tu/etu/e eindhoven university of technology 6 content characteristics: video semantics (i) a typical...
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TU/eeindhoven university of technology 1
Advanced Topics in Multi-Service Networks I
Lecture 3: Traffic characteristics, admission control and Integrated Services
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Scheduling of isolated traffic flowsOverview and characteristic propertiesFCFS, Round Robin, GPS, WFQ, EDF
Queuing Theory RevisitedLittle’s Law, Balance EquationsM/M/1, M/M/n, M/D/1
Clever Queuing: Congestion ControlFlow control with TCPQueue Management to avoid congestion
Overview
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Traffic characterisationTraffic classes and their characteristicsDeterministic Traffic DescriptorsStochastic Traffic Descriptors
Admission ControlOverviewParameter vs. measurement based AC
Integrated Services FrameworkResource reservation with RSVP and variantsScalability limitations and other problems
Overview
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Correlation Between Content and Traffic (I)
Image: 256x256 grey scale
JPEG Q=5, 0.25 bits/pixel (2080 bytes)
JPEG Q=20, 0.54 bits/pixel (4442 bytes)
original GIF(54749 bytes)
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Correlation Between Content and Traffic (II)
Image: 512 x 512 colour
JPEG, Q=5, 0.22 bits/pixel (7128 bytes)
original GIF (202749 bytes)
JPEG, Q=20, 0.43 bits/pixel (13984 bytes)
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Content Characteristics: Video Semantics (I)
A typical video sequence is usually described as a collection of independent video shots called scenes.Each scene is an ordered set of video frames depicting a real-time continuous actionExcept from static real-world scenes, a typical video sequence consists of various artificial effects (e.g. camera movement, zooming, picture in picture, graphics etc.)The assumption of scene independence does not seem to hold in most video sequencesThe scenes are in fact correlated. There is a low probability of having a short scene while the probability of having long ones decades relatively slow
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Video Compression Techniques
Bandwidth requirements for Uncompressed video: 100-240 MbpsUncompressed HDTV streams: around 1Gbps
Compression is achieved by removing redundancyRedundancy types in video signal:
within a video frame (intra-frame): spatialbetween frames in close proximity (inter-frame) : temporal
Compression methods:Lossless or lossy (w/o exact recovery)Symmetric or asymmetric (same computational effort in coder and decoder or not)
MPEG-1 Quality similar to that of a VHSBit-rate approximately 1.2 Mbps
MPEG-2Broadcast quality videoBit-rate: 4-6 MbpsWide range for rate and resolution
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Characteristics of Compressed Video
Statistical behavior is described by using histograms and correlation functionsCorrelations arise as a consequence of visual similarities between consecutive images (or parts of images) in video streamsCompression results in a reduction in these correlationsCompressed stream still contains considerable amount of correlations Correlation along with different information content of each frame leads to burstiness
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Video Semantics (I)
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Burstiness
One simple definition: Ratio between peak and average bit rate (PAR)Burstiness indicates the presence of non-negligible positive correlations between cell inter-arrival times
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Other Bit Rate Burstiness Measures
Bit-rate distributionAverage bit-ratePeak bit-rateVariance
Autocorrelation FunctionExpresses temporal variations
Coefficient of VariationUncorrelated streams have fixed CoVUseful when measuring multiplexing characteristics when VBR signals are buffered and statistically multiplexed
Scene duration distributionAverage duration of peaks
Useful to estimate probability of buffer overflow
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Classifying Parameters for Traffic
Mean packet rate Variance of instantaneous packet rateCorrelation of instantaneous packet rateOthers (e.g. higher order moments and cumulants)?Which classifying parameters are important?
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Model ?
Loss probability, delay
Unfinished work distribution
mean
variance
autocovarianceOther? Other?
