Emergency Services: Resource Management and QoS Control

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44thth MCM Wuerzburg, MCM Wuerzburg, GermanyGermany

11

Nikola RozicNikola Rozic, Dinko Begusic, Dinko Begusic

University ofUniversity of Split, CroatiaSplit, Croatia

Gorazd KandusGorazd Kandus

Institute Jozef Stefan, SloveniaInstitute Jozef Stefan, Slovenia

Emergency Services: Resource Emergency Services: Resource Management Management

and QoS Controland QoS Control

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ContentsContents Emergency Services and QoSEmergency Services and QoS

Prediction modelsPrediction models

ARIMA modelsARIMA models

Resource Management and Call Access Control (CAC)Resource Management and Call Access Control (CAC)

Proposed CAC methodProposed CAC method

Simulation resultsSimulation results

Conclusion remarks and Future WorkConclusion remarks and Future Work

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Emergency Services and QoSEmergency Services and QoS

How to provide reliable communication services under emergency, How to provide reliable communication services under emergency,

attack or catastrophe situations ?attack or catastrophe situations ?

1. 1. Reliable service infrastructure (fault tolerant systems: Reliable service infrastructure (fault tolerant systems:

hardware, hardware, ssoftware, protocolsoftware, protocols (robust, adaptive, (robust, adaptive, resistant to resistant to

DoS attacks)DoS attacks)

2. Quick response for recovery operations,2. Quick response for recovery operations,

33. System (capacity) design according to the criterion of the . System (capacity) design according to the criterion of the

worst case ?worst case ?

44. Resource management . Resource management (special algorithms)(special algorithms)

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Emergency Services and QoSEmergency Services and QoS

Global aspects:Global aspects:

1. Reliable signal an emergency situation 1. Reliable signal an emergency situation

2.2. Priority services among networks Priority services among networks

3.3. SSececuure access to services (only eligible users – police, fire re access to services (only eligible users – police, fire

and ambulance, and to everybody in case of emergencies)and ambulance, and to everybody in case of emergencies)

4. Limit the network damage caused by DoS attacks4. Limit the network damage caused by DoS attacks

QoS in emergency situations ?QoS in emergency situations ?

Operational aspects:Operational aspects:

1. Reliable voice, data, and video services 1. Reliable voice, data, and video services

2.2. Reliable mobility and localisation services Reliable mobility and localisation services

3.3. Reliable resource management and call access controlReliable resource management and call access control

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Emergency Services and QoSEmergency Services and QoS Traffic modelsTraffic models

Normal load condition:Normal load condition:

the objective is to keep probability of being unsatisfied (pthe objective is to keep probability of being unsatisfied (punsatisfiedunsatisfied) as ) as

low as possible by prioritizing the ongoing calls low as possible by prioritizing the ongoing calls

since drop can take place only if the call was admitted beforehand.since drop can take place only if the call was admitted beforehand.

)P(1PPP blockdropblockdunsatisfie

Emergency load condition:Emergency load condition:

the objective is twofold: the objective is twofold:

minimize blocking (all user should be able to call for help)minimize blocking (all user should be able to call for help), and, and

minimize dropping (all important information should be exchanged)minimize dropping (all important information should be exchanged)..

When consider dropping caused by handoffs in cellular networks the When consider dropping caused by handoffs in cellular networks the

two objectives are counteracted. two objectives are counteracted.

