Analysis of Adaptive Bandwidth Allocation in Wireless Networks with Multilevel Degradable Quality of...

Analysis of Adaptive Bandwidth Allocationin Wireless Networks with Multilevel

Degradable Quality of Service

Chun-Ting Chou and Kang G. Shin

CS 5214 Modeling and Evaluation of Computer Systems

Spring 2007Presented by Xueqin (Jennifer) Wang

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Presentation Outline• Introduction

• Study Assumptions

• Analytical Model

• Numerical Results

• Simulation Results

• Conclusions and Future Work

• References

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INTRODUCTION

Different applications can use different encoding schemes and generate traffic with different bandwidth requirements.

• generic video telephony 40 Kbps

• low-motion video telephony 25 Kbps

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INTRODUCTION (CONT.)

“In a mobile computing environment, scarcewireless bandwidth, asymmetric communication,limited battery power and mobility of the mobileclients are the major challenges to the design of a mobile system”

T. Imielinski and B. R. Badrinath. Mobile wireless computing : Challenges in data management. Communication of ACM, 37(10), 1994.

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OBJECTIVE

Adaptive Bandwidth Allocation:

• Maximize the bandwidth utilization

• Satisfying QoS constraints

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RELATED WORK

Sen et al.(1998)an optimal degradation strategy maximize a revenue function

Sherif et al.(2000) an adaptive resource allocation algorithm

maximize bandwidth utilization and attempted to achievefairness with a generic algorithm

S. Singh et al.(1996) a graceful degradation mechanism

adaptively adjusting bandwidth allocation according to user-specified loss profiles

Kwon et al.(1998) a degradation period ratio the time a user receives degraded quality, assuming meandegradation time and degradation states are independent

S. Choi (1998) and K.G. Shin(2001)

frequent switching of QoSlevel degrade the system performance 

Lin et al. (1994) Guard Channel system a portion of bandwidth is reserved for handoff users

R. Ramjee et al.(1996) and K.Mitchell et al. (2001) handoff users priority  

S. Choi et al. (1998) and A.Aljadhai et al (2000) predictive or adaptive bandwidth allocation algorithms

Z. Liu et al (1996) applied multicode CDMA for service degrade/upgrade

J.C. Haartsen (2000) applied an adequate assignment of time slots on TDMA to achieve service degrade andupgrade

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WHAT WE HAVE IN THIS PAPER?

Adaptive Bandwidth Allocation Algorithms

with Multilevel Degradable QoS focus on :

(1) the impact of quality degradation on individual users

(2) trade offs between system performance and user-perceived QoS

(3) consider various mobility patterns

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NOTATIONS

Handoff between the MS and BSs

QING-AN ZENG et al “Handoff in Wireless Mobile Networks”

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NOTATIONS(CONT.)

QING-AN ZENG et al “Handoff in Wireless Mobile Networks”

Giving only the system state,

the connection state:

where i is the quality index which represents the current QoS level of an admitted user.

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QoS METRICS

The forced-termination (or dropping) probability Pf:The probability to terminate an admitted user’s session beforeits completion.

The blocking probability Pb:The probability to reject the new user’s request.

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QoS METRICS (CONTD.)Degradation ratio (DR): The fraction of time a userreceives degraded QoS. It is defined as:

if a user receives level-i QoS for Ti seconds.

Upgrade/degrade frequency (UDF) : The frequency of changing the QoS level an admitted user receives.

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OTHER NOTATIONS

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STUDY ASSUMPTIONS(1) Arrivals of the new users are Poisson process; (2) Lifetime of each connection and Cell-sojourn

time are identical, independent and they are exponential distributed;

(3) Homogeneous cells.

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ANALYSIS

A connection with n handoffs

Where Tr is the remaining cell-sojourn time in the cell where a user’s connection is initiated, Ti is the cell-sojourn time in the ith cell, and Tc is the connection lifetime

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TRAFFIC MODELS

Where H is handoffs times during connection lifetime

the probability that a mobile user will experience handoffs H times can be defined as

P(H=n) = P( (Tr+T1+T2+…+Tn-1)< Tc <(Tr+T1+T2+…+Tn))

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TRAFFIC MODELS (CONT.)

where pf is the probability of terminating handoff users and pb the probability of blocking new users, is the handoff rate; is new user arrival rate; is the connection lifetime rate and is mean cell sojourn rate.

