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Engineering for QoS and the limits of service differentiation

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### Engineering for QoS and the limits of service differentiation

Outline

June 2000

Jim Roberts

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

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

Outline

- traffic characteristics
- QoS engineering for streaming flows
- QoS engineering for elastic traffic
- service differentiation

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

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

- traffic intensity is predictable ...
- ... and stationary in the busy hour

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

IP flows

- a flow = one instance of a given application
- a "continuous flow" of packets
- basically two kinds of flow, streaming and elastic

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

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)

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 (?)

- constant or variable rate

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 (?)

- constant or variable rate
- elastic flows
- infinite variance size distribution
- rate adaptive
- a Poisson flow arrival process (??)

variable rate video

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

Outline

- traffic characteristics
- QoS engineering for streaming flows
- QoS engineering for elastic traffic
- service differentiation

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

Packet scale: a superposition of constant rate flows

- constant rate flows
- packet size/inter-packet interval = flow rate
- maximum packet size = MTU

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

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]

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]

The "negligible jitter conjecture"

- constant rate flows acquire jitter
- notably in multiplexer queues

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

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

Burst scale: fluid queueing models

- assume flows have an intantaneous rate
- eg, rate of on/off sources

packets

arrival rate

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

0

0

log Pr [saturation]

Buffered multiplexing performance: impact of burst parametersPr [rate overload]

0

0

log Pr [saturation]

Buffered multiplexing performance: impact of burst parameterslonger

burst length

shorter

0

0

log Pr [saturation]

Buffered multiplexing performance: impact of burst parametersmore variable

burst length

less variable

0

0

log Pr [saturation]

Buffered multiplexing performance: impact of burst parameterslong range dependence

burst length

short range dependence

Choice of token bucket parameters?

- the token bucket is a virtual queue
- service rate r
- buffer size b

r

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

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

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

Efficiency of bufferless multiplexing

- small amplitude of rate variations ...
- peak rate << link rate (eg, 1%)

Efficiency of bufferless multiplexing

- small amplitude of rate variations ...
- peak rate << link rate (eg, 1%)

- ... or low utilisation
- overall mean rate << link rate

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

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,...)

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

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

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

Outline

- traffic characteristics
- QoS engineering for streaming flows
- QoS engineering for elastic traffic
- service differentiation

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

Packet scale: bandwidth and loss rate

- a multi-fractal arrival process

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)

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)

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)

Packet scale: bandwidth sharing

- reactive control (TCP, scheduling) shares bottleneck bandwidth unequally
- depending on RTT, protocol implementation, etc.
- and differentiated services parameters

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,...

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

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

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

fair shares

a processor sharing queue

throughput

C

r

0

0

1

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

- performance insensitive to size distribution
- Pr [n transfers] = rn(1-r)
- E [response time] = size / C(1-r)

Flow scale: performance of a bottleneck link

link capacity C

- 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

- performance insensitive to size distribution
- Pr [n transfers] = rn(1-r)
- E [response time] = size / C(1-r)

- instability if r > 1
- i.e., unbounded response time
- stabilized by aborted transfers...
- ... or by admission control

fair shares

a processor sharing queue

throughput

C

r

0

0

1

1-p

flows

Poisson

session

arrivals

p

think time

Generalizations of PS model- non-Poisson arrivals
- Poisson sessions
- Bernoulli feedback

processor

sharing

infinite

server

1-p

flows

Poisson

session

arrivals

processor

sharing

p

think time

infinite

server

Generalizations of PS model- non-Poisson arrivals
- Poisson sessions
- Bernoulli feedback

- discriminatory processor sharing
- weight fi for class i flows
- service rate fi

1-p

flows

Poisson

session

arrivals

processor

sharing

p

think time

infinite

server

Generalizations of PS model- non-Poisson arrivals
- Poisson sessions
- Bernoulli feedback

- discriminatory processor sharing
- weight fi for class i flows
- service rate fi

- rate limitations (same for all flows)
- maximum rate per flow (eg, access rate)
- minimum rate per flow (by admission control)

Admission control can be useful ...

... to prevent disasters at sea !

Admission control can also be useful for IP flows

- improve efficiency of TCP
- reduce retransmissions overhead ...
- ... by maintaining throughput

- prevent instability
- due to overload (r > 1)...
- ...and retransmissions

- avoid aborted transfers
- user impatience
- "broken connections"

- a means for service differentiation...

