Emergency Services: Resource Management
and QoS Control
Nikola Rozic, Dinko Begusic
University of Split, Croatia
Gorazd Kandus
Institute Jozef Stefan, Slovenia
13-14 Oct. 2005
4th MCM Wuerzburg, Germany
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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
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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)
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Emergency Services and QoS
 QoS in emergency situations ?
Global aspects:
1.
Reliable signal an emergency situation
2. Priority services among networks
3. 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
2. Reliable mobility and localisation services
3. Reliable resource management and call access control
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Emergency Services and QoS
Traffic models
Normal load condition:
 the objective is to keep probability of being unsatisfied (punsatisfied) as
low as possible by prioritizing the ongoing calls
Punsatisfied  Pblock  Pdrop  (1  Pblock )
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.
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Emergency Services and QoS
Traffic 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 emergency
call 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
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Emergency Services and QoS
Problem statement
QoS for roaming calls
the handoff failure
QoS criterion
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fast handoff between base stations/
access points
dropping of the connection
dropping of the connection after admission
should be considered much less acceptable
than blocking the new connection !
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Resource Management and QoS Control
The approach
an effective way to reduce
the handoff call dropping
probability (CDP)
efficient advanced
resource reservation
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advanced resource reservation
for future handoffs
good prediction methods for
future new and handoff call
arrivals
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Prediction Models
Good predictions ?
Predictable situations:
People speaking about predictable and
unpredictable 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, ...)
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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
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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
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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,
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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.
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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.
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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.
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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.
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ARIMA models
 Univariate Autoregressive Integrated Moving Average (ARIMA)
a( L ) A( LS ) d  SD Yt  b( L )B( LS ) t
ARIMA (p,d,q)x(P,D,Q)S
 t  N ( 0 , 2 ) ,  d  (1  L) d ;  SD  (1  LS ) D
p
a( L )   ai L ;
i 1
q
i
P
A( L )   ai LiS ;
s
i 1
Q
b( L )   bi Li ; B( LS )   Bi LiS ; with Li y t  y t i
i 1
i 1
yt  a( L )1 A( LS )1 b( L )B( LS ) t
one-step ahead conditional expectation:
where y t   d  SD Yt
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with a variance

2
y

2
y

 i2 
i  0 


1
;  ( L )  b( L ) 1 B( LS ) / a( L ) A( LS )
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ARIMA models
 Multi-Input Multi-Output ARIMA (MIMO-ARIMA)
A(L) y t  B(L) x t  C(L) n t
MIMO ARIMA (p,d,q)
y t   d  SD Yt , x t   d  SD X t
stationary output and input vectors
nt is 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
A(L)  I  A 1 L  A 2 L2      A p L p
B(L)  B 0  B 1 L  B 2 L2      B r Lr
with a covariance
C(L)  I  C1 L  C 2 L2      Cq Lq
 ij  
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ji
2
2
  11
 12
 2
2

 22
Σ   21
 

 2
2
 m1  m
2
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  12m 

  22m 
  

2
  mm

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Resource Management and CAC
The Call Admission Control (CAC)
y(t)
h, in
new arrival rate  N
-
handoff call arrival rate h ,in
call release rate  rel   term   h ,out
a number of accepted calls y n ( t )
actual number of used channels Ct
+
t
+
yn (t)
X
N
call termination rate term  n,term  h,term
demanded number of calls y N ( t )
+
CAC

(t)
h, out
+

N(t)
h,

N

term
out
X
-
+
y n ( t )  y h,in ( t )  yrel ( t )  Ct ( t )
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CDP=
+

n
CBP=Pr{
n
a number of released calls yrel ( t )  yn,term ( t )  yh,term ( t )  yCAC
h,out ( t )
equilibrium
equation
t
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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
N̂ h
interval:
(with handoff calls normally distributed)
t  

ˆ term
Example: ˆ term  0.15 calls/sec, N h  25
t    166.7 seconds
If the prediction interval is t the CAC algorithm has to start not later than
t

k c  round    0.5 
 t

Let t  1 min
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steps before the handoff call burst starts.
 160

k c  round 
 0.5   3  t
 60

(3 steps)
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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
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Simulation Scenarios
Let consider three typical traffic scenarios:
(i) “stationary” process,
ARIMA(p,1,0)x(0,0,0)
(ii) nonstationary seasonal process,
ARIMA(p,1,0)x(1,1,0)S
(iii) nonstationary burst-like process,
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 N is 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
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Simulation results: Scenario (i)
Scenario (i) ARIMA(p,1,0)x(0,0,0)
Actual and predicted total number of
channels at N=0.27 and h=0.004
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Handoff call dropping probability:
comparison for scenario (i)
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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
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Handoff call dropping probability:
comparison for scenario (ii)
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Simulation results: Scenario (iii)
Scenario iii)
ARIMA(p,1,0)x(0,0,0) + intervention model ;
 ( L ) yt  ( L ) t b ; n – intervention variable
Actual and predicted total number of
channels at N=0.27 and handoff burst
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Handoff call dropping probability:
comparison for scenario (iii)
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Emergency Services: Resource Management
and QoS Control
Concluding Remarks and Future Work
Forecasts integration
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