xin wang and henning schulzrinne internet real -time laboratory columbia university

Xin Wang and Henning SchulzrinneInternet Real -Time Laboratory

Columbia University

http://www.cs.columbia.edu/~xinwang

04/19/23

Xin Wang, Henning Schulzrinne,

Columbia University2

Outline

Introduction A Resource Negotiation And Pricing

protocol: RNAP Pricing models User adaptation Test-bed demonstration of Resource Negotiation

Framework Simulation and discussion of Resource

Negotiation Framework Conclusion and future work

Resource Negotiation Framework

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Is Simple Over-Provisioning Enough?

Current Internet: Growth of new IP services and applications with different bandwidth and

quality of service requirements Revenue from the traditional connectivity services is declining

New services present opportunities and challenges Even though average bandwidth utilization is low, congestion

can happen; access links get congested frequently Wireless bandwidth is even more scarce Bandwidth prices are not dropping rapidly No intrinsic upper limit on bandwidth use

Option - manage the existing bandwidth better, with a Option - manage the existing bandwidth better, with a service model which uses bandwidth efficiently.service model which uses bandwidth efficiently.

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A More Efficient Service Model

Quality of Service (QoS) Condition the network to provide predictability to an

application even during high user demand Provide multiple levels of services Problems: signaling to facilitate service negotiation;

differential charging

Application adaptation Source rate adaptation based on network conditions -

congestion control and efficient bandwidth utilization Problems:

How adaptive applications work with QoS-assured services? How to motivate an application to adapt?

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Design Goals

Develop an efficient service model which combines QoS assurance with user rate adaptation

Increase service value to the users through greater choices over price and quality, improved connectivity, and expected QoS

Reduce network provision complexity, improve network efficiency and increase revenue to the providers; allows network operator to create different trade-offs between blocking admissions and raising congestion prices

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Our Work

Propose a Resource Negotiation And Pricing protocol: RNAP Allows for service predictability, multi-party negotiation Designed to be scalable and reliable Can be embedded in other protocols, or implemented independently

Enables differential charging for supporting differentiated services, reflecting the service cost and long-term user demand

Support short-term resource commitment for better response to user demand and network conditions, and more efficient resource usage; congestion pricing to motivate user adaptation

Develop reference user adaptation model Demonstrate negotiation framework on test-bed network Show significant advantages relative to static resource

allocation and fixed pricing using simulations

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Outline






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Protocol Architectures: Centralized(RNAP-C)

Access Domain - B

Access Domain - A

Transit DomainInternal Router

Edge Router

RNAP Messages

Network Resource Negotiator

Intra-domain messages

NRNNRN NRN

Host Resource Negotiator

HRN

HRN

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Protocol Architectures: Distributed (RNAP-D)

Access Domain - B

Access Domain - A

Transit DomainInternal Router

Edge Router

RNAP Messages

HRN

HRN

LRN

LRN

LRN

LRN

LRN

LRN

LRNLRN

LRN LRN

LRN

LRN LRN

Local Resource Negotiator

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RNAP Messages

Query

QuotationReserve

Commit

QuotationReserve

Commit

Close

Release

Query: Inquires about available services, prices

Quotation: Specifies service availability, accumulates service statistics, pricesReserve: Requests services and resources,

Modifies earlier requests Commit: Confirms the service request at a

specific price or denies it.

Per

iod

ic n

egot

iati

on

Close: Tears down negotiation session

Release: Releases the resources

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Message Aggregation (RNAP-D)

Turn on router alertTurn off router alert

Sink-tree-based aggregation

Edge Routers

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Message Aggregation (RNAP-D)

Turn off router alertTurn on router alert

Sink-tree-based aggregation

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Block Negotiation (Network-Network)

Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics

time

Ban

dwid

th

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Outline






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Two Volume-based Pricing Strategies

Fixed-Price (FP): fixed unit volume price During congestion: higher blocking rate OR higher

dropping rate and delay Congestion-dependent-Price (CP): FP +

congestion-sensitive price component During congestion: users have options to maintain service

by paying more OR reducing sending rate OR switching to lower service class

Overall reduced rate of service blocking, packet dropping and delay

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Proposed Pricing Strategies

Holding price and charge: based on cost of blocking other users by holding bandwidth even without sending data ph

j = j (pu j - pu j-1) , ch

ij (n) = ph j r ij (n) j

Usage price and charge: maximize the provider’s profit, constrained by resource availability max [Σl x j (pu

1 , pu2 , …, pu

J ) puj - f(C)], s.t. r (x (pu

2 , pu2 , …, pu

J )) R

cuij (n) = pu

j v ij (n)

Congestion price and charge: drive demand to supply level pc j (n) = min [{pc

j (n-1) + j (Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ]

cc

ij (n) = pc j v ij (n)

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Usage Price for Differentiated Service