The handoff rate:

(Lin et al.)

0

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TRAFFIC MODELS (CONT.)

The distribution of channel-holding time is:

Where is the average connection lifetime and is the average cell sojourn time

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ANALYTICAL MODELSAssume that there are K different QoS levels system state, n,

as

where ni is the number of users in the ith QoS level in a cell.

Two strategies can be applied during shortage of bandwidth :

(1) allocate only Wmin to an incoming user and minimize the need to degrade the QoS levels of existing users to achieve DR and UDF

(2) degrade the QoS of one or few existing users to accommodate a new user and minimize the change of

current bandwidth allocation to achieve fairness.

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ANALYTICAL MODELS (CONT.)

A pseudocode of the bandwidth degradation algorithm

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ANALYTICAL MODELS (CONT.)

A pseudocode of the bandwidth upgrade algorithm.

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STATIONARY DISTRIBUTIONThe Transition Probability:

(1) If a user arrives at the cell before the departure of any existing user;

(2) If a level-i user leaves the cell

replacing with

The stationary state distribution, can be obtained by solving the equation

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STATIONARY DISTRIBUTION (CONT.)

State transitions of the number of users in one cell

Assume a system with K = 2, W1 = 2, and W2 = 1, new users and handoff users are the same (Nthresh = C)

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STATIONARY DISTRIBUTION (CONT.)

The stationary distribution of the number of users in a cell

Erlang-B formula can be applied to calculate the state probability:

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QoS METRICS

Transitions of connection states: K = 4

consider a system with K = 4, Wi = 5 - i for i = 1 to K,and C = 20.

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DEGRADATION RATIO

The number of visits to state cj before enteringthe completion state A

The initial state DR

DR

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UPGRADE/DEGRADE FREQUENCY

Transitions between different QoS levels

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UPGRADE/DEGRADE FREQUENCY(CONT.)

The average number of QoS-level switches before a connection is completed or handed off is,

given an initial state

UDF can be derived as:

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EXAMPLE

Transitions of a admitted user r1’s connection states in a cell.

Assume K =2, W1 = 2, and W2 =1, (level 1 is full quality, level 2 is degraded quality), consider three different events: the arrival of a new user, the departure of r1, or the departure of any other existing users

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EXAMPLE (CONT.)For a handoff user:

For a new user:

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NUMERICAL RESULTS(1)K =2: WFull = 2 W Degraded

Pb and Pf versus arrival rate: no quality degradation

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NUMERICAL RESULTS (CONT.)

Pb and Pf versus arrival rate with quality degradation.

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NUMERICAL RESULTS(CONT.)

DR and UDF versus arrival rate.

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NUMERICAL RESULTS (CONT.)

DR and UDF versus connection lifetime.

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NUMERICAL RESULTS (CONT.)

DR and UDF versus mobility.

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NUMERICAL RESULTS (CONT.)(2) K = 3; C=24 with W1=4, W2=3, W3=2; Fairness versus UDF

STATE-TRANSITION DIAGRAM

(a) Complete-fair algorithm (6,0,0) -> (3,4,0)(b) UDF-minimizing algorithm (“unfair” algorithm) (6,0,0) -> (5,0,2)

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NUMERICAL RESULTS (CONT.)

Fairness versus UDF.

“COM-1” unfair degradation and fair upgrade,“COM-2” fair degradation and unfair upgrade

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NUMERICAL RESULTS (CONT.)

Bandwidth reallocation algorithm: Com-2

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MECHANISMS

Adaptive Restriction Threshold: •The trade off between the blocking probability of new users and the other QoS metrics; •No Optimal value of restriction threshold;

Bandwidth Reallocation Algorithm: •Benefit other QoS metrics without degrading users’ DR•Adjust accordingly per user’s mobility

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SIMULATION

The cellular network used in the simulation

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SIMULATION (CONT.)

The flow-chart of event-driven simulator

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SIMULATION RESULT

DR and UDF under different mobility models

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Conclusions and Future Work• User-perceived QoS metrics (degradation ratio and

upgrade/degrade frequency) are defined and formulas for computation of these metrics are derived from Markov model.

• An analytical model of wireless network QoS model is derived and simulation is performed to validate formulas.