All performance techniques must make some assumptions regarding traffic or workload
Queuing models: arrival and service processesSimulation: traffic generatorsExperimental: work load generators
Assumptions are captured in traffic models
Traffic Models
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Developing Traffic Models
What is the basis for traffic models?Select from a constrained set
Analytical techniques limit the types of models´that can be utilized, e.g., MarkovianSpecific model may be based more on “tradition” than realityImportant to understand limitations and inaccuracies of such traffic assumptions
Analysis of trace dataA trace is captured from a “live” networkLive traffic may not exist, e.g., for a new network or applicationHow do we know our sample is typical or large enough?
Use insight into applicationsModels based on expert knowledge of the system
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Continuous Time Traffic Source Models
Events (arrivals and departures) can occur at any instant of time in continuous time modelsPoisson Process (PP or M)
Appropriate if there is a large number of independent users and each generates Poisson traffic and no source dominatesPoisson arrival process basis of standard queuing models (M/M/1,M/D/1, M/M/m/m, etc.)
Generally Modulated Poisson Process (GMPP):A Poisson arrival process, but with time-varying arrival rate,The arrival rate is determined by the modulating process
Markov Modulated Poisson Process (MMPP)A GMPP where the modulating process has a Markov chain
)(tλ)(tλ
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Rate is controlled by the modulating process (two-state Markov chain here, but could be more states)The arrival process is Poisson for given rate
Switched Poisson Process (SPP)Special case of a MMPP where the modulating process is a two-state Markov chainRate is when system is in state 0 and rate is when system is in state 1
Interrupted Poisson Process (IPP)
1λ0λ
Markov-Modulated Poisson Process
Special case of a SPP where rate and rate 00 =λ 01 >λ
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Discrete Time Traffic Source Models
Events (arrivals and departures) occur at slot timesOften assumes that one packet (cell) can be transmitted in a single slot, as in ATM
Deterministic Process (DP)Events occur at fixed multiples of the basic slot, e.g. transmission of an ATM cell requires one slot
Bernoulli Process (BP)Discrete time counterpart to the Poisson ProcessTractable first-cut model for discrete time systems
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Stochastic Models – Long Range Dependence
In many processes (e.g., Poisson), the autocovariance rapidly decays with t
Short-range dependent processes, covariance decays at least exponentially
A long-range dependent process has a hyperbolically decaying autocovariance
Long-range dependence reflects the existence of clustering and bursty characteristics at all time scales in self-similar processes
Self-similarity:A phenomenon that is self-similar looks or behaves the same when viewed at different degrees of “magnification” or different scales on a dimension (time or space)We are concerned with time series and stochastic processes that exhibit self-similarity with respect to time
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A self-similarstructure containssmallerreplicas of itself at all scales
Self-similarity Conditions:
[ ] [ ]HaatXEtXE )()( =
[ ] [ ]H
XXXX a
atXRtXR 2)()( =
[ ] [ ]Ha
atXVartXVar 2)()( =
Mean
Variance
Autocorrelation
15.0 ≤≤ H
Hurst Parameter:
H = 0.5 :No Self-similarity
Self Similarity
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Is Ethernet Traffic Self Similar
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Self-Similarity of Ethernet Traffic
Estimated Hurst parameter of H = 0.9The higher the load, the higher HAggregating several streams does not remove self-similarity
Failure of Poisson ModelingInstead Superposition of many Pareto-like ON/OFF sources
Pareto distribution exhibits long-range dependenceEach source alternates between “ON” and “OFF” statesIn ON, a burst of packets is transmitted, an OFF period is an idle period
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Pareto Distribution
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mA(t), l D(t)
Model of FIFO router queue
Deterministic upper/lower bound on trafficQ(t)
D(t)
A(t)
time
Q(t)d(t)
Cumulativenumber of bits
Facilitates computation of deterministic upper/lower bounds on network performance
)()()( stsAtA −≤− α
