A Novel CAC Algorithm Based on Bandwidth Degradation for More Efficient Provision of the Requirements of Multimedia Applications

Maryam Monemian, Pejman Khadivi

Department of Electrical and Computer Engineering

Isfahan University of Technology

84156-83111, Isfahan, IRAN

m.monemian@ec.iut.ac.ir, pkhadivi@ec.iut.ac.ir

ABSTRACT

Undoubtedly, cellular wireless networks have experienced an astounding progress during recent years. They also attracted a considerable number of subscribers and satisfied their requirements by the provision of services with reasonable qualities. However, there are still some shortcomings in the performance of these networks which restrict their usability. For instance, the unexpected dropping of ongoing connections and the blocking of new requests are some of the undesirable aspects of these networks. In this paper, a Call Admission Control (CAC) algorithm is proposed which can strongly improve the Quality of Service (QoS) in these networks by the reduction of call dropping probability of new requests. This algorithm works based on the bandwidth degradation of current users. It also considers the bandwidth requirements of different subscribers and tries not to damage the QoS of current users. Simulation results show that this algorithm can significantly improve the performance of cellular wireless networks.

KEYWORDS—Wireless Networks, Call Admission Control, Bandwidth Degradation, Multimedia Applications.

1. INTRODUCTON

It is clear that the demand for different services such as voice, video and data over cellular wireless networks is growing day after day [1, 2]. Also, wireless networks are developed for the betterment of the user’s experience through improved QoS provisions [3]. With respect to the astonishing growth of the users of cellular wireless networks, it is necessary to improve the level of QoS in the network services. In a considerable number of previous works, QoS is determined by the blocking probability of new requests and the dropping probability of current connections [4, 5]. In other words, the

subscribers of cellular wireless networks expect to receive services with reasonable quality and without any interruption.

Wireless cellular networks have the benefit of providing a large number of services for the subscribers. These subscribers can be in various places and request different types of services which can be provided by wireless networks. The different services that users request from wireless cellular networks (services such as voice, data, and video) have different requirements. Also, the arrival of multimedia applications into mobile systems has encouraged people to request different services [4, 6, 7]. With no doubt, one of the most important differences between applications is the value of bandwidth that each of which needs to make a connection with reasonable quality. Definitely, bandwidth is a valuable resource in cellular wireless networks which is usually scarce and varies from one application to another one [8, 9, 10]. This is an important issue to efficiently satisfy the different requirements of various services by proposing some novel Call Admission Control (CAC) schemes [11, 12, 13, 14]. Call Admission Control refers to a number of methods which are used to restrict the number of call connections into the network in order to reduce network congestion [15].

For the provision of the requirements of different subscribers which make requests to wireless networks, service providers should arrange some CAC schemes to decide about the admission or rejection of new requests. A large number of these schemes have been suggested in literature that could improve the QoS in cellular networks [11, 16, 17, 18, 19]. Some of these CAC schemes specially manage the available bandwidth in the cell in order to decide about the rejection or admission of new requests [11, 18, 19].

In this paper, the main goal is to suggest a novel CAC algorithm which can significantly reduce the dropping rate of new requests with different requirements. This algorithm dynamically calculates the amounts of bandwidth which users consume and makes decision about the admission of new requests

754

6'th International Symposium on Telecommunications (IST'2012)

978-1-4673-2073-3/12/$31.00 ©2012 IEEE

according to the current conditions of the network. It can admit more requests by the bandwidth degradation of current users. By defining some threshold values for bandwidth degradation of current connections, this algorithm tries not to damage the QoS of current connections.

The structure of this paper is as follows. Section 2 includes the verification of some related works. In Section 3, the proposed algorithm is introduced and described in details. Section 4 includes some simulation results showing the performance of the novel algorithm. Finally, Section 5 contains some concluding remarks.

