7-IJAEST-Vol-No.4-Issue-No.2-Comparison-of-two-Access-Mechanisms-for-Multimedia-Flow-in-High-Speed-D by ISERP ISERP

Abdelali EL BOUCHTI, et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 2, 029 - 035

Comparison of two Access Mechanisms for Multimedia Flow in High Speed Downlink Packet Access Channel Abdelali EL BOUCHTI

Abdelkrim HAQIQ

Computer, Networks, Mobility and Modeling laboratory e-NGN research group, Africa and Middle East

FST, Hassan 1st University, Settat, Morocco Emails: {a.elbouchti, ahaqiq}@gmail.com

distribute the time resources between the users. However HSDPA link supports multimedia services, which require differentiated QoS. Thus, besides user priority, the algorithm also has to determine priority among classes supported for a given user. The works in [8, 17] were interested to integrate a buffer per user in node B to take into account intra-user traffic differentiation. Modeling of multimedia traffic over shared is rather complicated. Therefore, most of studies typically investigate the problem using packet-level simulation [2] or as data flows which can be real time (voice or video) or non real time (www browsing, e-mail, ftp, or data access). Other works considered performance study of the HSDPA system taking into account system details rather than the multimedia traffic characteristics [16]. In this paper, we present two access control mechanisms of multimedia flow in HSDPA channel using queue threshold schemes. The RT and NRT packets compose the flow. The thresholds are used in order to manage access packets in the queue giving priority to the RT packets and avoiding the NRT packet loss. The RT packets arrive according to a Poisson process and the NRT packets arrive according to an MMPP process with two phases. In the first mechanism, named without control, both types of packets are not controlled. But in the second mechanism, named with control, the NRT packets are controlled. Our objective is to compare the performance parameters of these mechanisms. The same model of the second mechanism is studied in [11] and enhanced in [10], where the authors modeled the arrival flows by three Poisson processes and they added another threshold. They proved that, the arrival and service rates of the NRT packets donâ&#x20AC;&#x2122;t have an effect on the performance parameters related to RT packets, whereas those of RT packets influence on all the performance parameters of the system. The authors in [4] and [5] studied a similar model to the second mechanism, they modeled the arrival flow of NRT packets by an MMPP process, because it is capable of capturing inter-flow correlation, and they determined and evaluated the performance parameters. To improve the performance parameters related to the RT packets, recently the authors in [9] proposed an enhanced

Abstractâ&#x20AC;&#x201D; The High Speed Downlink Packet Access (HSDPA) is used more in the radio operator interface of the access network of the Universal Mobile Telecommunication System (UMTS). Its principle is to share a common channel (buffer) with high throughput between the users of the cell in order to improve the Quality of Service (QoS). In this paper, we compare the performance parameters of two access control mechanisms for multimedia flow in HSDPA channel using queue threshold schemes. Real Time (RT) packets (voice and/or video) and NonReal Time (NRT) packets (data) compose the flow. In the first mechanism, priority in the queue is given to real time packets and a threshold is used to limit the access of these packets in the queue. Another threshold is added in the second mechanism in order to manage NRT packet access in the queue. The RT packets arrive according to a Poisson process and the NRT packets arrive according to a Markov Modulated Poisson Process (MMPP) with two phases. For both mechanisms, different performance parameters (loss probability, average delay of packets, and average number of packets in the queue) have been derived analytically; numerical results are calculated and compared for the two schemes.

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Keywords- HSDPA, Multimedia Flow, Queueing Theory, QoS, MMPP, Performance Parameters.