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Emergency Services and QoSEmergency Services and QoS Traffic modelsTraffic models

PPdropdrop includes: includes:

Probability of being uncontrolled dropped due to bad link qualityProbability of being uncontrolled dropped due to bad link quality

Probability of being dropped due to controlled operation of the Resource Probability of being dropped due to controlled operation of the Resource

Management Control (RMC):Management Control (RMC):- duration control (time-limited calls)- duration control (time-limited calls)- pre-emption control (priority calls)- pre-emption control (priority calls) hhigher priority level for the emergency calls: if new igher priority level for the emergency calls: if new

emergencyemergency call cancall can’t be admitted normally, one of lower ’t be admitted normally, one of lower priority calls is priority calls is dropped dropped

- drop of the handoff call if it can’t admitted due to the system - drop of the handoff call if it can’t admitted due to the system congestion congestion

In this work we stress the importance of not dropping the handoff calls to assure In this work we stress the importance of not dropping the handoff calls to assure tracking the mobile people – they can help in gettingmore complete information about tracking the mobile people – they can help in gettingmore complete information about the state of the imperilled region the state of the imperilled region

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Emergency ServicesEmergency Services and QoSand QoS

Problem statementProblem statement

QoS for roaming callsQoS for roaming calls fast handoff between base stationsfast handoff between base stations//access pointsaccess points

the handoff failurethe handoff failure dropping of the connectiondropping of the connection

QoS criterionQoS criterion dropping of the connection after admissiondropping of the connection after admissionshould be consideredshould be considered much less acceptable much less acceptable than blocking thethan blocking the new connectionnew connection !!

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Resource Management and QoS Resource Management and QoS ControlControl

TThe approachhe approach

an effective way to reducean effective way to reduce

the handoff call droppingthe handoff call dropping

probability (CDP)probability (CDP)

advanced resource reservationadvanced resource reservation

for future handoffsfor future handoffs

efficient advanced efficient advanced resource reservationresource reservation

good prediction methods forgood prediction methods forfuture new and handoff call future new and handoff call arrivalsarrivals

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Prediction ModelsPrediction Models

Good predictions ?Good predictions ?

Predictable situations:Predictable situations:

Normal (“stationary”) mod of operationNormal (“stationary”) mod of operation- random traffic - random traffic

- seasonal patterns (daily, weekly, yearly)- seasonal patterns (daily, weekly, yearly)

- special events (sport, conferences, open-air concerts, political meetings, ...)- special events (sport, conferences, open-air concerts, political meetings, ...)

People speaking about predictable and People speaking about predictable and unpredictable situationsunpredictable situations

Special (“non-stationary”) mod of operationSpecial (“non-stationary”) mod of operation- non-stationary traffic- non-stationary traffic

- random burst and impulse patterns- random burst and impulse patterns

- sudden events (new accidents, earthquakes, new attacks, ...)- sudden events (new accidents, earthquakes, new attacks, ...)

Unpredictable situations:Unpredictable situations:

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However, all things that happen in real life are predictable:However, all things that happen in real life are predictable:

The only question is how reliable the prediction is !The only question is how reliable the prediction is !

In our approach we assume:In our approach we assume:- Management system uses “good” predictions- Management system uses “good” predictions

- Network(s) under accidental or natural disasters does - Network(s) under accidental or natural disasters does not fail completely, but can provide emergency services not fail completely, but can provide emergency services

- Network resources are managable and efficient control - Network resources are managable and efficient control algorithms can be performed algorithms can be performed

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Analytical models: Analytical models: based on hypothesis of probability laws,based on hypothesis of probability laws,

queuing theory, stationarity, independence, ...queuing theory, stationarity, independence, ...

Measurement-based models: Measurement-based models: based on stochastic systemsbased on stochastic systems

(linear/non-linear, state-space or time series) fitted (linear/non-linear, state-space or time series) fitted

to the traffic measurementsto the traffic measurements

ExpertExpert models: models: knowledge-knowledge-based based modelsmodels

(subjective assessments, experience-based inference, (subjective assessments, experience-based inference,

soft (fuzzy) decisions soft (fuzzy) decisions

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Analytical models: Analytical models: explicit relationships,explicit relationships, simple implementationsimple implementation hypothesis of the true model, assumptions of stationarity,hypothesis of the true model, assumptions of stationarity,

ergodicity, indenpendence, ...ergodicity, indenpendence, ...