2. RELATED WORKS

In cellular networks, the QoS requirements of users can be expressed by the blocking probability of new requests and dropping probability of hand-offs [20]. A hand-off request happens when an active user moves from one cell to one of the adjacent cells. It should be noted that one of the most important connection-level QoS issues in Multimedia Wireless Networks (MWN) is reducing blocking and dropping probabilities [20]. Many previous works have been proposed for the improvement of blocking and dropping probabilities in wireless cellular networks [5, 21, 22, 23]. For instance, in [22], Khadivi et al. proposed a self-organizing method which can improve handoff process in terms of resource utilization and seamless handoff performance. This method relies on mobile ad hoc relaying to do seamless vertical handoff in hybrid networks. It can also improve the dropping probability of hand-off requests through ad hoc relaying. In other words, it is possible to efficiently use non-active Mobile Stations (MS) as relay stations to divert the traffic of congested cell to its neighbors [23].

Another effective way for the betterment of blocking and dropping rates in wireless cellular networks is the bandwidth degradation of active connections [14, 21, 24, 25]. Many CAC schemes have been reported previously based on the bandwidth degradation of current connections [9, 21, 24, 26]. In [9], a dynamic and adaptive bandwidth management scheme is proposed to provide high bandwidth utilization and low blocking probability. This is another CAC scheme using bandwidth degradation to provide some benefits such as greater bandwidth utilization and less dropping and blocking rate. In this method, at the case of bandwidth shortage for the admission of a new request, the flows with lower priority are selected for bandwidth degradation in order to provide the required bandwidth. Moreover; compensation timing is considered which causes that the probability of quality fluctuations is considerably reduced [9].

In [21], Monemian et al. proposed a method of bandwidth degradation which can improve the dropping rate in cellular networks. However, in [21], three types of bandwidth are considered for all the subscribers of these networks. This issue should be considered that different users requesting various applications may have different requirements. In other words, the maximum bandwidth defined for each application may vary from one application to another. However, only one amount of bandwidth is considered for the maximum bandwidth in [21] and this value is greater than or equal to the maximum values defined for all applications. In fact, this amount may be a high value for some applications which can reduce the efficiency in the network.

We discussed in [26] that using bandwidth degradation for the reduction of dropping rate causes some disadvantages and costs which may restrict their usability. Note that these costs are related to the amount of bandwidth which is reduced from current connections to admit more arrival requests. In [26], it is explained that the cost of bandwidth degradation scheme should be less than that of high dropping rate in cellular networks.

The effect of failures in the performance of cellular networks is considered in [27]. We mathematically modeled the transient failures of Base Stations (BS) and evaluated the effects of these failures on the average number of waiting users and average amount of waiting time [27]. Evaluations in [27] show that failure and repair rates have interesting results on the average number of waiting users and average amount of waiting time.

3. PROBLEM FORMULATION

In this section, the proposed algorithm is described in details. To introduce this new algorithm, the following assumptions are considered.

There are totally n services with certain bandwidth requirements provided in the network. In addition, some threshold values containing maximum value and minimum one are defined for each service, in order for network to serve different requests according to available resources. For more description consider service i which its users receive the bandwidth that is equal to . This amount of bandwidth, , is a value between two threshold values called and . is the maximum required bandwidth for making a connection for a user of service i. This amount of bandwidth provides the best quality for that service. is the minimum reasonable bandwidth for setting a connection which uses service i. Note that if the service provider can not provide the bandwidth being equal to for the new user of service i, it is dropped immediately. Considering this details, the following inequality holds:

755

1 (1)

With the advent of any new request, this algorithm starts to decide about the admission or rejection of that according to the current conditions of network. This algorithm contains several steps completely described in this section. It is supposed that this algorithm will be used in a unit named Bandwidth Allocation Unit (BAU). In this situation, the algorithm executes in a centralized manner. However, it is possible to employ this algorithm in a distributed manner.

