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Statistical Call Admission Control Framework based on Achievable Capacity Estimation. Huiling Zhu 1 , Victor O. K. Li 1 , Zhengxin Ma 2 , Miao Zhao 3 1 Dept. of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China

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statistical call admission control framework based on achievable capacity estimation

Statistical Call Admission Control Framework based on Achievable Capacity Estimation

Huiling Zhu1, Victor O. K. Li1, Zhengxin Ma2, Miao Zhao3

1Dept. of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China

2Dept. of Electronic Engineering, Tsinghua University, Beijing, China

3Dept. of Electrical and Computer Engineering, State University of New York, Stony Brook, New York, USA

Nov. 10, 2005

slide2
Outline

Introduction of Call Admission Control

Statistical Call Admission Control (SCAC)

Framework

Implementation of SCAC

Simulation Results and Performance Analyses

Conclusion

introduction of call admission control
host

new call

Introduction of Call Admission Control
  • Call (Connection) Admission Control (CAC)

Determine if a new traffic connection request can be accepted

host

host

host

host

host

host

host

Conditions:

  • Satisfy the QoS requirements of the new request
  • Maintain QoS levels of connections already accepted
introduction of call admission control1
Introduction of Call Admission Control

Parameter-based Admission Control:

  • Hard real time services
  • Decision based on worst case bounds
  • Typically, low network utilization

Measurement-based Admission Control (MBAC):

  • Statistical real time services (occasional packet loss or delay violation)
  • Decision based on existing traffic measurements
  • Higher utilization than parameter-based admission control
introduction of call admission control2
Introduction of Call Admission Control
  • Statistical Quality of Service (QoS) Guarantee
    • Violation Event

QoS metric of packet transmission :

      • packet end-to-end delay constraint D exceeded
      • packet loss
    • Violation Probability Pviolation

the occurrence probability of violation event

      • P(end-to-end delay > D)
      • Ploss = P(a packet is lost)
    • Statistical QoS Service

Given a threshold  (violation probability constraint)

Pviolation ≤ 

introduction of call admission control3
new call

Example:

QoS Metric: packet loss

C

Introduction of Call Admission Control

: traffic rate of connection k at time t

  • Measurement-based Admission Control (Previous Work)

: includes the accepted connection and the new connection request

Xk(t)

introduction of call admission control4
Introduction of Call Admission Control
  • Problems of MBAC
    • Resource reservation
    • State coordination among nodes

Extend single link model to the multiple-link environment

Example: packet end-to-end delay constraint D must be decomposed into D1, D2, etc.

D1

D2

DN

introduction of call admission control5
Introduction of Call Admission Control
  • Endpoint Admission Control
    • Basic idea

End host sends “probing” packets to mimic the rate it would like to reserve for a short period to measure the level of network service

    • Problems
      • Signaling overhead
      • Thrashing problem

When large number of new arrivals send probing packets simultaneously, the cumulative volume of probing packets may prevent further admissions, even though the traffic load is light.

admission control framework
Admission Control Framework
  • Objectives
    • Scalability

No per-flow signaling or state management requirement at core nodes

    • Flexibility

Be adaptive to different service models and traffic sources

    • Low overhead

Small overhead for collecting information to make admission decision

    • High Utilization
admission control framework1
Performance Information

new call

Ĉ

Admission Control Framework
  • Basic Idea

Motivated by MBAC

Xk(t)

Ĉ(t) : Achievable Capacity

the utilized network resource for a pair of edge nodes

admission control framework2
Admission Control Framework
  • Statistical Admission Control Framework
implementation of scac
Implementation of SCAC
  • QoS violation probability constraint

Ploss = P{end-to-end packet loss} loss

implementation of scac1
Implementation of SCAC
  • Implementation of SCAC
    • Information Collection
      • Sampling Period  (time slot duration)

R(n) = A(n) /  , n = 0, 1, 2, ······

R(n): the aggregated traffic rate at time slot n

A(n): the amount of traffic in bits transmitted between the given pair of ingress-egress nodes in the interval[n, (n+1)]

      • Collection Window

window size: Tloss = N ·

collection interval:[i · N ·, (i+1) ·N ·], i = 0, 1, 2, ······

      • Measurement Window

window size: TM = M ·

collection interval:[(i-M+1)·, i ·], i = 0, 1, 2, ······

implementation of scac2
Implementation of SCAC
  • Estimation of Achievable Capacity
  • MBAC based on Gaussian Model[1]

C: link capacity

loss: packet loss ratio constraint

mi: the average rate of connection i

i: the standard deviation of the traffic rate of connection i

X(t): the aggregated traffic with average rate and standard deviation at time t

  • Equivalent Capacity Cg(t)

the minimum network capacity to guarantee

Cg(t) > C: reject

Cg(t)  C: accept

When the number of aggregated connections K is large enough, use Gaussian approximation

[1] R. Guerin, H. Ahmadi and M. Naghshineh, “Equivalent Capacity and Its Application to Bandwidth Allocation in High-Speed Networks,” IEEE Journal on Selected Areas in Communications, vol. 9, No. 7, Sept. 1991, pp. 968-981

implementation of scac3
Implementation of SCAC
  • Estimation of Achievable Capacity

Gaussian Model: the number of aggregated connections is large enough

Achievable Capacity

The ingress node receives the measured packet loss ratio in time slot n, then estimates the achievable capacity at the end of the nth time slot followingthe approximation in [1].

At the end of the nth time slot

implementation of scac4
Implementation of SCAC
  • Admission Decision Criteria

A new connection request with mean and variance arrives during

(n+1)th time slot. The ingress node estimates the packet loss ratio

If , accept the new request. Otherwise, reject it.

Or, estimate the equivalent capacity

If , accept the new request. Otherwise, reject it.

simulations and performance analyses1
Simulations and Performance Analyses
  • Traffic Model
  • Parameters

link capacity: 10Mbps

sampling period: 0.01s

collection window: 1s

measurement window: 1s

simulations and performance analyses2
Simulations and Performance Analyses
  • Link Utilization
  • Satisfaction Ratio
  • Traffic Load
simulations and performance analyses3
Simulations and Performance Analyses
  • Link Utilization

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)

  • Simulation Parameters:
    • Source traffic: Type 1
    • Transit traffic : Type 1
simulations and performance analyses4
Simulations and Performance Analyses
  • Satisfaction Ratio

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)

simulations and performance analyses5
Simulations and Performance Analyses
  • Impact of Burstiness of Transit Traffic

rt(n) : the rate of the transit traffic at time slot n

Rt: {rt(n)} n=1,2,…

Use the coefficient of variation J to describe the traffic burstiness

  • Performance

Estimation Error

Satisfaction Ratio

  • Simulation Parameters

Source traffic: Type 1

Transit traffic: Type 2 and Type 3

Buffer size: 100 packets

simulations and performance analyses6
Simulations and Performance Analyses
  • Impact of Burstiness of Transit Traffic
    • Estimation Error
simulations and performance analyses7
Simulations and Performance Analyses
  • Impact of Traffic Burstiness on Estimation Error

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)

simulations and performance analyses8
Simulations and Performance Analyses
  • Impact of Traffic Burstiness on Satisfaction Ratio

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)

slide26
Conclusion
  • Develop SCAC Framework
    • Signaling overhead is small
    • Flexible to accommodate different service models and traffic sources
    • No cooperation requirement on core nodes
    • High utilization

Future Work

  • Impact of the fluctuation of aggregated traffic on the performance of SCAC
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