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Resource Provisioning and Bandwidth Brokering for IP-core Networks

Explore strategies for providing end-to-end QoS in scalable IP-core networks. Topics include resource reservation, traffic prediction, hierarchical clearing house approach, and evaluation metrics.

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Resource Provisioning and Bandwidth Brokering for IP-core Networks

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  1. Resource Provisioning and Bandwidth Brokering for IP-core Networks Chen-Nee Chuah ISRG Retreat Jan 10-12, 2000 Problem: How to provide end-to-end QoS in IP-core networks in a scalable manner?

  2. H1 H3 ISP2 ISP2 ISP2 H2 Example Scenario • SLA: Agreements that describe the volume of traffic exchanged, bandwidth reserved and price ISP2 SLA SLA ISP1 Resource Reservation ISP3

  3. Research Issues • Resource Provisioning • How to estimate bandwidth usage in advance for capacity planning purposes? • Adaptive Reservations • How to adapt aggregate reservations based on traffic fluctuation? • What are the trade-offs between granularity, QoS and signaling complexity? • Admission Control • End-to-end? • In stages: Per ISP cloud? Per domain?

  4. ISP2 ISP2 Hierarchical Clearing House Approach • Distributed database • reservation status, % link utilization, optimum path • Bandwidth brokering software agent • adapt reservation dynamically destination source Edge Router ISP n ISP2 ICH ICH ICH ISP1 CH1 CH1 CH2

  5. Resource Reservation Strategies • Aggregation of reservation requests • Hierarchical approach • De-couple notifications & reservation requests • Static and Dynamic Advanced Reservations Adapt Reservations- Advance reservations - Process reservation requests CH2 Notifications (every Du s) - Reservation status - Link utilization - Bandwidth predictor CH1 CH1 ICH ICH ICH ICH ERs aggregate reservation requests (Ta)

  6. Traffic Predictors • Monitoring system at Edge Router • Online measurement of aggregate rate of incoming & outgoing traffic (regular interval: West) • Two Traffic predictors for advanced reservations • Local Gaussian predictor for static reservation • Larger time-scale (e.g. an hour) • Compensate for the coarse granularity of the notifications • Auto-regressive predictor for dynamic reservation • Smaller time-scale (West)

  7. Evaluation • Overall Performance Metrics • Link utilization • % blocking/dropping • Bandwidth Estimator • How well does the predictor track the traffic fluctuation? • Choice of estimation window, % over-provisioning • Signaling between CHs • Sensitivity analysis: effect of aggregation on QoS and complexity • Completely de-coupled notifications • Limited notifications

  8. Boston Chicago Seattle NY DC Denver SF St. Louise Atlanta LA Orlando Houston Simulation Study: Network Topology • vBNS Backbone Network Map (1999) • Extreme cases - Dumbbell - Highway with merging paths

  9. Simulation Study: Workload Modeling • Two QoS classes • High priority voice calls and video conferencing • Best-effort data traffic (e.g. web, telnet, ftp) • Traffic model • Voice & video conferencing calls • Poisson arrivals with lv and lc Erlangs • Exponentially distributed call duration (mean = 2.5 min. for voice, 30 min. for video conferencing calls) • Individual source is modeled as two state-Markov chain. When “on”, a voice call requires bandwidth of 128 kbps, defined as one basic unit (BU) • Video conferencing calls occupy 4 BU • TCP connections get equal share of the non-reserved bandwidth

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