The document discusses open loop flow and congestion control. It describes how open loop flow control works, with the source describing its traffic behavior using a descriptor during call setup. The network then verifies if it can support the requested quality of service. Common traffic descriptors like peak rate, average rate, and linear bounded arrival process are covered. The document also discusses issues like traffic burstiness, self-similar traffic, and how traffic policing and shaping are used to control traffic and enforce compliance with the descriptor.

1. Open Loop Flow and
Congestion Control
TELCOM2321 – CS2520
Wide Area Networks
Dr. Walter Cerroni
University of Bologna – Italy
Visiting Assistant Professor at SIS, Telecom Program
Slides based on Dr. Znati’s material

2. Reading
1. About self-similar traffic:
Textbook, Chap. 9, Sections 9.1, 9.3, 9.4
and subsection of 9.2 on heavy-tailed
distributions
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3. Flow and congestion control implementation
• Provided at different layers
• Data Link Layer Flow and Error Control
– Stop-And-Wait ARQ
– Continuous ARQ
• End-to-End Flow and Congestion Control
– Closed Loop
– Open Loop
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4. Open Loop Flow Control
1. During call setup, the source describes its behavior
using a traffic descriptor
– bandwidth and buffer requirements
– QoS requirements, in terms of delay, jitter and loss
2. Network nodes, along the path, verify the feasibility of
supporting QoS requirements
– renegotiation of parameters, if call not acceptable, with
potential rejection
– reservation of resources in case of acceptance
3. During data transfer, the source shapes its traffic to
match descriptor
4. Network nodes schedule traffic from admitted calls
– to meet bandwidth, buffer and QoS requirements
– verifying actual compliance with traffic descriptor
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5. Open Loop Flow Control: Design Issues
• Traffic descriptor
– universal descriptor for different types of applications is not likely
• Call admission control scheme
– QoS guarantees of newly accepted connections should not affect
currently supported connections
• too conservative schemes, based on worst case scenario, are
resource wasteful
• too optimistic schemes may fail to meet QoS guarantees
– traffic must be controlled
– specific scheduling discipline at intermediate nodes is required
• tradeoff between efficiency, simplicity and capability of supporting
delay bounds
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6. Traffic Descriptor
• It provides behavioral information
– it usually describes the worst case behavior rather
than the exact behavior
• It represents the basis of a traffic contract
– source agrees not to violate traffic descriptor
– network guarantees the negotiated level of QoS
• A traffic policing mechanism is used to verify that
the source adheres to its traffic specification
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7. Traffic Descriptor Properties
• Usability
– source must be able to describe its traffic easily
– network must be able to perform admissibility test easily
• Verifiability
– policing mechanism must be able to verify the source compliance
with its traffic descriptor
• Preservability
– network nodes must be able to preserve the traffic characteristics
along the path, if necessary
• Three traffic descriptors are commonly used
– Peak Rate
– Average Rate
– Linear Bounded Arrival Process (LBAP)
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8. Traffic Descriptor: Peak Rate
• Highest rate allowed of traffic generation
– network with fixed size packets
• peak rate measured in pps or bps
• peak rate in pps is the inverse of the closest spacing between the
starting times of consecutive packets
– network with variable size packets
• peak rate measured in bps
• it defines an upper bound on the total number of packets generated
over all window intervals of a specified size
• Descriptor easy to compute an police
• It is a loose boundary measure
• Highly affected by large deviations from average
• Useful for sources with smooth traffic only
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9. Traffic Descriptor: Average Rate
• Objective is to reduce the effect of outliers
• Transmission rate is averaged over a specified
period of time
• Two parameters are defined
– t = time window over which rate is measured
– N = number of bits/packets to be sent over t
• Two mechanisms are used to compute the
average rate
– jumping window
– moving window
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10. Traffic Descriptor: Average Rate
• Jumping Window
– source claims that no more than N bits/packets will be
transmitted to the network over t
– a new time window starts immediately after the last
one
– jumping window is sensitive to the starting time of the
first window
• Moving Window
– source claims that no more than N bits/packets will be
submitted to the network over all windows of size t
– time window moves continuously
– enforces tighter bounds on spikes in the input traffic
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11. Traffic Descriptor: LBAP
• Linear Bounded Arrival Process
• Source bounds the number N of bits/packets it
transmits in any interval of length t by a linear
function of t
N≤ρt+σ
– ρ is the long term average rate allocated by the
network
– σ is the longest burst a source is allowed to sent
– source has an intrinsic long-term average rate ρ, but
can sometimes deviate from this rate, as specified by
σ
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12. Traffic Descriptor and Burstiness
• One of the main causes of the congestion is that
traffic is often bursty
• Traffic descriptor must be chosen based on
source behavior
– peak rate is enough for CBR traffic
– average rate is enough for VBR traffic with relatively
limited rate variability
– LBAP is better if VBR traffic has higher variability
• Data bursts should be controlled to comply with
descriptor
• But what exactly is traffic burstiness
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13. Traffic Burstiness
• Takes into account the variability of source rate
• No universal definition
– Peak rate / Average rate
– Average source rate / Average rate of reference source
– ...
• Poisson arrivals are “less regular” than CBR
• M/D/1 input traffic is smoother that M/M/1
• Markov-Modulated Poisson Process (MMPP) is
bursty compared to a simple Poisson source
• Real-life traffic traces show even higher burstiness
– self-similar behavior
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14. Self-Similar Traffic
W.E. Leland et al., On the Self-similar Nature of Ethernet Traffic (Extended Version),
IEEE/ACM Transactions On Networking, Vol. 2, No. 1, February 1994.
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15. Self-Similar Traffic
• Different kinds of network traffic show self-similar
behavior
– Ethernet, WWW, ...
• High variability leads to strong autocorrelation
also for large time scales
– Long-Range Dependence
• Modeling with Heavy-Tailed Distributions
– ex. superposition of many Pareto-distributed ON/OFF
sources with 1 < α < 2
– Pareto distribution with parameters
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16. Heavy-Tailed Distributions
α>2 finite mean, finite variance
1<α≤2 finite mean, infinite variance
0<α≤1 infinite mean, infinite variance
Probability density function
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17. Effect on queue size
H: Hurst parameter
Self-similarity when
0.5 < H < 1
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18. Traffic Policing
• Source behavior must comply with traffic descriptor
• Traffic policing is performed at network edges to detect
violations to contract
• Packets conforming to agreed bounds are forwarded to
the network
– required resources are guaranteed
• Packets exceeding the agreed bounds can be
– dropped at edge
– marked as non-conforming packets and forwarded to the network
• resources are not guaranteed
• dropped at any point in case of congestion
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19. Traffic Shaping
• In order to comply with descriptor, source traffic could be
shaped to a predictable pattern
– smoothing burstiness out
– applied at source or network edges
• Exceeding packets are delayed
– sent later when they eventually conform to descriptor
– buffer required
• buffer limit may cause loss/marking
– latency introduced
• Traffic policing must still be enforced if shaping is left to
the source
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20. Traffic Policing vs. Traffic Shaping
Example based on peak rate
policing
Rate
Peak rate Time
Rate
Rate
Time
shaping
Time 20