1) With the advent of each request from service i, if the available bandwidth in the cell (which is called ) is more than , BAU allocates to that request.

2) With the arrival of each request from service i, if the available bandwidth in the cell is between and , BAU allots all the available bandwidth in the cell to that request.

3) With the advent of each request from service i, if the available bandwidth in the cell is less than , BAU should perform some calculations in order to decide about the rejection or admission of that request. In other words, it should evaluate whether or not it is possible to serve the new request through bandwidth degradation of current users. For doing such calculations, it is necessary to consider some assumptions. Assume that there are subscribers whose connections are related to service j. Each of these subscribers from service j, receives the bandwidth which is equal to . BAU calculates ∑ for the service j, in order to measure the amount of extra bandwidth which the related subscribers receive in comparison to their minimum threshold. ∑ (2) Then, BAU performs a comparison between the values that are related to all current services. In other words, BAU calculates arg ∑ . This calculation determines the service which its subscribers receive the greatest bandwidth in comparison to the minimum threshold of that service. The previously allocated bandwidth to the subscribers of the chosen service is degraded in order to provide bandwidth for the new user. The chosen service is called SELECTIVE service. It should be considered that this service is chosen if two conditions are satisfied. The first condition is that ∑

has the maximum value among the similar values for other services. The second one is that the released bandwidth by the users of SELECTIVE service is enough to provide the bandwidth requirements of the new user requesting service i. In other words, the service the subscribers of which receive the most additional bandwidth in comparison to the minimum reasonable bandwidth, release the additional bandwidth sooner than other subscribers. In this regard, we must have: ∑ (3) The policy used in this step of algorithm is that the bandwidth of the first user of the SELECTIVE service is degraded to . Afterwards, if the released bandwidth by this degradation is enough to admit the new request from service i, it can receive . Otherwise, BAU degrades the bandwidth of another user of SELECTIVE service so that the required bandwidth of the new user is provided by these two users of SELECTIVE service. The process of bandwidth degradation for the users of SELECTIVE service is repeated until the minimum required bandwidth of new request is achieved.

4) Assume a situation in which the available bandwidth in the cell is less than the minimum reasonable bandwidth of new request. In this situation, the service the users of which consume the greatest bandwidth (in comparison to the minimum threshold of that service) is known according to the step 3. Suppose that the additional bandwidth that the users of that service can release is less than the minimum reasonable bandwidth of the new user. In other words, the following inequality holds:

∑ (4) In this situation, BAU sorts different services according to the amount of the additional bandwidth that their subscribers consume in comparison to the corresponding minimum reasonable thresholds. Then, BAU selects the second service in the sorted list and calculates the sum of additional bandwidth that the first and the second services can release by bandwidth degradation. If the result of this summation is greater than the required bandwidth of the new request, it can be served by bandwidth degradation of users

756

from the first and second services. In this situation, the following inequality holds: ∑ ∑ (5) Otherwise, BAU calculates the sum of additional bandwidth that can be released by the users of the first, the second and the third services and repeats the process until it can provide the required bandwidth of the new request. In this situation, the dropping probability of the new user requesting service m, can be calculated through the following equation. ∑ ∑ ) (6)

4. SIMULATION RESULTS

In order to evaluate the performance of the proposed algorithm and its effects on the improvement of dropping rate, a large number of simulations have been performed. In the simulations, the process of arrival of the requests for different applications is considered to be Poisson with different rates. It is also assumed that the call duration for all requests has an exponential distribution. In the simulations we consider the case in which two services with two different ranges of bandwidth are provided for subscribers. Table 1 shows the values of different parameters which are effective in the final results. Simulation results have been shown in figures 1, 2, 3.