Deterministic Traffic Model
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Peak Rate is most conservative traffic envelope
tpt =)(α
time t
data
A(t)
0
b
time t
data
A(t)
0
b
p
Leaky Bucket is a frequently deployed traffic envelope (for policing)
btt += ρα )(
ρ
Deterministic Models – Traffic Envelope (I)
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Dual Leaky Bucket/Token Bucket as straight forward and computationally tractable extension
[ ]bttpt += ρα ,min)(
data
A(t)
0 time t
data
A(t)
0 time t
bp
N-segment piecewise linear service curve as compromise between accuracy and computational complexity
[ ]iiibtt += ρα min)(
Deterministic Models – Traffic Envelope (II)
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Leaky Bucket
data
B
rate, L
Two parameters:B: bucket size [Bytes]L: leak rate [B/s or b/s]
Data pours into the bucket and is leaked outB/L is maximum latency at transmissionTraffic always constrained to rate L
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Three parameters:b: bucket size [B]r: bucket rate [B/s or b/s]p: peak rate [B/s or b/s]
Bucket fills with tokens at rate r, starts fullPresence of tokens allow data transmissionBurst allowed at rate pdata sent < rt + b
data
tokens, rate r
b
peak rate, p
Token Bucket
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Traffic characterisationTraffic classes and their characteristicsDeterministic Traffic DescriptorsStochastic Traffic Descriptors
Admission ControlOverviewParameter vs. measurement based AC
Integrated Services FrameworkResource reservation with RSVP and variantsScalability limitations and other problems
Overview
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Single Node Admission Control
Node receives a service request and checks:Buffer sizeAvailable capacity (depending on scheduling discipline)
Possibly local computation of performanceReject or admit flow
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End-to-end Admission Control
Parameter based Admission Control Deterministic Traffic ModelStateful Network (allocated resources known)
Measurement Based Admission ControlMeasurement of recent traffic load historyMeasured load + new flow vs network capacity
Network Probing (Measurement Variant) Alleviates us from continuous traffic measurementSending probing packets to obtain instantaneous network load indication
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Measurements vs Parameter Admission Control
Parameter based Admission Control: Facilitates provision of deterministic performance guaranteesDecision based on worst case boundsTypically, low network utilization
Measurement based Admission Control:No deterministic QoS guarantees, statistical guarantees onlyDecision based on traffic measurementsHigher utilization than parameter based
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Measurement Based Admission Control
Many different varieties of MBACs:Some based on “solid” math models (eg, theory of large deviations) Others “ad hoc” (no theory underpinning)Different load estimations: from simple point estimate, to exponential averaging , combined mean and variance measurements, etc
Two Components:Network load measurements (on aggregate rather than per flow)Admission control decision based on load measurement and trafficprofile indicated in service request
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Probe Based Admission Control
Variant of Measurement Based Admission Control, where:Not the (aggregate) traffic load is measured at the ingress, butThe load in the network is measured by means of probing packets
No need for stateful network management, e.g. no link state advertisementsAdmission decision based on measured network load and indicated traffic profile of new serviceCriticalities:
Probing measures instantaneous load, in case of high (aggregate)traffic dynamics load measure may not be reasonableDanger of cheating: probing packets may be explicitly dropped bydomains on purposeBottleneck link sometimes hard to determine
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Available Bandwidth
Probing
Active Probing
Packet Train
Packet Pair
Trains of Packet Pairs
Variable Packet Size
Probing Taxonomy
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General Definitionn : number of hops in a path
: capacity of linki: avail-bw of the path at time t: utilization of linki
Link1=2Mbps=20%
Link2=1Mbps=30%
Link3=3Mbps=50%
A(2) = C1 x (1-u1) = 1.6MbpsA(2) = C2 x (1-u2) = 0.7MbpsA(2) = C3 x (1-u3) = 1.5Mbps
Sender
Receiver
( ) ( ) ( ) ( )( )1...