.8

.6

.4

.2

0

300

200

100

0

Blocking probability

E [Response time]/size

0 100 200 N

0 100 200 N

Choosing an admission control threshold- N = the maximum number of flows admitted
- negligible blocking when r<1, maintain quality when r>1

r = 1.5

r = 1.5

r = 0.9

r = 0.9

Choosing an admission control threshold- N = the maximum number of flows admitted
- negligible blocking when r<1, maintain quality when r>1

- M/G/1/N processor sharing system
- min bandwidth = C/N
- Pr [blocking] = rN(1 - r)/(1 - rN+1) (1 - 1/r) , for r>1

1

.8

.6

.4

.2

0

300

200

100

0

Blocking probability

E [Response time]/size

0 100 200 N

0 100 200 N

.8

.6

.4

.2

0

300

200

100

0

Blocking probability

E [Response time]/size

r = 1.5

r = 1.5

r = 0.9

r = 0.9

0 100 200 N

0 100 200 N

Choosing an admission control threshold- N = the maximum number of flows admitted
- negligible blocking when r<1, maintain quality when r>1

- M/G/1/N processor sharing system
- min bandwidth = C/N
- Pr [blocking] = rN(1 - r)/(1 - rN+1) (1 - 1/r) , for r>1

- uncritical choice of threshold
- eg, 1% of link capacity (N=100)

C

backbone link

(rate C)

access

rate

access links

(rate<<C)

0

0

r

1

Impact of access rate on backbone sharing- TCP throughput is limited by access rate...
- modem, DSL, cable

- ... and by server performance

C

backbone link

(rate C)

access

rate

access links

(rate<<C)

0

0

r

1

Impact of access rate on backbone sharing- TCP throughput is limited by access rate...
- modem, DSL, cable

- ... and by server performance
- backbone link is a bottleneck only if saturated!
- ie, if r > 1

utilization (r) for B = 0.01

r

0.8

0.6

0.4

0.2

m

0 20 40 60 80 100

Provisioning for negligible blocking for elastic flows- "elastic" teletraffic theory; assume
- Poisson arrivals, rate l
- mean size s

- blocking probability for capacity C
- utilization r= ls/C
- m = admission control limit
- B(r,m) =rm(1-r)/(1-rm+1)

Provisioning for negligible blocking for elastic flows

- "elastic" teletraffic theory; assume
- Poisson arrivals, rate l
- mean size s

- blocking probability for capacity C
- utilization r= ls/C
- m = admission control limit
- B(r,m) =rm(1-r)/(1-rm+1)

- impact of access rate
- C/access rate = m
- B(r,m) E(m,rm)

utilization (r) for B = 0.01

r

0.8

E(m,rm)

0.6

0.4

0.2

m

0 20 40 60 80 100

- traffic characteristics
- QoS engineering for streaming flows
- QoS engineering for elastic traffic
- service differentiation

Service differentiation

- discriminating between stream and elastic flows
- transparency for streaming flows
- response time for elastic flows

- discriminating between stream flows
- different delay and loss requirements
- ... or the best quality for all?

- discriminating between elastic flows
- different response time requirements
- ... but how?

Integrating streaming and elastic traffic

- priority to packets of streaming flows
- low utilization negligible loss and delay

Integrating streaming and elastic traffic

- priority to packets of streaming flows
- low utilization negligible loss and delay

- elastic flows use all remaining capacity
- better response times
- per flow fair queueing (?)

Integrating streaming and elastic traffic

- priority to packets of streaming flows
- low utilization negligible loss and delay

- elastic flows use all remaining capacity
- better response times
- per flow fair queueing (?)

- to prevent overload
- flow based admission control...
- ...and adaptive routing

Integrating streaming and elastic traffic

- priority to packets of streaming flows
- low utilization negligible loss and delay

- elastic flows use all remaining capacity
- better response times
- per flow fair queueing (?)

- to prevent overload
- flow based admission control...
- ...and adaptive routing

- an identical admission criterion for streaming and elastic flows
- available rate > R

Differentiation for stream traffic

- different delays?
- priority queues, WFQ, ...
- but what guarantees?

delay

delay

Differentiation for stream traffic

- different delays?
- priority queues, WFQ, ...
- but what guarantees?

- different loss?
- different utilization (CBQ, ...)
- "spatial queue priority"
- partial buffer sharing, push out

delay

delay

loss

loss

Differentiation for stream traffic

- different delays?
- priority queues, WFQ, ...
- but what guarantees?