Measurement-based models:Measurement-based models: incorporate real system behavior, adaptivness, ...incorporate real system behavior, adaptivness, ... no closed form relationships, computing complexityno closed form relationships, computing complexity

Expert models:Expert models: incorporate real life features, unstructured models, ...incorporate real life features, unstructured models, ... problem to define the expertproblem to define the expert’s reliability’s reliability, ,

Prediction Models: Prediction Models: Advantages and Advantages and drawbacksdrawbacks

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Prediction ModelsPrediction Models:: ssome referenced ome referenced modelsmodels

Analytical models:Analytical models: “ “Guard Channel SchemeGuard Channel Scheme” ,” ,O.T.W. Yu and V.C.M. LeungO.T.W. Yu and V.C.M. Leung, ,

IEEE JIEEE JSAC-SAC-15, 1997. 15, 1997. “ “Adaptive QoS Handoff Priority SchemeAdaptive QoS Handoff Priority Scheme” , ” ,

W. Zhuang, B. Bensaou, and K.C. ChuaW. Zhuang, B. Bensaou, and K.C. Chua , , IEEE Trans. on IEEE Trans. on Vehicular Techn., Vol. 49, No. 2, pp. 494-505, March 2000. Vehicular Techn., Vol. 49, No. 2, pp. 494-505, March 2000.

“ “MultiMedia One-Step PREDiction (MMOSPRED)”MultiMedia One-Step PREDiction (MMOSPRED)” , , B.M. Epstein and M. Schwartz B.M. Epstein and M. Schwartz , , IEEE JIEEE JSAC-18SAC-18, March, 2000., March, 2000.

“ “Admission Limit Curve (ALC)”Admission Limit Curve (ALC)” , J. Siwko, I. Rubin , J. Siwko, I. Rubin , , IEEE IEEE Trans. on Net.Trans. on Net., , Vol. 9, June 2001Vol. 9, June 2001..

“ “Dynamic Channel Pre-reservation Scheme (DCPr)”Dynamic Channel Pre-reservation Scheme (DCPr)” , , X. Luo, I. Thng,X. Luo, I. Thng, and W. Zhuangand W. Zhuang , , Proc. IEEE Int. Symp. Computers Commun., July 1999.Proc. IEEE Int. Symp. Computers Commun., July 1999.

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Prediction ModelsPrediction Models:: sosome referenced me referenced modelsmodels

Measurement-based models:Measurement-based models:

“ “Measurement-Based Admission Control (MBAC)Measurement-Based Admission Control (MBAC)” ,M. Grossglauser, ” ,M. Grossglauser, D.N.C. Tse, D.N.C. Tse, IEEE IEEE Trans. on Net.Trans. on Net., , Vol. 7, June 1999.Vol. 7, June 1999.

“ “Hierarchical Location Prediction (HLP)Hierarchical Location Prediction (HLP)” , ” , T. Liu, P. Bahl, I. ChlamtacT. Liu, P. Bahl, I. Chlamtac , , IEEE JIEEE JSAC-16SAC-16, , August 1998.August 1998.

“ “Wiener & ARMA models)”Wiener & ARMA models)” , , T. Zhang, E. van den Berg, T. Zhang, E. van den Berg, J. Chenninkara, P. Agrawal, J.C. Chen and T. KodamaJ. Chenninkara, P. Agrawal, J.C. Chen and T. Kodama, ,

IEEE JIEEE JSAC-19SAC-19, , Oct.Oct. 200 20011..

“ “Region-Based Call Admission Control)”Region-Based Call Admission Control)” , J-H. Lee, S-H. Kim, , J-H. Lee, S-H. Kim, A-S.Park, J-K. Lee, A-S.Park, J-K. Lee, IEIEICICE E Trans. on Com.Trans. on Com., , Vol. E84-B, Nov. 2001Vol. E84-B, Nov. 2001..