INTRODUCTION

While third Generation (3G) wireless systems are being intensively deployed worldwide, new proposals for enhanced data rates and quality of service (QoS) provision are being standardized. One of the most promising enhancements to the widely deployed Universal Mobile Telecommunication System (UMTS) which is based on Wideband Code Division Multiple Access (W-CDMA) is called High Speed Downlink Packet Access (HSDPA) [1] which is also referred to as 3.5G. HSDPA aims at providing data rates around 2-10Mbps on the downlink to mobile users mainly for multimedia services such as real-time and streaming video in packet-switched common channel. In the HSDPA, the link adaptation and packet scheduling functionalities are executed directly from the Node B, this allows advanced packet scheduling techniques. The target of majority of packet scheduling algorithms proposed and studied in the literature [3, 6, 12, 15] is to

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scheme. They considered a system as the first one but with the following modifications: - The arrival processes of RT and NRT packets are assumed to be poissonian. - Even if the number of RT packets exceeds the threshold R, an RT packet will enter in the system if it isn’t full. - When an NRT packet arrives and the system is full, if there is more than R packets of type RT in the system, then an RT packet will be rejected and the arriving NRT packet will enter in the system, otherwise the NRT packet will be rejected.

N) and all arrived RT packets after this threshold will be lost. In the queue, the server changes according to the type of packet that it treats, a server is reserved for the RT packets and another for the NRT packets, these two servers operate independently. Furthermore, the service times of RT and NRT packets are assumed to be independent and exponential with rates  and respectively. In the first mechanism (Figure 1), named without control, the NRT packets arrive according to an MMPP processwith two phases and their number in the queue cannot exceed N. Then, when an NRT packet arrives and finds N packets in the queue, it will be lost.

II.

The rest of this paper is organized as follows: section 2 gives an idea about the MMPP processes and describes mathematically the two mechanisms. The performance parameters of these mechanisms are given in section 3. In section 4 we present and discuss the numerical results, and section 5 gives a conclusion and a perspective of this paper. MMPP PROCESSES AND MATHEMATICAL DESCRIPTION

Figure 1: System without control

In the second mechanism (Figure 2), named with control, we add another threshold H (R<H such that H=N-R) in the queue in order to control the arrival of the NRT packets. Then, the NRT packets enter in the system if the their number in the queue is lower than H. This second mechanism enables to prevent either the congestion in the system or the loss of the NRT packets.

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A. The Markov Modulated Poisson Processes Markov Modulated Poisson Processes (MMPP) is a term introduced by Neuts [14] for a special class of versatile point processes whose Poisson arrivals are modulated by a Markov process. The model is a doubly stochastic Poisson process [7], whose rate varies according to a Markov process, it can be used to model time-varying arrival rates and important correlations between inter-arrival times. Despite these abilities, the MMPPs are still tractable by analytical methods. The current arrival rate  i , , of an MMPP is defined by the current state i of an underlying continuous time Markov chain (CTMC) with m states. The counting process of an MMPP is given by the bivariate process , where is the number of arrivals within a certain time interval [0,t), t T (T  IR ) and is the state of the underlying CTMC. , being the If we consider QMMPP  qij  ,

infinitesimal generator matrix of the underlying CTMC, then the rates of the transitions between the states of the CTMC are given by the non-diagonal elements of QMMPP . With the

Poisson arrival rate at each state 1 , 2 ,...., m , we define a diagonal matrix  as:

  diag (1 , 2 ,...., m )

B. Mathematical Description For the two mechanisms studied in this paper, we model the HSDPA link by a single server-queue of a finite capacity N, N>0. The input flow in the queue is heterogeneous and composed by the RT and NRT packets. The RT packets arrive according to a Poisson process with parameter  , their number in the queue cannot exceed a given threshold R (R <

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Figure 2: System with control Remark: For both systems, the RT packets are placed all the time in front of the NRT packets. For the first and second mechanisms, the states of the system are respectively described by the processes and , where and are the phases of two MMPP processes and (respectively ) are respectively the numbers of RT (respectively NRT) packets in both systems at time t. The state spaces of are respectively given by: and

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STATIONARY PROBABILITIES AND PERFORMANCE PARAMETERS

B. Performance Parameters In this section, we determine analytically, for both mechanisms, different performance parameters (loss probabilities, average delays of packets and average numbers of RT and NRT packets in the buffer) at the steady state. These performance parameters can be derived from the stationary state probabilities. 