• Model is useful for adaptive QoS management balancing user service admission blocking, failure, degradation ratio and upgrade/degrade frequency.

• Examples of multi-dimensional QoS management model applied in mobile simulation.

• Paper fails to compare with other similar QoS models (Quasar work for one) as it appears to survey only literature in mobile computing.

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References• [1] S. Sen, J. Jawanda, K. Basu, and S. Das, “Quality-of Service Degradation Strategies in Multimedia Wireless Network,” Proc. IEEE Vehicular Techonology Conf., vol. 3, pp. 1884-1888, May 1998.• [2] S. Singh, “Quality of Service Guarantees in Mobile Computing,” Computer Comm., no. 19, pp. 359-371, 1996.• [3] M.R. Sherif, I.W. Habib, M.N. Nagshineh, and P.K. Kermani, “Adaptive Allocation of Resources and Call Admission Control for Wireless ATM Using Generic Algorithm,” IEEE J. Selected Areas in Comm., vol. 18, no. 2, pp. 268-282, Feb. 2000.• [4] T. Kwon, Y. Choi, C. Bisdikian, and M. Naghshineh, “Call Admission Control for Adaptive Multimedia in Wireless/Mobile Network,” Proc. First ACM Int’l Workshop Wireless Mobile Multimedia, pp. 111-116, Oct. 1998.• [5] S. Choi and K.G. Shin, “Location/Mobility-Dependent Bandwidth Adaptation in QoS-Sensitive Cellular Networks,” Proc. IEEE Vehicular Technology Conf., vol. 3, pp. 1593-1597, 2001.• [6] Y.B Lin, S. Mohan, and A. Noerpel, “Queueing Priority Channel Assignment Strategy for PCS Handoff and Initial Access,” IEEE Trans. Vehicular Technology, vol. 43, no. 3, pp. 704-712, Aug. 1994.• [7] R. Ramjee, R. Nagarajan, and D. Towsley, “On Optimal Call Admission Control in Cellular Networks,” Proc. IEEE INFOCOM ’96, vol. 1, pp. 43-50, 1996.

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References• [8] K. Mitchell and K. Sohraby, “An Analysis of the Effects of Mobility on Bandwidth Allocation Strategies in Multi-Class Cellular Wireless Networks,” Proc. IEEE INFOCOM ’01, vol. 2, pp. 1075-1084, 2001.• [9] A. Sutoving and J.M. Peha, “Novel Heuristic for Call Admission Control in Cellular Systems,” Proc. IEEE Int’l Conf. Universal Personal Comm., vol. 1, pp. 129-133, 1997.• [10] M. Naghshineh and M. Schwartz, “Distributed Call Admission Control in Mobile/Wireless Networks,” IEEE J. Selected Areas in Comm., vol. 14, no. 3, pp. 289-293, May 1994• [11] S. Choi and K.G. Shin, “Predictive and Adaptive Reservation for Handoffs in QoS-Sensitive Cellular Networks,” Proc. ACM SIGCOMM ’98, pp. 155-166, 1998.• [12] A. Aljadhai and T. Znati, “A Framework for Call Admission Control and QoS Support in Wireless Environments,” Proc. IEEE INFOCOM ’99, vol. 3, pp. 1019-1026, 1999.• [13] Z. Liu, M.J. Karol, M.E. Zarki, and K.Y. Eng, “Channel Access and Interference Issues in Multi-Code DS-CDMA Wireless Packet (ATM) Networks,” Wireless Networks, vol. 2, no. 3, pp. 173-193, 1996.• [14] J.C. Haartsen, “The Bluetooth Radio System,” IEEE Personal Comm., vol. 7, no. 1, pp. 28-36, Feb. 2000.

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References• [16] V. Paxson and S. Floyd, “Wide Area Traffic: The Failure of Poisson Modeling,” IEEE Trans. Networking, vol. 3, no. 3, pp. 226-244, 1995.• [17] S.N. Subramanian and T. Le-Ngoc, “Traffic Modeling in a Multi- Media Environment,” Proc. IEEE CCECE/CCGEI ’95, pp. 838-841, 1995.• [18] P. Bremaud, Markov Chains: Gibbs Fields, Monte Carlo Simulation and Queues. New York: Springer, 1999.