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Prediction ModelsPrediction Models:: ssome referenced ome referenced modelsmodels

ExpertExpert-based models:-based models:

“ “Measurement-Based Admission Control (MBAC)Measurement-Based Admission Control (MBAC)” ,M. Grossglauser, ” ,M. Grossglauser, D.N.C. Tse, D.N.C. Tse, IEEE IEEE Trans. on Net.Trans. on Net., , Vol. 7, June 1999.Vol. 7, June 1999.

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Prediction ModelsPrediction Models:: Our approach Our approach

Measurement-based ARIMA (univariate/multivariate) modelMeasurement-based ARIMA (univariate/multivariate) model

“ “N.Rožić, D.Begušić, G.Kandus: “N.Rožić, D.Begušić, G.Kandus: “Application of ARIMA Models for Application of ARIMA Models for Handoff Control in Multimedia IP NetworksHandoff Control in Multimedia IP Networks”, Proceedings of ”, Proceedings of the International Symposium on Intelligent Signal Processing and the International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS'03), pp. 787-791, Awaji Island, Communication Systems (ISPACS'03), pp. 787-791, Awaji Island, Japan, December 7-10, 2003. Japan, December 7-10, 2003.

“ “N.Rožić, G. Kandus: "N.Rožić, G. Kandus: "MIMO ARIMA models for handoff resource MIMO ARIMA models for handoff resource reservation in multimedia wireless networksreservation in multimedia wireless networks", ", Wireless Communications and Mobile Computing (WCMC), Wireless Communications and Mobile Computing (WCMC), Vol. 4, No.5, August 2004, pp. 497-512, John Wiley&Sons, Vol. 4, No.5, August 2004, pp. 497-512, John Wiley&Sons,

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Univariate Univariate Autoregressive Integrated Moving Average (ARIMA)Autoregressive Integrated Moving Average (ARIMA)

tS

tDS

dS )L(B)L(bY)L(A)L(a

),0(N 2t DSD

Sdd LL )1(;)1(

itti

Q

1i

Sii

Sq

1i

ii

P

1i

Sii

sp

1i

ii

yyLwith;LB)L(B;Lb)L(b

;La)L(A;La)L(a

,

ARIMA (p,d,q)x(P,D,Q)S

tS1S1

t )L(B)L(b)L(A)L(ay one-step ahead conditional expectationone-step ahead conditional expectation::

tDS

dt Yy wherwher

ee;

1

0i

2i

2y

2y

with a with a variancevariance

)L(A)L(a/)L(B)L(b)L( SS1

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Multi-Input Multi-OutputMulti-Input Multi-Output ARIMA ARIMA ((MIMO-MIMO-ARIMA)ARIMA)

MIMO ARIMA (p,d,q)

Polynomial matrices Polynomial matrices A, B, CA, B, C should satisfy certain conditions when applied to should satisfy certain conditions when applied to prediction or control problemsprediction or control problems:: we choose we choose

with a with a cocovariancevariance

ttt nCxByA (L)(L)(L)

tttt XxYy DS

dDS

d , stationary output and input vectorsstationary output and input vectors

nt is i.i.d. withis i.i.d. with < nt>=0 and covariance and covariance matrixmatrix

pLLL(L) p21 AAAIA 2

rLLL(L) r210 BBBBB 2

qLLL(L) q21 CCCIC 2

2mm

22m

21m

2m2

222

221

2m1

212

211

Σ

jiij

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Resource Management and CACResource Management and CAC

The The Call Admission Control Call Admission Control (CAC(CAC))

equilibrium equilibrium equationequation

term,htermn,term

new arrival ratenew arrival rate N

a number of accepted callsa number of accepted calls

demanded number of cdemanded number of calalls ls

)t(yn

)t(yN

actual number of used actual number of used channelschannels Ct

)t(C)t(y)t(y)t(y trelinh,n

)t(y)t(y)t(y)t(y out,hterm,htermn,rel

handoff call arrival ratehandoff call arrival rate

call call releaserelease rate rate

in,h

call termination rate call termination rate

out,hrel term

a number of released callsa number of released calls

N

h, out

te rm

y (t )

y

h , in

N

t

n

C A C

+-

+

+

X

( t)