System without control

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A packet of type RT is lost when there are R packets of type RT in the queue. Then the loss probability of RT packets can be obtained as follows: 2 N R

1 PlossRT    p1 (i, R, k )

(1)

i 1 k  0

A packet of type NRT is lost when there are N packets in the queue such that the number of packets of type RT in the queue doesn’t exceed R. Then, the loss probability of NRT packets is given by: 1 lossNRT

   p1 (i, j , N  j ) i 1 j  0 i 2

R 1

(2)

Then the average numbers of RT and NRT packets are respectively given by:

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(3)

i 1 j  0 k  0

1 NRT

1 min( R , N  k ) N



  k . p (i, j, k )

i 1

j 0

(4)

k 0

By using the Little’s formula [13] (The average packet delay equals the average number of packets in the system divided by the arrival rate for packets eventually served), we determine respectively the average delays of RT and NRT packets in the system by (5) and (6) where the denominators of these expressions represent respectively the effective arrival rates of RT and NRT packets in the system. 1 RT

1 N RT  1  (1  PlossRT )

D1NRT  

(5)

1  N 1NRT N RT

  (1  p

1 lossNRT

i 1

(6)

)

System with control

A. Stationary Probabilities Since for both systems, the arrival processes are poissonian (i.e. the inert-arrivals are exponential), the service times are exponential and these processes are all mutually independent between them, then are the Markov processes (because the exponential distribution is without memory). We can prove easily that are irreductible because all their states communicate between them. Moreover, and are the finite spaces, then are positive recurrent. Consequently, the processes are ergodic (i.e. both systems are stable) and their stationary probabilities exist. We denote the stationary probability of (respectively ) by (respectively ). and can be computed by solving the systems of the global balance equations (the average flow outgoing of each state is equal to the average flow go into that state) and the normalization equations (the sum of all state probabilities equals to 1) given in the appendix of this paper. In these systems, denotes the transition rate from phase to phase , where .

R N j

1 N RT    j. p1 (i, j , k )

III.

For the second mechanism, a packet of type RT is lost when there is R packets of type RT in the queue and the NRT packets enter in the system if their number in the queue is lower than H. Then the loss probabilities of RT and NRT packets are respectively given by: H

2 PlossRT   p2 (i, R, k )

(7)

2 PlossNRT 0

(8)

i 1 k  0

Then the average number of RT and NRT packets are respectively given by: R

2 N RT   j. p2 (i, j , k )

(9)

i 1 j  0 k  0 2

2 N NRT   k . p2 (i, j , k )

(10)

i 1 j  0 k  0

By using the Little’s formula, we determine respectively the average delays of RT and NRT packets in the system by (11) and (12) where the denominators of these expressions represent respectively the effective arrival rates of RT and NRT packets in the system.

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2 DNRT 

2 N RT 2  (1  PlossRT )

(11)

2 2 N RT  N NRT

R H  j 1

  i 1 j  0

k 0

IV.

(12)

i p2 (i, j, k )

NUMERICAL RESULTS

Figure 4: Variations of the average numbers of NRT packets according to the service rate of RT packets.

For

when the service rate of NRT packets increases, we remark (Figures 5 and 6) the same behaviour of the average delay and the average number of NRT packets as in the previous case. is lower the second mechanism is Moreover, when clearly more effective.

In [4, 5], the authors have just determined and evaluated the performance parameters for the second mechanism. Here we compare the performance parameters between both mechanisms. We remark that both mechanisms, with control and without control, present similar performances for the RT packets. Whereas, the performances for the NRT packets vary from a mechanism to the other. Furthermore, for the second mechanism, the loss probability of the NRT packets is equal to 0 because we stop to send them to the system when there are H packets of type NRT in the queue. To see the difference between the performance parameters of the NRT packets for both mechanisms, we study some simulations below.

