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Predictive End-to-End QoS Reservations via Hierarchical Clearing House for Real-Time Applications

This paper presents a novel approach to delivering end-to-end Quality of Service (QoS) for real-time applications over IP networks. The difficulty of assuring QoS is addressed through a predictive reservation mechanism that accommodates diverse traffic characteristics. By utilizing a hierarchical clearing house architecture, it aggregates reservation requests and performs admission control across multiple domains while maintaining scalability. The proposed solution includes advance reservations and dynamic monitoring for optimal resource utilization, aiming for less than 0.1% loss rates without extensive over-provisioning.

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Predictive End-to-End QoS Reservations via Hierarchical Clearing House for Real-Time Applications

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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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