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Emergency Services: Resource Management and QoS Control

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

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  1. Emergency Services: Resource Management and QoS Control Nikola Rozic, Dinko Begusic University ofSplit, Croatia Gorazd Kandus Institute Jozef Stefan, Slovenia 4th MCM Wuerzburg, Germany

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. Emergency Services: Resource Management and QoS Control Concluding Remarks and Future Work Forecasts integration 4th MCM Wuerzburg, Germany

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