min 1t ti ii n
A n c u n=
⎡ ⎤= −⎣ ⎦ic
iu
1u1c
2c2u 3c
3u
( )tA
A(2) = C2 x (1-u2) = 0.7Mbps
Probe Capacity Measurement
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P2 P1
Gap (time) gi
P2 P1
go
router
P2 P1 : back-to-back packet pair
Cross traffic packets
gi: initial gap (time between first bit of P1 and P2 when they enter the router)
go: output gap (time betweenfirst bit of P1 and P2 when they leave the router)
IGI - Initial Gap Increasing
Benefit: Reflect whether the link is bursting or not
Tradeoff: The length of giToo short: Cause probe overflow Too long: Underestimate the available bandwidth
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Traffic characterisationTraffic classes and their characteristicsDeterministic Traffic DescriptorsStochastic Traffic Descriptors
Admission ControlOverviewParameter vs. measurement based AC
Integrated Services FrameworkResource reservation with RSVP and variantsScalability limitations and other problems
Overview
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IETF Integrated Services
Architecture for providing QOS guarantees in IP networks for individual application sessionsResource reservation: routers maintain state info of allocated resources and performance requirementsAdmit/deny new call setup requests:
Question: can newly arriving flow be admittedwith performance guarantees while not violatedQoS guarantees made to already admitted flows?
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IntServ: QoS guarantee scenario
Resource reservationcall setup, signalingtraffic, QoS declarationper-element admission control
QoS-sensitive scheduling (e.g., WFQ)
request/reply
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Call Admission
Arriving session must :Declare its QOS requirement
R-spec: defines the QOS being requestedCharacterize traffic it will send into network
T-spec: defines traffic characteristicsSignaling protocol: needed to carry R-spec and T-spec to routers (where reservation is required)
RSVP
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IntServ Specifications
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Guaranteed service:worst case traffic arrival: leaky-bucket-policed source simple (mathematically provable) bound on delay [Parekh 1992, Cruz 1988]
Controlled load service:"a quality of service closely approximating the QoS that same flow would receive from an unloaded network element."
WFQ
token rate, r
bucket size, bper-flowrate, R
D = b/Rmax
arrivingtraffic
T-spec for traffic specification
IntServ QoS: Service Models [rfc2211, rfc2212]
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Resource Reservation Signalling
RSVP, Resource-Reservation ProtocolRFC 2205Enable resources to be reserved for a given session in priorThe most complex, and closest to circuit emulation
Strong QoS guaranteesSignificant granularity of resource allocationSignificant feedback to applications
Two levels of serviceGuaranteed - as close as possible to circuit emulationControlled load – equivalent to the service in a best-effort network under no-load conditions
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Soft State
Routers keep state about reservation.Periodic messages refresh state.Non-refreshed state times out automatically.Alternative: Hard state
No periodic refresh messages.State is guaranteed to be there.State is kept till explicit removal.Why could there be a problem?
