Predictive end to end reservations via a hierarchical clearing house
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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

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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‚Äôt 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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