Emergency services resource management and qos control
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Emergency Services: Resource Management and QoS Control. Nikola Rozic , Dinko Begusic University of Split, Croatia Gorazd Kandus Institute Jozef Stefan, Slovenia. Emergency Services: Resource Management and QoS Control. Contents. Emergency Services and QoS. Prediction models.

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Emergency services resource management and qos control

Emergency Services: Resource Management and QoS Control

Nikola Rozic, Dinko Begusic

University ofSplit, Croatia

Gorazd Kandus

Institute Jozef Stefan, Slovenia

4th MCM Wuerzburg, Germany


Emergency services resource management and qos control1

Emergency Services: Resource Management and QoS Control

Contents

  • Emergency Services and QoS

  • Prediction models

  • ARIMA models

  • Resource Management and Call Access Control (CAC)

  • Proposed CAC method

  • Simulation results

  • Conclusion remarks and Future Work

4th MCM Wuerzburg, Germany


Emergency services and qos

Emergency Services and QoS

  • How to provide reliable communication services under emergency,

    attack or catastrophe situations ?

1. Reliable service infrastructure (fault tolerant systems: hardware, software, protocols (robust, adaptive, resistant to DoS attacks)

2. Quick response for recovery operations,

3. System (capacity) design according to the criterion of the worst case ?

4. Resource management (special algorithms)

4th MCM Wuerzburg, Germany


Emergency services and qos1

Emergency Services and QoS

  • QoS in emergency situations ?

Global aspects:

1. Reliable signal an emergency situation

Priority services among networks

Secure access to services (only eligible users – police, fire and ambulance, and to everybody in case of emergencies)

4. Limit the network damage caused by DoS attacks

Operational aspects:

1. Reliable voice, data, and video services

Reliable mobility and localisation services

Reliable resource management and call access control

4th MCM Wuerzburg, Germany


Emergency services and qos traffic models

Emergency Services and QoSTraffic models

Normal load condition:

  • the objective is to keep probability of being unsatisfied (punsatisfied) as low as possible by prioritizing the ongoing calls

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

Emergency load condition:

the objective is twofold:

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

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

    When consider dropping caused by handoffs in cellular networks the two objectives are counteracted.

4th MCM Wuerzburg, Germany


Emergency services and qos traffic models1

Emergency Services and QoSTraffic models

Pdrop includes:

  • Probability of being uncontrolled dropped due to bad link quality

  • Probability of being dropped due to controlled operation of the Resource Management Control (RMC):

    - duration control (time-limited calls)

    - pre-emption control (priority calls)

    higher priority level for the emergency calls: if new emergencycall can’t be admitted normally, one of lower priority calls is dropped

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

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 the state of the imperilled region

4th MCM Wuerzburg, Germany


Emergency services and qos2

Emergency Servicesand QoS

Problem statement

QoS for roaming calls

fast handoff between base stations/

access points

the handoff failure

dropping of the connection

QoS criterion

dropping of the connection after admission

should be consideredmuch less acceptable

than blocking thenew connection!

4th MCM Wuerzburg, Germany


Resource management and qos control

Resource Management and QoS Control

The approach

an effective way to reduce

the handoff call dropping

probability (CDP)

advanced resource reservation

for future handoffs

good prediction methods for

future new and handoff call

arrivals

efficient advanced

resource reservation

4th MCM Wuerzburg, Germany


Prediction models

Prediction Models

Good predictions ?

People speaking about predictable and

unpredictable situations

Predictable situations:

  • Normal (“stationary”) mod of operation

  • - random traffic

  • - seasonal patterns (daily, weekly, yearly)

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

Unpredictable situations:

  • Special (“non-stationary”) mod of operation

  • - non-stationary traffic

  • - random burst and impulse patterns

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

4th MCM Wuerzburg, Germany


Prediction models1

Prediction Models

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

The only question is how reliable the prediction is !

In our approach we assume:

- Management system uses “good” predictions

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

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

4th MCM Wuerzburg, Germany


Prediction models2

Prediction Models

Analytical models: based on hypothesis of probability laws,

queuing theory, stationarity, independence, ...

Measurement-based models: based on stochastic systems

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

to the traffic measurements

Expert models: knowledge-based models

(subjective assessments, experience-based inference,

soft (fuzzy) decisions

4th MCM Wuerzburg, Germany


Prediction models advantages and drawbacks

Prediction Models: Advantages and drawbacks

  • Analytical models:

    • explicit relationships,simple implementation

    • hypothesis of the true model, assumptions of stationarity,

      ergodicity, indenpendence, ...

  • Measurement-based models:

    • incorporate real system behavior, adaptivness, ...

    • no closed form relationships, computing complexity

  • Expert models:

    • incorporate real life features, unstructured models, ...

    • problem to define the expert’s reliability,

4th MCM Wuerzburg, Germany


Prediction models s ome referenced models

Prediction Models:some referenced models

Analytical models:

  • “Guard Channel Scheme” ,O.T.W. Yu and V.C.M. Leung,

    IEEE JSAC-15, 1997.

  • “Adaptive QoS Handoff Priority Scheme” ,

    W. Zhuang, B. Bensaou, and K.C. Chua, IEEE Trans. on

    Vehicular Techn., Vol. 49, No. 2, pp. 494-505, March 2000.

  • “MultiMedia One-Step PREDiction (MMOSPRED)” ,

    B.M. Epstein and M. Schwartz , IEEE JSAC-18, March, 2000.