Properties of soft state:Adapts to changes in routes, sources, and receivers.Recovers from failuresCleans up state after receivers drop outs
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RSVP Signalling Flow (I)
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RSVP Signalling Flow (II)
The RSVP SENDER_TSPEC objectCarries the traffic specification (sender TSpec) generated by each data source within an RSVP sessionTransported unchanged through the network, and delivered to both intermediate nodes and receiving applications
The RSVP ADSPEC objectCarries information which is generated at either data sources or intermediate network elements,Flows downstream towards receivers, and may be used and updated inside the network before being delivered to receiving applications
The RSVP FLOWSPEC objectCarries reservation request (Receiver_TSpec and RSpec) information generated by data receiversFlows upstream towards data sourcesMay be used or updated at intermediate network elements before arriving at the sending application
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TSpec
A token bucket specificationbucket size, btoken rate, rthe packet is transmitted onward only if the number of tokens in the bucket is at least as large as the packet
peak rate, p>rmaximum packet size, Mminimum policed unit, m
All packets less than m bytes are considered to be m bytes
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Flowspec
An indication of the QoS control service requestedControlled-load service and Guaranteed service
For Controlled-load serviceSimply a Tspec
For Guaranteed serviceA Rate (R) term, the bandwidth required
R ≥ r, extra bandwidth will reduce queuing delaysA Slack (S) term
The difference between the desired delay and the delay that would be achieved if rate R were usedUsed to reduced the resource reserved
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AdSpec
PATH(TSpec)RESV(flowspec)The receiver to be informed about the network
The receiver does not request what the network cannot provide
The sender and routersIndicate their QoS capabilities; advertisingThe sender constructs an initial ADSpecEach router update the ADSpecAlso indicate that one or more routers RSVP-incapable
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Router Mechanisms
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Reservation Errors
A given resource reservation failsAn error message is returned
PathErr messages simply sent back to the senderResvErr messages are sent to a receiver
Only to the receiver whose request fails
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Guaranteed Service Specification
Tspec parameters are:P = peak rate of flow (bytes/sec)B = bucket depth (bytes)r = token bucket rate (bytes/sec)m = minimum policed unit (bytes)M = maximum datagram size (bytes)
Rspec parameters are:R = service rate (bytes/sec)
The rate that will be reserved at every routerS = slack term (micro seconds)
The maximum delay tolerancea router if does not have enough available bandwidth as requested by the receiver in R, then it reserves R’ that will cause increase in delay di such that di < S.
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Guaranteed Service Performance
Each router reserves bandwidth R and buffer space BThe delay bound will be: b/R + C/R + DThe loss bound will be: b + C + (D*R)Error terms C and D (may be included in rate-latency model):
C is rate dependent error factor measured in bytesD is rate independent error factor measured in micro-secondsC and D will be included in scheduler’s latency
Traffic Shaping at the source: The traffic must not exceed at any time T: M+min(pT, rT+b-M)
With peak rate p and maximum packet size M more accurate bound equations are:
( )+
+
⎥⎦
⎤⎢⎣
⎡−
−−
−++=
++−−−
=
δδ
δ
rpMbRrrbQ
RM
rpRRpMbD
)(])[(
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Reservation Styles
Three stylesWildcard/No filter – does not specify a particular senderFixed filter – sender explicitly specified for a reservation
Video conference
Dynamic filter – valid senders may be changed over time
Audio conference
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Example
S1 A
S2, S3
B
CR1
D R2R3
Senders S1, S1, S3Receivers R1, R2, R3Router interfaces A,B,C,C
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Wildcard Filter Reservation
R1, R2, and R3 want to reserve 4b, 3b, and 2b, respectively (b is given rate).
4b
3b
4b
4b
S1 A
S2, S3
B
CR1
D R2R3
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Fixed Filter Reservation
R1 wants to reserve 4b for S1 and 5b for S2.R2 wants to reserve 3b for S1 and b for S3.R3 wants to reserve b for S1.
S1:4bS2:5bS1:4b
S2:5bS3:b
S1 A
S2, S3
B
CR1
D R2R3S1:3b
S3:b
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Dynamic Filter Reservation
R1 wants to reserve b for S1 and S2 (shared).R2 wants to reserve 3b for S1 and S3 (shared).R3 wants to reserve 2b for S2.
(S1,S2):b
(S1,S2,S3):3b
S1:3b
(S2,S3):3b
S1 A
S2, S3
B
CR1
D R2R3
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ScalabilityEvery router needs to keep per flow RSVP state
It does not recognize internet domain architectureAutonomously administered domainsIt is a gross assumption that every domain will provide the same bounded service
Service definition is up to the service providersIt is business orientedConsistent end-to-end services difficult to achieve
IntServ Criticalities
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Traffic characterisationSource Traffic ModelsDeterministic Traffic ModelsSelf Similarity
Admission ControlParameter based ACMeasurement based ACProbing based AC
IETF Integrated ServicesResource reservation and RSVPQoS ClassesCriticalities
Lecture Summary