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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

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
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

why is it hard
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

example workload real time packet audio
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!
proposed solution predictive reservations
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

predictor characteristics
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
reservations across multiple domains via a clearing house architecture
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

clearing house approach
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
advantages
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
future work simulation study
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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