2 DRT 

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For N  60, H  45, R  15,   20, 1  8, 2  5, q12  3, q21  2, 1  20, we remark that (Figures 3 and 4), when the service rate of RT packets increases: 1- the average delays and the average numbers of NRT packets decrease for both mechanisms, 2- the values of the average delay and average number of NRT packets for the second mechanism are lower or equal to those related to the first mechanism. Moreover, when is lower the second mechanism is clearly more effective.

Figure 5: Variations of the average delays of NRT packets according to the service rate of NRT packets.

Figure 3: Variations of the average delays of NRT packets according to the service rate of RT packets.

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Figure 6: Variations of the average numbers of NRT packets according to the service rate of NRT packets.

Figure 8: Variations of the average numbers of NRT packets according to the arrival rate of RT packets.

q21  2, 1  20, we remark that (Figures 9 and 10), when the arrival rate ( 1 or 2 ) of NRT packets increases: 3- the average delays and the average numbers of NRT packets increase for both mechanisms, 4- the values of the average delay and average number of NRT packets for the second mechanism are lower or equal to those related to the first mechanism. Moreover, the second mechanism becomes more effective when the arrival rate of NRT packets ( 1 or 2 ) is higher.

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For N  60, H  45, R  15,   30, 1  8, 2  5, , we remark that (Figures 7 and 8), when the arrival rate of RT packets increases: 1- the average delays and the average numbers of NRT packets increase for both mechanisms, 2- the values of the average delay and average number of NRT packets for the second mechanism are lower or equal to those related to the first mechanism. Moreover, when is higher the second mechanism enhances these parameters.

For N  60, H  45, R  15,   20,   30, q12  3,

Figure 7: Variations of the average delays of NRT packets according to the arrival rate of RT packets.

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Figure 9: Variations of the average delays of NRT packets according to the arrival rate of NRT packets.

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REFERENCES

[2] [3] [4] [5]

Figure 10: Variations of the average numbers of NRT packets according to the arrival rate of NRT packets.

[6] [7]

CONCLUSION AND PERSPECTIVE [8]

[9]

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In this paper, we have studied and compared two queueing systems, which represent two access mechanisms for multimedia flow in HSDPA channel using queue threshold schemes. RT and NRT packets compose this flow. In the first mechanism (named without control), priority in the buffer is given to the RT packets and a threshold is used to limit the access of these packets. Another threshold is added in the second mechanism (named with control) in order to manage the NRT packets access in the buffer. We have modeled these systems by two different Markov processes and we have proved that these processes are stable and their stationary probabilities exist. Each stationary probability is given by the global balance equations of its process. We have also determined analytically different parameters of both systems. After, we have evaluated numerically these parameters and we have remarked that, for both systems, the performance parameters related to the RT packets are similar, while those related to the NRT packets are in general different but they vary in the same direction (either they are increasing or decreasing) according to the common parameters of both systems. We have also varied the average delays and the average numbers of NRT packets in both systems, according to the common parameters, and we have remarked that the performance parameter values for the system with control are less or equal to those for the system without control. We concluded that the system with control is more efficient for the NRT packets in comparison with the system without control because it improves the performance parameters of these packets. To give more interest to this research work, we could propose a dynamic access control mechanism for the NRT packets in the channel. This mechanism should determine dynamically the threshold H instead of to fix it at the beginning.

