42 Admission control for elastic flows to prevent congestion collapse for ρ >1 M/G/1/N processor sharing system min bandwidth = C/N Pr [blocking] = ρ N (1 - ρ)/(1 - ρ N+1 ) (1-1/ρ), for ρ >1 1.8 Blocking probability 300 E [Response time]/size ρ = N 100 ρ = N

43 Admission control for elastic flows to prevent congestion collapse for ρ >1 M/G/1/N processor sharing system min bandwidth = C/N Pr [blocking] = ρ N (1 - ρ)/(1 - ρ N+1 ) (1-1/ρ), for ρ >1 1.8 Blocking probability 300 E [Response time]/size.6.4 ρ = ρ = N 200 ρ = ρ = N

44 Admission control for elastic flows to prevent congestion collapse for ρ >1 M/G/1/N processor sharing system min bandwidth = C/N Pr [blocking] = ρ N (1 - ρ)/(1 - ρ N+1 ) (1-1/ρ), for ρ >1 uncritical choice of threshold eg, 1% of link capacity (N=100) 1.8 Blocking probability 300 E [Response time]/size.6.4 ρ = ρ = N 200 ρ = ρ = N

46 Two service classes stream traffic interactive and streaming audio, video open loop control rate envelope multiplexing for minimal delay priority queueing elastic stream

47 Two service classes stream traffic interactive and streaming audio, video open loop control rate envelope multiplexing for minimal delay priority queueing elastic traffic Web pages, mail, files, stored video,... closed loop control share available bandwidth (fairly and/or efficiently) elastic stream

48 Two service classes stream traffic interactive and streaming audio, video open loop control rate envelope multiplexing for minimal delay priority queueing elastic traffic Web pages, mail, files, stored video,... closed loop control share available bandwidth (fairly and/or efficiently) integration, a natural synergy priority to stream flows elastic traffic uses all available capacity negligible loss for stream flows elastic stream

49 Flow admission control for both classes measurement-based admission control estimate "available rate" (rate achievable by new elastic flow) eg, a "phantom" TCP connection a common admission criterion admit flow if available rate > R for stream and elastic flows max peak = min throughput stream peak rate < R elastic throughput > R eg, R = 1% of link rate flow state to identify flows in progress on the fly identification => no signalling elastic stream

50 Implementing admission control identifying the flows "on the fly" for rapidity maintain a table of active flows flow ID x y z last packet time t x t y t z

51 Implementing admission control identifying the flows "on the fly" for rapidity maintain a table of active flows measuring available bandwidth on a link, on a path a "phantom" elastic flow? flow ID x y z last packet time t x t y t z

52 Implementing admission control identifying the flows "on the fly" for rapidity maintain a table of active flows measuring available bandwidth on a link, on a path a "phantom" elastic flow? incremental implementation flow ID x y z last packet time t x t y t z

53 Additional advantages of flow awareness QoS routing choosing the best path for elastic and stream flows per flow queueing (cf Suter et al) max-min fairness or something else (?)

54 Additional advantages of flow awareness QoS routing choosing the best path for elastic and stream flows per flow queueing (cf Suter et al) max-min fairness or something else (?) service differentiation by accessibility class i blocked when available b/w < a i a i < a j Pr [class i blocked] < Pr [class j blocked]

55 Additional advantages of flow awareness QoS routing choosing the best path for elastic and stream flows per flow queueing (cf Suter et al) max-min fairness or something else (?) service differentiation by accessibility class i blocked when available b/w < a i a i < a j Pr [class i blocked] < Pr [class j blocked] simple usage-based pricing

56 Challenges overcoming technical difficulties: flow identification at line speed measurement based admission control (estimating "available bandwidth")... getting flow aware networking adopted incremental implementation standardization...