21. Traffic Shaping: Leaky Bucket
• Purpose is to shape bursty traffic into a data
regular stream of packets
– flow is characterized by a rate ρ
– bucket is characterized by a size β
• Packets are drained out at rate ρ by a
regulator at the bottom of the bucket β
• When bucket is full, incoming packets are
discarded or marked
• The effect of β is to
– limit the maximum bucket size
– bound the amount of delay a packet can incur
• Given β loss/marking rate vs. ρ tradeoff ρ
• β = 0 for peak rate policing
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22. Traffic Shaping: Leaky Bucket
• Traffic shaping using leaky bucket generates
fixed-rate data flows
– QoS requirements easily guaranteed
• Suitable for smoothing small rate variations
– depending on β
• Highly variable rate sources must choose rate ρ
very close to their peak rate
– wasteful solution
– bursts are not permitted
– a shaper allowing limited rate variation at the output
would be better
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23. Traffic Shaping: Token Bucket
• Bucket collects tokens
• Tokens are generated at rate ρ
– discarded when bucket is full
ρ tokens
• Each packet requires a token to be sent
σ
• A burst lesser than or equal to the number of
tokens available can be transmitted (up to σ)
data
β
• When bucket is empty, packets are buffered
and sent at rate ρ
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24. Traffic Shaping: Token Bucket
• Number of packets sent in interval of length t
N≤ρt+σ LBAP regulator
• β = 0 for LBAP policing
• Given β and the maximum loss/marking rate allowed, the
minimal LBAP descriptor is not unique
– ρ and σ must be chosen
– average rate A ≥ ρ buffer grows without bound avoiding
packet losses would require σ to be infinite
– peak rate P ≤ ρ there are always tokens available σ can
be small at will
– as ρ increases in the range [ A, P ], the minimum σ needed to
meet the loss bounds decreases
– any ρ and its corresponding σ is a minimal LDAP descriptor
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25. Traffic Shaping: Token Bucket
σ
β and loss/marking rate fixed
σ0
A ρ0 P ρ
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26. Summary
• Open loop flow and congestion control
– Traffic descriptor
– Burstiness
– Policing
– Shaping
– Leaky bucket
• constant data rate
• easier resource management
– Token bucket
• variable data rate
• specific actions required for QoS enforcement
– packet scheduling
– advanced buffer management
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