3GPP TS 25.848 “Physical Layer aspects of UTRA High Speed Downlink Packet Access”, v4.0.0, 2001. T. Bonald and A. Proutiere, “Wireless downlink data channels: user performance and cell dimensioning”, Proceeding of Mobicom03, San Diego, USA, 2003. Y. Cao and V.O.K. Li “Scheduling algorithms in broadband wireless networks”, Proceeding of the IEEE, vol. 89, no. 1,pp. 76-87, Jan. 2001. A. El bouchti and A. Haqiq “The performance evaluation of an access control of heterogeneous flows in a channel HSDPA”, proceedings of CIRO’10, Marrakesh, Morocco, 24-27 May 2010. A. El bouchti , A. Haqiq, M. Hanini and M. Elkamili “Access Control and Modeling of Heterogeneous Flow in 3.5G Mobile Network by using MMPP and Poisson processes”, proceedings of MICS’10, Rabat, Morocco, 2-4 November 2010. H. Fattah and C. Leung, “An overview of scheduling algorithms in wireless multimedia networks”, IEEE Wireless Communications, vol.9, no. 5, pp. 76-83, Oct. 2002. W. Fischer and K. Meier-Hellstem, “The Markov-modulated Poisson process (MMPP) cookbook, Performance evaluation”, Vol. 18, Issue 2, pp. 149-171, September, 1993. A. Golaup, O. Holland and A. H. Aghvani, “Concept and Optimization of an effective Packet Scheduling Algorithm for Multimedia Traffic over HSDPA”, Proceeding of the IEEE Personal, Indoor and Mobile Radio Comms, pp. 1693-1697, Berlin, Germany, 2005. M. Hanini, A. El Bouchti, A. Haqiq and A. Berqia, “An Enhanced Time Space Priority Scheme to Manage QoS for Multimedia Flows transmitted to an end user in HSDPA Network”, International Journal of Computer Science and Information Security, Vol. 9, No. 2, pp. 65-69, February 2011. M. Hanini, A. Haqiq and A. Berqia, “Comparison of two Queue management mechanisms for heterogeneous flow in a 3.5G network”, proceedings of NGNS’10, pp. 135-140, Marrakesh, Morocco, 8-10 July 2010. A. Haqiq, M. Hanini and A. Berqia, “ Contrôle d’accès des flux multimédia dans un canal HSDPA”, Actes de WNGN, pp. 135-140, Fès, Maroc, 2008. J. Ramiro-Moreno, K.I. Pedersen and P.E. Mogenson, “Network Performance of Transmit and Receive Antenna Diversity in HSDPA under Different Packet Scheduling”, Vehicular Technology Conference, VTC 2003 Spring, Vol. 2, pp. 1454-1458, 2003. R. Nelson, “Probability, stochastic process, and queueing theory”, Springer-Verlag, third printing, 2000. M.F. Neuts, “Matrix Geometric Solution in Stochastic Models – An algorithmic approach”, The Johns Hopkins University Press, Baltimore, 1981 J. Ohyun, S. Jong-Wuk and CHO Dong-Ho, “An Enhanced packet Scheduling Algorithm Combined with HARQ for HSDPA System”, IEEE communications letters, vol. 12, no 4, pp. 247-249, 2008. H. Van den Berg, R. Litjens and J. Laverman, “HSDPA Flow Level Performance: The Impact of key System and Traffic Aspect”, COST290 TD(05)007, Colmar, 2005. S. Yerima and K. Al-Begain, “Performance Modelling of a Queue Management Scheme with Rate Control for HSDPA”, The 8th Annual PostGraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting Liverpool John Moores University Liverpool, U.K. 28-29 June 2007.

[1]

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[10]

[11] [12]

[13] [14]

[15] [16] [17]

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For k=1,..., H-R-1, we have: (i  q,ii '   ) p2 (i, R, k )  qi 'i p2 (i ', R, k )   p2 (i, R 1, k )

APPENDIX For both systems, the global balance equations and the normalisation equations are given as follows:

i p2 (i, R, k  1).

A. System without Control

For k=H-R+1,…, H, we have: (   qii ' ) p2 (i, R, k )  qi ' i p2 (i ', R, k )   p2 (i, R  1, k ).

For i and i ' = 1, 2, we have: (i    qii ' ) p1 (i, R, 0)   p1 (i, R  1, 0)  qi 'i p1 (i ', R, 0). (qii '    i ) p1 (i, 0, 0)   p1 (i,1, 0)  qi 'i p1 (i ', 0, 0)  1 p1 (i, 0,1).

