Statistical call admission control framework based on achievable capacity estimation
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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


Outline Achievable Capacity Estimation

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 Achievable Capacity Estimation

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 Achievable Capacity Estimation

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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

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 Achievable Capacity Estimation

  • Statistical Admission Control Framework


Implementation of scac
Implementation of SCAC Achievable Capacity Estimation

  • QoS violation probability constraint

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


Implementation of scac1
Implementation of SCAC Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • 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 analyses
Simulations and Performance Analyses Achievable Capacity Estimation

  • Network Model


Simulations and performance analyses1
Simulations and Performance Analyses Achievable Capacity Estimation

  • Traffic Model

  • Parameters

    link capacity: 10Mbps

    sampling period: 0.01s

    collection window: 1s

    measurement window: 1s


Simulations and performance analyses2
Simulations and Performance Analyses Achievable Capacity Estimation

  • Link Utilization

  • Satisfaction Ratio

  • Traffic Load


Simulations and performance analyses3
Simulations and Performance Analyses Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • Satisfaction Ratio

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)


Simulations and performance analyses5
Simulations and Performance Analyses Achievable Capacity Estimation

  • 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 Achievable Capacity Estimation

  • Impact of Burstiness of Transit Traffic

    • Estimation Error


Simulations and performance analyses7
Simulations and Performance Analyses Achievable Capacity Estimation

  • Impact of Traffic Burstiness on Estimation Error

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)


Simulations and performance analyses8
Simulations and Performance Analyses Achievable Capacity Estimation

  • Impact of Traffic Burstiness on Satisfaction Ratio

Packet Interval (s)

exponential(

Packet Intervals (s)

Pareto(

)


Conclusion Achievable Capacity Estimation

  • 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


Thanks q a
Thanks Achievable Capacity EstimationQ&A


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