- different loss?
- different utilization (CBQ, ...)
- "spatial queue priority"
- partial buffer sharing, push out

- or negligible loss and delay for all
- elastic-stream integration ...
- ... and low stream utilization

delay

delay

loss

loss

loss

delay

C

access

rate

r

0

0

1

1st class

3rd class

2nd class

Differentiation for elastic traffic- different utilization
- separate pipes
- class based queuing

C

access

rate

r

0

0

1

1st class

3rd class

2nd class

throughput

C

access

rate

r

0

0

1

Differentiation for elastic traffic- different utilization
- separate pipes
- class based queuing

- different per flow shares
- WFQ
- impact of RTT,...

Differentiation for elastic traffic

throughput

- different utilization
- separate pipes
- class based queuing

- different per flow shares
- WFQ
- impact of RTT,...

- discrimination in overload
- impact of aborts (?)
- or by admission control

C

access

rate

r

0

0

1

1st class

3rd class

2nd class

throughput

C

access

rate

r

0

0

1

Different accessibility

- block class 1 when 100 flows in progress - block class 2 when N2 flows in progress

1

0

Blocking probability

r = 1.5

r = 0.9

0 100 200 N

r1 = r2 = 0.4

0

100

N2

0

Different accessibility- block class 1 when 100 flows in progress - block class 2 when N2 flows in progress

r1 = r2 = 0.4

0

100

N2

0

Different accessibility- block class 1 when 100 flows in progress - block class 2 when N2 flows in progress
- in underload: both classes have negligible blocking (B1» B2» 0)

B2B10

r1 = r2 = 0.6

B2

.33

B1

0

N2

0

100

Different accessibility- block class 1 when 100 flows in progress - block class 2 when N2 flows in progress
- in underload: both classes have negligible blocking (B1» B2» 0)
- in overload: discrimination is effective
- if r1 < 1 < r1 + r2, B1» 0, B2» (r1+r2-1)/r2

1

r1 = r2 = 0.4

B2B10

0

N2

0

1

1

B2

r1 = r2 = 0.4

r1 = r2 = 0.6

r1 = r2 = 1.2

B2

.33

B1

.17

B2B10

B1

0

0

0

100

N2

N2

0

0

100

N2

Different accessibility- block class 1 when 100 flows in progress - block class 2 when N2 flows in progress
- in underload: both classes have negligible blocking (B1» B2» 0)
- in overload: discrimination is effective
- if r1 < 1 < r1 + r2, B1» 0, B2» (r1+r2-1)/r2
- if 1 < r1, B1» (r1-1)/r1, B2» 1

Service differentiation and pricing

- different QoS requires different prices...
- or users will always choose the best

- ...but streaming and elastic applications are qualitatively different
- choose streaming class for transparency
- choose elastic class for throughput

- no need for streaming/elastic price differentiation
- different prices exploit different "willingness to pay"...
- bringing greater economic efficiency

- ...but QoS is not stable or predictable
- depends on route, time of day,..
- and on factors outside network control: access, server, other networks,...

- network QoS is not a sound basis for price discrimination

Pricing to pay for the network

- fix a price per byte
- to cover the cost of infrastructure and operation

- estimate demand
- at that price

- provision network to handle that demand
- with excellent quality of service

capacity

demand

time of day

demand

demand

capacity

$$$

time of day

time of day

Pricing to pay for the network- fix a price per byte
- to cover the cost of infrastructure and operation

- estimate demand
- at that price

- provision network to handle that demand
- with excellent quality of service

optimal price

revenue = cost

$$$

Outline

- traffic characteristics
- QoS engineering for streaming flows
- QoS engineering for elastic traffic
- service differentiation
- conclusions

Conclusions

- a statistical characterization of demand
- a stationary random process in the busy period
- a flow level characterization (streaming and elastic flows)

Conclusions

- a statistical characterization of demand
- a stationary random process in the busy period
- a flow level characterization (streaming and elastic flows)

- transparency for streaming flows
- rate envelope ("bufferless") multiplexing
- the "negligible jitter conjecture"

r

0

0

1

Conclusions- a statistical characterization of demand
- a stationary random process in the busy period
- a flow level characterization (streaming and elastic flows)

- transparency for streaming flows
- rate envelope ("bufferless") multiplexing
- the "negligible jitter conjecture"

- response time for elastic flows
- a "processor sharing" flow scale model
- instability in overload (i.e., E [demand]> capacity)

r

0

0

1

Conclusions- a statistical characterization of demand
- a stationary random process in the busy period
- a flow level characterization (streaming and elastic flows)

- transparency for streaming flows
- rate envelope ("bufferless") multiplexing
- the "negligible jitter conjecture"

- response time for elastic flows
- a "processor sharing" flow scale model
- instability in overload (i.e., E [demand]> capacity)

- service differentiation
- distinguish streaming and elastic classes
- limited scope for within-class differentiation
- flow admission control in case of overload

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