Figure 1 shows the dropping probability of users requesting service 1 versus the difference between the minimum and maximum bandwidths defined for each service. If the proposed algorithm is deeply verified, it will be clear that the difference between the maximum and minimum bandwidths of each service has effect on the dropping rate of the system. It sounds reasonable that with more difference between the maximum and minimum bandwidths of each service, more bandwidth can be released for the new connections. In the simulations, the maximum bandwidth for each service is considered as a constant value and we change the minimum bandwidth defined for that. It should be considered that the minimum value for the bandwidth of each service is an important value. In other words, if we want to evaluate the effect of difference between the maximum and minimum bandwidth of each application on the performance of the proposed algorithm, we should change the value of one threshold and keep another one constant. We change the minimum value and evaluate the effect of various values for difference between maximum and

minimum bandwidth. In this evaluation we notice that although we consider different values for the minimum bandwidth, this value should not be less than a threshold related to that application. Note that the numbers having been shown in the horizontal axis of Figure 1 explains the difference between the maximum and the minimum bandwidth defined for service class 1.

Figure 1. Dropping Probability of Users Vs. Difference between minimum and maximum bandwidths of services.

Table 1. The values of effective parameters in simulation.

Figure 2 shows the dropping probabilities of requests versus different values for arrival rate. It is clear that with increasing the arrival rate to the cell the dropping rate of new requests is increased. Undoubtedly, if the arrival rate of new requests is increased, the users enter to the cell more frequently and the available bandwidth in the cell is occupied faster, comparing with the smaller arrival rates. In this figure, the three trends which are related to our proposed algorithm, a simple degradation algorithm and a simple algorithm (with no bandwidth degradation) are compared. Note that in the simple algorithm the requests are served subsequently and for each request only one amount of bandwidth is considered. (In the proposed algorithm, three values of bandwidth are considered for each service). This amount of bandwidth is known as maximum bandwidth. If a request arrives and the available bandwidth in the cell is less than its maximum required bandwidth, it is rejected. Also we consider a simple degradation method in which the minimum

5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Difference Between Maximum & Minimum Bandwidth of Services

Dro

ppin

g P

roba

bilit

y of

Use

rs

Dropping Users Requesting Service 2

Dropping Users Requesting Service 1

Name Value Details Μ 100 Average of Call Duration (s) λ 0.15 Rate of Arrival for Users of Service 1 λ 0.15 Rate of Arrival for Users of Service 2 T 2000 Simulation Time (s) , 75,65 Maximum & Minimum Bandwidth for

Service 1 (kbps) , 65,50 Maximum & Minimum Bandwidth for Service 2 (kbps)

BW 7500 Total Bandwidth in Cell (kbps)

Difference between minimum and maximum bandwidths of services (kbps)

757

and maximum bandwidths are defined for each service. In this algorithm if the available bandwidth in the cell is lower than the minimum threshold of the service, the request is rejected. As it is obvious from Figure 2, the dropping rate in the proposed algorithm has better values in comparison with simple algorithm and the simple degradation method.

Figure 3 shows the dropping probabilities of users requesting service 2 versus different values for arrival rate. The trends in this figure show similar behavior to trends in Figure 2.

1. CONCLUSIONS

With respect to the tremendous growth of the users of wireless cellular networks, it is necessary for service providers to provide network services with reasonable quality. Considering different requirements of various services, the CAC schemes which decide based on the requirements of each service will be effective. In this paper, a CAC algorithm was proposed which works based on the bandwidth degradation of ongoing connections. In this algorithm, with the advent of any new request, if the available bandwidth in the cell is less than the minimum bandwidth defined for that request, some of the ongoing connections degrade their bandwidth. Those connections are the ones who consume the maximum amount of bandwidth in comparison to their minimum reasonable bandwidth. By using this algorithm, although the QoS of some current connections is to some extent degraded, but the algorithm does not significantly damage it since the minimum reasonable thresholds are not exceeded during the implementation of this algorithm. Simulation results show that the parameters determining QoS such as dropping and blocking probabilities of new connections are considerably improved.

Figure 2. Dropping rate of users requesting service 1 Vs. different values for arrival rate.