  • “Admission Limit Curve (ALC)” , J. Siwko, I. Rubin,

    IEEE Trans. on Net., Vol. 9, June 2001.

  • “Dynamic Channel Pre-reservation Scheme (DCPr)” , X. Luo, I. Thng,

    and W. Zhuang, Proc. IEEE Int. Symp. Computers Commun., July 1999.

4th MCM Wuerzburg, Germany


Prediction models so me referenced models

Prediction Models:some referenced models

Measurement-based models:

  • “Measurement-Based Admission Control (MBAC)” ,M. Grossglauser,

    D.N.C. Tse, IEEE Trans. on Net., Vol. 7, June 1999.

  • “Hierarchical Location Prediction (HLP)” ,

    T. Liu, P. Bahl, I. Chlamtac, IEEE JSAC-16, August 1998.

  • “Wiener & ARMA models)” , T. Zhang, E. van den Berg,

    J. Chenninkara, P. Agrawal, J.C. Chen and T. Kodama,

    IEEE JSAC-19, Oct. 2001.

  • “Region-Based Call Admission Control)” , J-H. Lee, S-H. Kim,

    A-S.Park, J-K. Lee, IEICE Trans. on Com., Vol. E84-B, Nov. 2001.

4th MCM Wuerzburg, Germany


Prediction models s ome referenced models1

Prediction Models:some referenced models

Expert-based models:

  • “Measurement-Based Admission Control (MBAC)” ,M. Grossglauser,

    D.N.C. Tse, IEEE Trans. on Net., Vol. 7, June 1999.

4th MCM Wuerzburg, Germany


Prediction models our approach

Prediction Models:Our approach

Measurement-based ARIMA (univariate/multivariate) model

  • “N.Rožić, G. Kandus: "MIMO ARIMA models for handoff resource

    reservation in multimedia wireless networks",

    Wireless Communications and Mobile Computing (WCMC),

    Vol. 4, No.5, August 2004, pp. 497-512, John Wiley&Sons,

  • “N.Rožić, D.Begušić, G.Kandus: “Application of ARIMA Models for

    Handoff Control in Multimedia IP Networks”, Proceedings of

    the International Symposium on Intelligent Signal Processing and

    Communication Systems (ISPACS'03), pp. 787-791, Awaji Island,

    Japan, December 7-10, 2003.

4th MCM Wuerzburg, Germany


Arima models

ARIMA models

  • Univariate Autoregressive Integrated Moving Average (ARIMA)

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

,

one-step ahead conditional expectation:

with a variance

where

4th MCM Wuerzburg, Germany


Arima models1

ARIMA models

  • Multi-Input Multi-OutputARIMA (MIMO-ARIMA)

MIMO ARIMA (p,d,q)

stationary output and input vectors

ntis i.i.d. with< nt>=0 and covariance matrix

Polynomial matrices A, B, C should satisfy certain conditions when applied to

prediction or control problems: we choose

with a covariance

4th MCM Wuerzburg, Germany


Resource management and cac

Resource Management and CAC

The Call Admission Control (CAC)

new arrival rate

handoff call arrival rate

call release rate

call termination rate

demanded number of calls

a number of accepted calls

actual number of used channelsCt

a number of released calls

equilibrium equation

4th MCM Wuerzburg, Germany


Resource management and cac1

Resource Management and CAC

The Call Admission Control (CAC) – cont.

t-the reservation 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

interval:

(with handoff calls normally distributed)

seconds

Example:

,

If the prediction interval is

the CAC algorithm has to start not later than

steps before the handoff call burst starts.

(3 steps)

Let

4th MCM Wuerzburg, Germany


Resource management and cac2

Resource Management and CAC

Example:

The total number of channels,

new accepted channels,

handoff channels and the

time precedence for the case

of burst-like handoff traffic

4th MCM Wuerzburg, Germany


Simulation scenarios

Simulation Scenarios

  • Let consider three typical traffic scenarios:

    • (i) “stationary” process,

    • (ii) nonstationary seasonal process,

    • (iii) nonstationary burst-like process,

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

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

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

intervention model

  • average call holding time Tcall =200 s,

  • call’s average channel holding time in each cell Tchannel =100 s,

  • average new call arrival rate Nis considered in the range 0 to 0.45 calls per second

  • total cell capacity is N=30 channels

  • the target call droping probability (TCDP) is 0.05

4th MCM Wuerzburg, Germany


Simulation results scenario i

Simulation results:Scenario (i)

Scenario (i)

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

Handoff call dropping probability: comparison for scenario (i)

Actual and predicted total number of

channels at N=0.27 and h=0.004

4th MCM Wuerzburg, Germany


Simulation results scenario ii

Simulation results:Scenario (ii)

Scenario (ii)

ARIMA(p,1,0)x(1,1,0)S;S=60 minutes

Actual and predicted total number of

channels at N=0.27 and seasonal handoff

Handoff call dropping probability: comparison for scenario (ii)

4th MCM Wuerzburg, Germany


Simulation results scenario iii

Simulation results:Scenario (iii)

Scenario iii)

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

; n – intervention variable

Actual and predicted total number of

channels at N=0.27 and handoff burst

Handoff call dropping probability: comparison for scenario (iii)

4th MCM Wuerzburg, Germany


Emergency services resource management and qos control2

Emergency Services: Resource Management and QoS Control

Concluding Remarks and Future Work

Forecasts integration

4th MCM Wuerzburg, Germany


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