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Predictive End-to-End Reservations via A Hierarchical Clearing House

Predictive End-to-End Reservations via A Hierarchical Clearing House. Endeavour Retreat June 19-21, 2000 Chen-Nee Chuah (Advisor: Professor Randy H. Katz) EECS Department, U. C. Berkeley. Problem Statement. How to deliver end-to-end QoS for real-time applications over IP-networks?.

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Predictive End-to-End Reservations via A Hierarchical Clearing House

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  1. Predictive End-to-End Reservations via A Hierarchical Clearing House Endeavour Retreat June 19-21, 2000 Chen-Nee Chuah(Advisor: Professor Randy H. Katz) EECS Department, U. C. Berkeley

  2. Problem Statement • How to deliver end-to-end QoS for real-time applications over IP-networks? H.323 Gateway PSTN Web surfing, emails,TCP connections Internet GSM VoIP (e.g. Netmeeting) Wireless Phones Video conferencing,Distance learning

  3. Why Is It Hard? ? ? ISP1 • Lack of QoS assurance in current IP-networks • SLAs are not precise • Scalability issues • Limited understanding on control/policy framework • How to regulate resource provisioning across multiple domains? SLA H1 H3 ISP2 SLA ISP 3

  4. Example Workload: Real-Time Packet Audio • Application Specific Traffic Patterns • Wide range of audio intensive applications • Multicast lecture, video conferencing, etc. • Significantly different from 2-way conversations • Traffic characteristics too diverse, cannot be described by one model • Resource pre-partitioning doesn’t work!

  5. Proposed Solution: Predictive Reservations Advance Reservation Dynamic Reservation • Online measurement of aggregate traffic statistics • Advance reservations based on local Gaussian predictor • RA = m + Q-1(ploss).s • Allow local admission control H1 H2 Edge Router LCH Edge Router

  6. Predictor Characteristics • 1-min predictor - 0.4 % Loss - 7 % Over-Prov. • 10-min predictor - 0.7% Loss - 33 % Over-Prov. • More BW for BE traffic than pre-partitioning - avg. 286 Kbps - max 857.2 Kbps

  7. Reservations Across Multiple Domains via A Clearing House Architecture LCH LCH LCH CH1 CH1 CH2 • Introduce logical hierarchy • Distributed database • CH-nodes maintain reservation status, link utilization, network performance destination source Edge Router ISP n ISP2 ISP1

  8. Clearing House Approach • Delivers statistical QoS • Aggregate reservation requests • Coordinates aggregate reservations across multiple domains • Performs coarse-grained admission control in a hierarchical manner • Assumptions • Networks can support differentiated service levels • Traffic and network statistics are easily available • Independent monitoring system or ISPs • Control and data paths are separate

  9. Advantages • Maintain scalability by aggregating requests • Core routers only maintain coarse-grained network state information • Provide statistical end-to end QoS • Advance reservations & admission control • Reduce setup time • Advance reservations allow fast admission control decisions • Optimize resource utilization • Predictive reservations achieve loss rate < 1% without extensive over-provisioning

  10. Future Work: Simulation Study Boston Chicago Seattle NY DC Denver SF St. Louise Atlanta LA Orlando Houston • vBNS backbone network topology (1999) • Traffic matrix weighted by population • Three-level Clearing House architecture- one top CH-node- one CH-node per city- local hierarchy of LCHs • Workload models: two QoS classes • High priority packet audio • 25 traces (conference & telephone calls), 0.5 - 113 minutes • Best-effort data traffic

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