Utility-based Bandwidth Allocation for Triple-Play Services

1
Utility-based Bandwidth Allocation for
Triple-Play Services

• Changbin Liu, Lei Shi, Bin Liu
• Department of Computer Science and Technology,
  Tsinghua University
• Proceedings of the Fourth European Conference on
  Universal Multiservice Networks (ECUMN07)
• Chen Bin Kuo (20077202)
• Young J. Won (20063292)

2
Outline

• Introduction
• NGN traffic classifications and their utility
  functions
• Network utility maximization (NUM)
• Numeric results and analysis
• Discussion
• Conclusion

3
Introduction

• Next generation network (NGN) must natively
  support triple-plays.
• How to schedule traffic and allocate bandwidth at
  both backbone and access links.
• Designing a scheduling (bandwidth allocation)
  algorithm is exactly the issue this paper tries
  to settle.

4
Introduction (contd.)

• In industry designing NGN 1314, the
  strict-priority scheduling is mostly adopted.
• Rigidly favors the voice and video traffic
  without flexibility
• Utility-based solutions
• Shenker 1 discussed traffic classifications in
  IP network from the viewpoint of user utility
• Kelly et al. 56 applying utility-based
  methods to scheduling and bandwidth allocation in
  the objective of Network Utility Maximization
  (NUM)

5
Introduction (contd.)

• No single work has emphasized on the practical
  issue of scheduling triple-play services under
  the background of NGN.
• Translating this issue into a nonlinear
  maximization problem with inequality constraints.

6
NGN traffic classifications and their utility
functions

1. VoIP traffic
2. IPTV traffic
3. TCP elastic traffic
4. HTTP traffic
5. Other UDP traffic

7
NGN traffic classifications and their utility
functions

• Due to remarkable distinction of QoS
  requirements in NGN
• Classifying NGN traffic into five categories
• User utility function is introduced
• To measure network performance and user
  satisfaction degree
• Determined by the QoS metrics received in the
  user end
• Including packet delay, jitter and loss rate

8
VoIP traffic

• Sensitive to packet delay and loss caused by
  bandwidth insufficiency
• Utility function falls into the category of hard
  real-time kind 1210, with a minimal
  bandwidth requirement of Bmin1

9
IPTV traffic

• Utility function is similar to VoIPs but with
  some differences
• Tolerate occasional delay-bound violations and
  packet drops
• Minimal encoding rate, denoted as Bmin2 is
  independent of network congestion
• Logistic model is used

10
TCP elastic traffic

• Generated by delay-tolerant TCP applications
• Such as file transfer and email
• Utility function have been studied by Kelly et
  al. 6 and other researchers 1112

11
HTTP traffic

• TCP traffic which concerns packet delay
• Mainly contains the HTTP traffic generated by web
  services
• Utility function is different from TCP elastic
  traffic, has a minimum tolerable bandwidth Bmin4

12
Other UDP traffic

• DNS packets, other streaming media traffic, and
  on-line gaming traffic 1718
• Delay-sensitive
• Every application type has a utility function
• The shape of utility function resembles IPTV
  traffic

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Traffic setting
VoIP IPTV TCP elastic HTTP UDP
Bmin 64Kbps 100 Kbps 24Kbps
Bmax 10Mbps 10Mbps 10Mbps 500Kbps
? 0.001 0.001
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Network utility maximization

1. KKT method
2. Lagrange multipliers method without KKT conditions

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Network utility maximization

• Based on NGN traffics utility functions, we can
  solve the congestion-phased bandwidth allocation
  issue while conforming to NUM.
• Total utility gained on the link is
• Bandwidth allocation is restricted by

N the number of NGN users utilizing this
link pi traffics classes C the bandwidth of
a link (set to 10Gbps)
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KKT method

• Lagrange Multiplier method with KKT
  (Karush-Kuhn-Tucker) conditions
• Solving the nonlinear optimization problem
• Accurate and comprehensive solution requires
  substantial complicated computations
• Applying simplified form which is enough to ravel
  NUM problem for triple-plays

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KKT method (contd.)

• Observing NGN traffics utility functions
• VoIP/IPTV/other UDP traffics utility functions
  are relatively smoother in some points
• It is not cost-effective to allocate bandwidth to
  VoIP/IPTV/other UDP traffic without booming the
  utility
• Turning point (TP)

IPTV
HTTP
Bandwidth
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Lagrange multiplier method

• After finding the TP, we can apply the Lagrange
  Multipliers method without KKT conditions to
  solve the NUM problem in (10)
• Subject to

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Numeric results and analysis

1. Data-dominated network
2. IPTV-dominated network

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Numeric results and analysis

• Two network scenarios
• Current Internet, where HTTP and TCP elastic
  traffic still dominate the volume
• Prospective NGN, where the emerging services,
  especially the IPTV traffic, will dominate the
  network
• For each scenario, calculate in two situations
• Maximal Utility Equalization (MUE)
• Maximal Utility In-equalization (MUI)

V1 (VoIP) V2 (IPTV) V3 (TCP elastic) V4 (HTTP) V5 (other UDP)
MUE 1 1 1 1 1
MUI 1 9 1 1.5 2
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Numeric results and analysis

• Data-dominated network
• According to recent trace observation 15
• IPTV-dominated network

VoIP IPTV TCP elastic HTTP other UDP
Traffic proportions 10 10 10 50 20
VoIP IPTV TCP elastic HTTP other UDP
Traffic proportions 10 50 10 20 10
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Data-dominated network
23
IPTV-dominated network
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Discussion

• Previous bandwidth allocation schemes for
  triple-play services mostly adopt the
  strict-priority scheduling
• Highest priority to VoIP traffic
• Second highest priority to IPTV and lowest
  priority to others
• In this paper
• Highest priority to VoIP traffic
• Assigning IPTV traffic with second-highest
  priority is not well supported from the objective
  of NUM
• Suggesting that ISP charges more about IPTV
  services (future work)

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Conclusion

• Studied the problem of scheduling and bandwidth
  allocation for triple-play services in the
  objective of NUM.
• Presenting theoretical method to compute
  bandwidth allocation results
• Results
• VoIP and other low-throughput UDP traffic can
  always be guaranteed of sufficient bandwidth
• As congestion becomes severer, IPTVs bandwidth
  decreasing quickly
• TCP elastic and HTTP traffic experience
  exponential bandwidth degradations when
  congestion degree increases

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Q AThanks for your attention!