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Design and Implementation ofMeasurement-Based Resource AllocationSchemes Using the Realtime Traffic Flow
Measurement Architecture
Robert D. Callaway†, Michael Devetsikiotis†, and Chao Kan‡
†Department of Electrical and Computer Engineering ‡Alcatel Research and Innovation Center
North Carolina State University Alcatel USA, Inc.
Raleigh, NC 27695-7911 Plano, TX 45045
{rdcallaw,mdevets}@eos.ncsu.edu [email protected]
June 22, 2004
IEEE International Conference on Communications 2004: Paris, France
Presentation Outline
• Motivation and Background
• Effective Bandwidth Estimators
• Overview of Realtime Traffic Flow Measurement Architecture
• Modifications to Realtime Traffic Flow Measurement Architecture
• Emulation Setup and Tests
• Results and Conclusions
Callaway, Devetsikiotis, & Kan 1
Motivation and Background
• Goals of Self-Sizing Networks
? Optimize network utilization while ensuring QoS
? Ensure QoS of network traffic
? Adaptively change to network conditions while meeting the above criterion
• Benefits of Measurement-Based Resource Allocation
? Not dependent on a priori assumptions
? Able to track some transient behavior in traffic (non-abrupt changes)
Callaway, Devetsikiotis, & Kan 2
Our Contribution
• Review effective bandwidth proposals in literature
• Implement effective bandwidth algorithms in IETF-standardized environment
• Verify and validate the implementation
• Monitor allocations and QoS of traffic to measure algorithm accuracy
• Demonstrate by emulation that algorithms are implementable in real networks
Callaway, Devetsikiotis, & Kan 3
Effective Bandwidth Estimators
• A generic formula for effective bandwidth was proposed by Kelly as:
eb(s, t) =1
stlog E
[e
sX[0,t]]
• The s parameter in the general definition cannot be directly estimated from
measurements; therefore, the direct application of this formula in an online measurement
resource allocation scheme is not practical.
• Three algorithms were chosen for further analysis because of their computational
complexity, performance, and memory requirements.
? Gaussian Approximation
? Courcoubetis Approximation
? Norros Approximation
Callaway, Devetsikiotis, & Kan 4
Gaussian Approximation
Guerin, et. al defined the Gaussian Approximation as:
CEB = µ + σ√−2 ln ε− ln 2π
where µ is the mean arrival rate of the traffic, σ is the standard deviation of the arriving
traffic, and ε is the QoS parameter (packet loss probability).
• Assumes a bufferless link
• Serves as an upper bound
Callaway, Devetsikiotis, & Kan 5
Courcoubetis Approximation
Courcoubetis, et. al defined the following approximation for effective bandwidth:
CEB = µ +IDs
2B
where µ is the mean arrival rate of the traffic, ID is the index of dispersion, s is the
space parameter, and B is the buffer size of the queue.
• The index of dispersion is defined as:
ID = limn→∞
1
nE
( n∑i=1
Xi
)2
• The s parameter is calculated from an asymptotically exponential decrease assumption.
• This approximation does not address long range dependent traffic.
Callaway, Devetsikiotis, & Kan 6
Norros Approximation
Norros defined the following approximation for effective bandwidth:
CEB = µ +[B
H−1κ (H)
√−2aµ ln ε
] 1H
where κ (H) = HH(1−H)1−H, µ is the mean arrival rate of the traffic, B is the buffer
size of the queue, H is the Hurst parameter of the traffic , a is the coefficient of variation
of the traffic, and ε is the QoS parameter (packet loss probability) of the traffic flow.
• The coefficient of variation is approximated by the index of dispersion; this
approximation is only valid when the arriving traffic is short range dependent.
• The Norros Approximation is the only formula we considered that uses the Hurst
parameter in its calculations; therefore, it is the only formula that takes self-similarity
into consideration.
• It is also the only formula that addresses long range dependent traffic.
Callaway, Devetsikiotis, & Kan 7
Overview of Realtime Traffic Flow MeasurementArchitecture
The 3 main components within the RTFM architecture are the meter, reader, and
manager.
• The meter serves to collect statistics on network flows that pass through links that are
connected to it.
• The reader retrieves the statistics from the meter at a regular interval via SNMP.
Callaway, Devetsikiotis, & Kan 8
Implementation within RTFM Architecture
• We are interested in the number of arriving bytes in a given time period (tslot) for a
particular traffic flow; RFC 2722 provides a byte counting statistic called toOctets.
• We utilize a sliding window system with a size of N slots in our online implementation.
Initialization ofEffective
BandwidthThread
Delay for tslotseconds
Input toOctetsfrom the last tslot
into slidingwindow system
RecomputeMean
RecomputeVariance
UsingCourcoubetis or
Norros ?
