Statistical Call Admission Control Framework based on Achievable Capacity Estimation

1. 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

2. Outline Introduction of Call Admission Control Statistical Call Admission Control (SCAC) Framework Implementation of SCAC Simulation Results and Performance Analyses Conclusion

3. 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

4. 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

5. 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 ≤ 

6. 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)

7. 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

8. 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.

9. 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

10. 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

11. Admission Control Framework • Statistical Admission Control Framework

12. Implementation of SCAC • QoS violation probability constraint Ploss = P{end-to-end packet loss} loss

13. 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, ······

14. 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

15. 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

16. 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.

17. Simulations and Performance Analyses • Network Model

18. Simulations and Performance Analyses • Traffic Model • Parameters link capacity: 10Mbps sampling period: 0.01s collection window: 1s measurement window: 1s

19. Simulations and Performance Analyses • Link Utilization • Satisfaction Ratio • Traffic Load

20. Simulations and Performance Analyses • Link Utilization Packet Interval (s) exponential( Packet Intervals (s) Pareto( ) • Simulation Parameters: • Source traffic: Type 1 • Transit traffic : Type 1

21. Simulations and Performance Analyses • Satisfaction Ratio Packet Interval (s) exponential( Packet Intervals (s) Pareto( )

22. 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

23. Simulations and Performance Analyses • Impact of Burstiness of Transit Traffic • Estimation Error

24. Simulations and Performance Analyses • Impact of Traffic Burstiness on Estimation Error Packet Interval (s) exponential( Packet Intervals (s) Pareto( )

25. Simulations and Performance Analyses • Impact of Traffic Burstiness on Satisfaction Ratio Packet Interval (s) exponential( Packet Intervals (s) Pareto( )

26. 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

27. ThanksQ&A