(  1  qii ' ) p1 (i, 0, N )  qi 'i p1 (i ', 0, N )  i p1 (i, 0, N  1). (  qii ' ) p1 (i, R, N  R)  qi 'i p1 (i ', R, N  R)  i p1 (i, R, N  R 1)  p1 (i, R  1, N  R  1)   p1 (i, R  1, N  R).

For j= 1,…, R-1, we have: (    i  qii ' ) p2 (i, j, 0)  qi 'i p2 (i ', j, 0)   p2 (i, j  1, 0)   p2 (i, j  1, 0).

 1 p1 (i,0, k  1)  i p1 (i,0, k  1).

(    qii ' ) p2 (i, j, H  j )  qi 'i p2 (i ', j, H  j )   p2 (i, j 1, H  j )

i p2 (i, j, H  j 1)   p2 (i, j  1, H  j ).

For j=1,…, R-1 and k=1,..., H-j-1, we have: (    i  qii ' ) p2 (i, j, k )  qi 'i p2 (i ', j, k )   p2 (i, j  1, k )   p2 (i, j  1, k )  i p2 (i, j, k  1).

For j= 1,…, R-1, we have: (    i  qii ' ) p1 (i, j,0)  qi 'i p1 (i ', j,0)   p1 (i, j  1,0)

 p2 (i, j  1, H )   p2 (i, j  1, H ).

(  i  1  qii ' ) p1 (i,0, k )  qi 'i p1 (i ',0, k )   p1 (i,1, k )

(    qii ' ) p1 (i, j, N  j )  qi 'i p1 (i ', j, N  j )   p1 (i, j 1, N  j ) i p1 (i, j, N  j  1)   p1 (i, j  1, N  j )   p1 (i, j  1, N  j  1).

For j=1,…, R and k=1,..., N-j-1, we have: (    i  qii ' ) p1 (i, j, k )  qi 'i p1 (i ', j, k )   p1 (i, j  1, k )   p1 (i, j  1, k )  i p1 (i, j, k  1).

 p2 (i,1, k )  1 p2 (i, 0, k  1)  i p2 (i, 0, k  1).

(    qii ' ) p2 (i, j, H )  qi 'i p2 (i ', j, H ) 

For k=1,..., N-1, we have:

 p1 (i, j  1,0).

(  i  1    qii ' ) p2 (i, 0, k )  qi 'i p2 (i ', 0, k ) 

For j=1,…, R-1 and k=H-j+1, ...., H-1, we have: (    qii ' ) p2 (i, j, k )  qi 'i p2 (i ', j, k )   p2 (i, j  1, k )   p2 (i, j  1, k ).

The normalization equation is given by: 2

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For k=1,…., N-R+1, we have:

(  i  qii ' ) p1 (i, R, k )  qi 'i p1 (i ', R, k )  i p1 (i, R, k 1)

 p (i, j, k )  1 . i 1 j  0 k  0

 p1 (i, R  1, k ).

The normalization equation is given by: 2

 p (i, j, k )  1 . i 1 j  0 k  0

B. System with Control For i and

i ' =1, 2, we have:

(qii '    i ) p2 (i,0,0)   p2 (i,1,0)  qi 'i p2 (i ',0,0)  1 p2 (i,0,1).

(  1  qii ' ) p2 (i,0, H )  qi 'i p2 (i ',0, H )  i p2 (i,0, H  1)   p2 (i,1, H ).

(  qii ' ) p2 (i, R, H  R)  qi 'i p2 (i ', R, H  R)   p2 (i, R 1, H  R) i p2 (i, R, H  R  1). (i    qii ' ) p2 (i, R, 0)   p2 (i, R  1, 0)  qi ' i p2 (i ', R, 0).

ISSN: 2230-7818

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