RecomputeIndex of
DispersionYes
time_to_realloc=0
RecomputeEffective
Bandwidth
Changeservice rateon queue
Yes
time_to_realloc=N time_to_realloc--
No
No
The mean, variance, and index of dispersion are recalculated after every tslot. Each
network flow (or class) is filtered into its own queue, so after N slots, the service rate of
the queue is dynamically changed to the measured effective bandwidth.
Callaway, Devetsikiotis, & Kan 9
Emulation Setup
• We added the effective bandwidth algorithms into the meter component of the RTFM
architecture.
• We installed NeTraMeT onto several Linux PC’s in order to validate and verify the
integrity of our environment.
• Traffic used in the emulation tests was generated using the Sup-FRP method proposed
by Byu & Rowen.
Effective
Bandwidth
Incoming
Traffic Outgoing
Traffic
C
C
C
RTFM meter
RTFM meter reader / manager
SNMP
EB Algorithms
ingress 172.16.0.1
10/100 Mbps Switch
carolina 172.16.0.2
ncstate 172.16.0.3
wolfpack 172.16.0.25
core1 10.0.1.2
Logical Diagram Network Diagram
Callaway, Devetsikiotis, & Kan 10
Emulation Cases
We present the results from three cases of our emulation tests:
• Case I: The performance of each algorithm is tested against the same traffic trace.
• Case II: The scalability of the implementation is tested when multiple flows are sent
simultaneously through the measurement architecture.
• Case III: The ability of the implementation to track abrupt transient behavior in the
traffic characteristics (mean arrival rate)
Callaway, Devetsikiotis, & Kan 11
Emulation Results: Case I
0 100 200 300 400 500 600 700 800 900 10000
0.5
1
1.5
2
2.5
3x 105 Plot of Traffic Trace vs. Estimated Effective Bandwidths − Meter Implementation: 1 Stream
Time (sec)
Thro
ughp
ut (b
ytes
/sec
)
Actual TrafficGaussian MethodCourcoubetis MethodNorros Method
0 1 2 3 4 5 6 7 8 9
x 104
10−4
10−3
10−2Packet Loss Probability vs. Target PLP: 10−3 − Meter Implementation: 1 Stream
Packet Number
Pac
ket L
oss
Pro
babi
lity
Target PLPGaussian PLPCourcoubetis PLPNorros PLP
From these graphs, we can see that each of the EB algorithms can provide the requested
QoS while providing significant bandwidth savings over peak-rate allocation.
Callaway, Devetsikiotis, & Kan 12
Emulation Results: Case II
0 100 200 300 400 500 600 700 800 900 10000
0.5
1
1.5
2
2.5
3x 105Plot of Traffic Trace vs. Estimated Effective Bandwidths − Gaussian Method − Meter Implementation: 3 Streams
Time (sec)
Thro
ughp
ut (b
ytes
/sec
)
Actual TrafficStream 1Stream 2Stream 3
0 1 2 3 4 5 6 7 8 9
x 104
10−4
10−3
10−2
10−1Packet Loss Probability vs. Target PLP: 10−3 − Gaussian Method − Meter Implementation: 3 Stream
Packet Number
Pac
ket L
oss
Pro
babi
lity
Target PLPStream 1 PLPStream 2 PLPStream 3 PLP
These graphs illustrate the robustness of the implementation to track multiple flows
simultaneously and still provide the QoS for each flow.
Callaway, Devetsikiotis, & Kan 13
Emulation Results: Case III
0 100 200 300 400 500 600 700 800 900 10000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 105 Plot of Traffic Trace vs. Estimated Effective Bandwidths − Meter Implementation: 1 Stream
Time (sec)
Thro
ughp
ut (b
ytes
/sec
)
Actual TrafficGaussian MethodCourcoubetis MethodNorros Method
0 0.5 1 1.5 2 2.5
x 105
10−4
10−3
10−2
10−1Packet Loss Probability vs. Target PLP: 10−3 − Meter Implementation: 1 Stream
Packet Number
Pac
ket L
oss
Pro
babi
lity
Target PLPGaussian PLPCourcoubetis PLPNorros PLP
These graphs show that two of the algorithms are unable to provide the requested QoS
when there is an abrupt increase in the mean arrival rate of the traffic.
Callaway, Devetsikiotis, & Kan 14
Conclusions & Summary of Our Contribution
• Implemented EB algorithms in open-source implementation of RTFM environment
• Verified and validated the implementation
• Showed the robustness and scalability of the system
? The measurement time scale is relative to the traffic characteristics; therefore, a
static tslot value fails to accurately capture the characteristics of non-stationary
traffic.
? Additional work has shown that dynamically changing the length of tslot at the
completion of N window slots allows the system to accurately track abrupt changes
in the characteristics of the traffic.
? With a dynamic tslot, QoS constraints can be met even when dramatic changes in
the traffic characteristics are observed.
• Demonstrated by emulation that algorithms are feasible to be implemented in real
networks
Callaway, Devetsikiotis, & Kan 15