Usage price based on cost of class bandwidth: lower target load (higher QoS) -> higher per-unit bandwidth price

Parameters: pbasic basic rate for fully used bandwidth

j : expected load ratio of class j xij : effective bandwidth consumption of application i Aj : constant elasticity demand parameter Price for class j: pu

j = pbasic / j

Demand of class j: xj ( puj ) = Aj / pu

j

Effective bandwidth consumption: xe j ( puj ) = Aj / ( pu

j j ) Network maximizes profit:

max [Σl (Aj / pu j ) pu

j - f (C)], puj = pbasic / j , s. t. Σl

Aj / ( pu j j ) C

Hence: pbasic = Σl Aj / C , puj = Σl Aj /(C j)

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Outline






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Rate Adaptation of Multimedia System

Gain optimal perceptual value of the system based on the network conditions and user profile

Utility function: users’ preference or willingness to pay

Bandwidth

CostU1 U2

U3 Budget

U

tili

ty/c

ost/

budg

et

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Example Utility Function

Utility is a function of bandwidth at fixed QoS An example utility function: U (x) = U0 + log (x / xm)

U0 : perceived (opportunity) value at minimum bandwidth

: sensitivity of the utility to bandwidth

Function of both bandwidth and QoS U (x) = U0 + log (x / xm) - kd d - kl l , for x xm

kd : sensitivity to delay

kl : sensitivity to loss

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Rate-Adaptation Models

Optimize perceived surplus of the multimedia system subject to budget and application requirements

User utility optimization: U = Σi Ui (xi (Tspec, Rspec)]

max [Σl Ui (xi ) - Ci (xi) ], s. t. Σl Ci (xi) b , xmin

i xi xmaxi

Determine optimal Tspec and Rspec

With the example utility functions, resource request of application i: Without budget constraint: x i = i / pi

With budget constraint: x i = bi / pi, with b i = b ( i / Σl k)

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Outline






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Testbed Architecture

Demonstrate functionality and performance improvement: blocking rate, loss, delay, price

stability, perceived media quality Host

HRN negotiates for a system Host processes (HRN, VIC, RAT)

communicate through Mbus Network

Router: FreeBSD 3.4 + ALTQ 2.2, CBQ extended for DiffServ

NRN: (1) Process RNAP messages; (2) Admission control, monitor statistics, compute price; (3) At edge, dynamically configure the conditioners and form charge

Inter-entity signaling: RNAP

Mbus

HRN

VIC

NRN

RAT

RNAP

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Outline






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Simulation Design

Performance comparison: fixed price policy (FP) vs. congestion price based adaptive service (CPA) loss, delay, blocking rate, user benefit, network revenue, stability

Three groups of experiments: effect of traffic load, admission control, and load balance between classes

Weighted Round Robin (WRR) scheduler Three classes: EF, AF, BE

EF: load threshold 40%, delay bound 2 ms, loss bound 10-6

AF: load threshold 60%, delay bound 5 ms, loss bound 10-4

BE: load threshold 90%,delay bound 100 ms,loss bound 10-2

Sources: mix of on-off traffic and Pareto on-off traffic

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Simulation Architecture

Topology 1 (60 users)Topology 1 (60 users) Topology 2 (360 users)Topology 2 (360 users)

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Effect of Traffic Load

CPA CPA maintains the traffic load at the targeted level, meets the expected performance bounds

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Effect of Admission Control

Admission control is important in maintaining the expected performance of a class.

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Effect of Admission Control (cont’d)

With admission control, the dynamics of the network price can be better controlled. Coupled with user adaptation, the blocking rate of CPA is up to 30 times smaller than that of FP.

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Effect of Admission Control (cont’d)

CPA allows for higher network revenue and user benefit.

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Load Balance Between Classes

Even when a small portion of users (15%) select other service classes, the performance of the over-loaded class is greatly improved.

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Conclusions

Proposed a dynamic resource negotiation framework consisting of: A Resource Negotiation And Pricing protocol (RNAP) , a rate and QoS adaptation model, and a pricing model

RNAP: supports dynamic service negotiation Pricing models: based on resources consumed by service

class and long-term user demand; including congestion-sensitive component to motivate user demand adaptation

Performance: Effectively restricts load to targeted level and meet service assurance Provide lower blocking rate, higher user satisfaction and network revenue Admission control and inter-service class adaptation give further

improvements in blocking rate and price stability

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Further Work

Interaction of short-term resource negotiation with longer-term network provision

A light-weight resource management protocol Cost distribution in QoS-enhanced multicast network Pricing and service negotiation in the presence of

alternative data paths or competing networks User valuation models for different QoS Resource provisioning in wireless environment

xin wang and henning schulzrinne internet real -time laboratory columbia university

Documents

service predictability

service cost

static resource allocation

different bandwidth

pricing protocol

network operator

network efficiency

bandwidth useoption