( t)

h, out

N

t

n

C A C

++

+

-

X

CDP=Pr{C >C }t tot

C P=PrB {n<}

(t)

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The The Call Admission Control Call Admission Control (CAC(CAC)) – cont. – cont. t- the the reservation reservation time that has to be ensured for the CAC system to be able to time that has to be ensured for the CAC system to be able to

reserve sufficient amount of resources that will be required in the next time reserve sufficient amount of resources that will be required in the next time

iintervalnterval:: term

hˆN̂

t

Example:Example:

If the prediction interval is If the prediction interval is

calls/sec0.15ˆterm 25hN,, 7166.t

tthe CAC algorithm has to start not later than the CAC algorithm has to start not later than

50.t

troundkc steps before steps before the the handoff call burst starthandoff call burst starts.s.

Let Let min 1t t35.060

160roundkc

secondsseconds

(3 steps)(3 steps)

(with handoff calls normally distributed)(with handoff calls normally distributed)

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Example:Example:

The total number of The total number of channels,channels,

new accepted new accepted channels, channels,

handoff channels and handoff channels and the the

time precedence for time precedence for the case the case

of burst-like handoff of burst-like handoff traffictraffic

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Simulation Simulation ScenariosScenarios

Let Let consider three typical traffic consider three typical traffic scenariosscenarios: :

(i) (i) ““stationarystationary”” process, process,

(ii) nonstationary seasonal (ii) nonstationary seasonal

process, process,

(iii) nonstationary burst-like (iii) nonstationary burst-like

processprocess,,

ARIMA(p,1,0)x(ARIMA(p,1,0)x(0,0,0)0,0,0)

• average call holding time Taverage call holding time Tcallcall =200 s, =200 s,• call’s average channel holding time in each cell call’s average channel holding time in each cell TTchannelchannel =100 s, =100 s,

• average new call arrival rate average new call arrival rate NN is considered in is considered in

the range 0 to 0.45 the range 0 to 0.45 calls per second calls per second • total cell capacity is Ntotal cell capacity is N=30 =30 channels channels • the target call droping probability (TCDP) is 0.05the target call droping probability (TCDP) is 0.05

ARIMA(p,1,0)x(0,ARIMA(p,1,0)x(0,0,0)0,0) + +

intervention modelintervention model

ARIMA(p,1,0)x(ARIMA(p,1,0)x(11,,11,0),0)SS

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SimulationSimulation results: results: Scenario (i)Scenario (i)

ScenarioScenario (i) (i) ARIMA(p,1,0)x(ARIMA(p,1,0)x(0,0,0)0,0,0)

Actual and predicted total Actual and predicted total number of number of channels at channels at NN=0.27 and =0.27 and

hh=0.004=0.004

Handoff call dropping Handoff call dropping probability: comparison for probability: comparison for scenario (i)scenario (i)

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SimulationSimulation results: results: Scenario (ii)Scenario (ii)

ScenarioScenario (ii) (ii)


seasonal seasonal handoff handoff

Handoff call dropping Handoff call dropping probability: comparison for probability: comparison for scenario (ii)scenario (ii)

ARIMA(p,1,0)x(ARIMA(p,1,0)x(11,,11,0),0)SS ;; S=60 S=60 minutesminutes

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SimulationSimulation results: results: Scenario (iii)Scenario (iii)

ScenarioScenario iii) iii)


handoff bursthandoff burst

Handoff call dropping Handoff call dropping probability: comparison for probability: comparison for scenario (iii)scenario (iii)

ARIMA(p,1,0)x(0,0,0) + ARIMA(p,1,0)x(0,0,0) + intervention model ; intervention model ;

btt )L(y)L( ; ; nn – intervention variable – intervention variable

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Forecasts integrationForecasts integration

Concluding Remarks and Future WorkConcluding Remarks and Future Work

Emergency Services: Resource Management and QoS Control

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