1

Resource Management in IP Telephony Networks

Matthew Caesar, Dipak Ghosal, Randy H. Katz

{mccaesar, randy}@cs.berkeley.edughosal@cs.ucdavis.edu

2

Motivation What is IP Telephony?

Packetized voice over IP PSTN access through Internet Telephony Gateway (ITG)

Benefits: Improved network utilization Next generation services (POTS PANS)

Growth: Revenues $1.7 billion in 2001, 6% of international traffic

was over IP, growing [Frost 2002] [Telegeography 2002] Standardized, deployed protocols (TRIP, SIP, H.323)

Requires scalable architecture to limit congestion.

3

Goals High quality, economically efficient

telephony over the Internet. Low blocking probability Provide preferential treatment, high QoS

Questions: How to perform call admission control? How best to route calls through converged

network?

4

Approach Mechanisms

Congestion sensitive call admission control

ITG selection Techniques

Awareness of ITG congestion

Path quality between important points in network

Dis

tance

ITG Utilization

**

**

*

* *

5

Overview IP Telephony Networks Pricing-based Admission Control Redirection Techniques Experimental Design Results Future Work

6

System Architecture

ITG

LS

Example Call SetupExample AdvertisementGateway (ITG)

IP TerminalLocation Server (LS)

InternetAdmin. Domain (AD)

Example Call Session

ITGITG

ITG

ITG

ITG

ITG

LS

LS

LS

LS

LS

LS

1 2

3

4

5

6

7

Scope of Study 1. All calls are net-to-phone2. ADs cooperate to provide service.3. Use IETF’s TRIP architecture to

support interoperability.4. Disregard degradation in access

network.5. Prices determined at start of call.6. ITGs offer equal PSTN reachability.

8

Pricing PSTN

distance pricing time of day pricing

IP Telephony richer user interface allows for more dynamic pricing

schemes Baseline: Flat-rate Admission

Control (FAC)

9

Congestion Sensitive Call Admission Control (CAC) Goal: prevent system overload and

generate revenue Price of call

function of number of voice ports in use

rises when highly utilized More dynamic than PSTN

10

Price-Congestion Function Used M/M/m/m (m-

server loss system) responsive server loss system discouraged arrivals

Found price-congestion function that maximized revenue with respect to

0

1

2

m-1

m

...

m-1

m

11

Congestion Pricing Analysis Exponential function generates most

revenue Stepwise linear function almost as good

Maximum system price charged early Approximation to function minimizes price

fluctuationsPrice-congestion Function Used in this Study

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60Utilization [voice ports]

No

rmal

ized

Pri

ce C

har

ged

Revenue-maximizing Price-congestion Function

00.10.20.30.40.50.60.70.80.9

1

0 10 20 30 40 50 60Utilization [voice ports]

No

rma

lize

d P

ric

e C

ha

rge

d

12

Redirection Problem: finding the “best” ITG Approach: tradeoffs between quality and load Method: LS maintains

Average measured path quality Number voice ports in use

Algorithms: Random Redirection (RR) (baseline) QoS Sensitive Redirection (QR) Congestion Sensitive Redirection (CR) Hybrid Scheme (CQR)

13

Redirection Schemes QoS Sensitive Redirection (QR)

Different paths provide different service Technique:

Use RTCP RRs to monitor path congestion Route over best paths

Congestion Sensitive Redirection (CR) Unbalanced load causes call blocks Technique:

Use TRIP advertisements to estimate ITG utilization

Route to least utilized ITG

14

Hybrid Redirection (CQR) Choosing nearby ITG improves call quality, but

can unbalance load. Algorithm:

Compute Rdm = *Mi+(1-)*Qi Mi is utilization, Qi is loss rate

Select randomly from k ITGs with lowest Rdm Tradeoffs:

Use to trade off call quality and load balance Use k to vary flash crowd protection

Price Sensitive CQR (PCQR) Decrease for higher bids

15

Overview IP Telephony Networks Pricing-based Admission Control Redirection Techniques Experimental Design Results Future Work

16

Experimental Method Modified ns-2 Ran for 1.5 simulated hours

Eliminated first half-hour User Model

Bid uniformly distributed Voice traffic on-off Markov process

Self-similar cross-traffic Data points stable across several time

scales

17

Evaluation: Metrics Blocking Probability Average call QoS

Used Mean Opinion Score (MOS) based on RTP loss rate

Economic efficiency Ratio of service tier to QoS achieved

18

Admission Control: Blocking Probability

Flat pricing unnecessarily blocks many callers

Congestion pricing changes system price dynamically with load

Call Blocking Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1Offered Load

Blo

ckin

g P

rob

ab

ilit

y

QR+FAC

QR+CAC

19

Call Blocking Probability

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Beta

Blo

ckin

g P

ro

bab

ilit

y

CQR+NAC k=1CQR+NAC k=3CQR+NAC k=6RR+NAC

Redirection: Blocking Probability

Congestion sensitivity decreases blocking probability Small k few blocked calls Congestion Sensitive Redirection (CR) improves balance over

Random Redirection (RR)

20

Redirection: Background Traffic Effects

Effects of Background Traffic

0

1

2

3

4

5

6

0 1 2 3 4 5Background Traffic Multiplier

Qo

S [

MO

S]

CQR+NAC Beta=0

CQR+NAC Beta=0.9

CQR+NAC Beta=1

RR+NAC

QoS sensitivity minimizes effects of cross traffic Small amount of sensitivity vastly

improves call quality

21

Summary Admission Control Schemes:

Congestion sensitive pricing decreases unnecessary call blocking, increases revenue, and improves economic efficiency

Derived exponential price-congestion function that maximizes revenue

Redirection Schemes: Hybrid scheme achieves “best of both worlds” Price sensitivity improves economic efficiency

22

Future Work Realistic workload Improve user model

Develop price-congestion function for real users

Study flash-crowd effects ITG Placement Competitive Network

23

Resource Management in IP Telephony Networks

Matthew Caesar, Dipak Ghosal, Randy H. Katz

{mccaesar, randy}@cs.berkeley.edughosal@cs.ucdavis.edu