Figure 3. Dropping rate of users requesting service 2 Vs. different values for arrival rate.

REFERENCES

[1] S. Kim, and P. K. Varshney, “Adaptive fault tolerant bandwidth management framework for multimedia cellular networks”, Proceedings of IEE Communications, Volume: 152, Issue: 6, 2005.

[2] C. Wen-Yu, Y. Hai-Bo, “A hierarchical QoS framework for wireless multimedia network”, International Conference on Communications, Circuits and Systems, ICCCAS 2008, 2008, pp. 732-736.

[3] N. Abuali, M. Hayajneh, and H. Hassanein, “Congestion-based pricing resource management in broadband wireless networks”, IEEE Transactions on Wireless Communications, Volume: 9, Issue: 8, 2010, pp. 1-10.

[4] N. Nasser, “Service adaptability in multimedia wireless networks”, IEEE Transactions on Multimedia, Vol. 11, No. 4, 2009, pp. 786-792.

[5] H. K. Pati, “A distributed adaptive guard channel reservation scheme for cellular networks”, International Journal of Communication Systems, 2006, pp. 1037-1058.

[6] A. Iera, A. Molinaro, and C. Compolo, “New concept platforms for QoS management in future telecommunication scenarios”, International Journal of Wireless Information Networks, Volume: 14, Number: 2, 2007, pp. 79-91.

[7] H. Park, and M. VanderSchaar, “Fairness strategies for wireless resource allocation among autonomous multimedia users”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 20, Issue: 2, 2010, pp. 297-309.

[8] N. Velmurugan, S. Karthikeyan, and P. Dananjayan, “Resource allocation management for multimedia QoS in wireless networks”, International Conference on Signal Processing, Communications and Networking, ICSCN’07, 2007, pp. 122-127.

[9] H. B. Guo, and G. S. Kuo, “A dynamic and adaptive bandwidth management scheme for QoS support in wireless multimedia networks”, IEEE 61st Vehicular Technology Conference, VTC 2005, Volume: 3, 2005, pp. 2081-2085.

[10] S. Kim, P. K. Varshney, “An adaptive bandwidth reservation algorithm for QoS sensitive multimedia cellular networks”, Proceedings of IEEE 56th Vehicular Technology Conference, VTC 2002, Volume: 3, 2002, pp. 1475-1479.

[11] I. S. Hwang, B. J. Hwang, L. F. Ku, and P. M. Chang, “Adaptive bandwidth management and reservation scheme in

0.2 0.25 0.3 0.35 0.4 0.45 0.50

0.1

0.2

0.3

0.4

0.5

0.6

Dro

ppin

g P

roba

bilit

y of

Use

rs R

eque

stin

g S

ervi

ce 1

Sum of Arrival Rates Related to Services

Proposed Algorithm

Simple Degradation Algorithm

Simple Algorithm

0.2 0.25 0.3 0.35 0.4 0.45 0.50

0.1

0.2

0.3

0.4

0.5

0.6

Sum of Arrival Rates Related to Services

Dro

ppin

g P

roba

bilit

y of

Use

rs R

eque

stin

g S

ervi

ce 2 Proposed Algorithm

Simple Degradation AlgorithmSimple Algorithm

758

heterogeneous wireless networks”, IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, SUTC’08, 2008, pp. 338-342.

[12] H. Pervaiz, “A Multi-Criteria Decision Making (MCDM) network selection model providing enhanced QoS differentiation to customers”, International Conference on Multimedia Computing and Information Technology, MCIT 2010, 2010, pp. 49-52.

[13] H. Zhai, X. Chen, and Y. Fang, “A call admission and rate control scheme for multimedia support over IEEE 802.11 wireless LANs”, Proceedings of the First International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QSHINE’04), 2004, pp. 76-83.

[14] T. C. Hong, K. S. Kang, D. S. Ahn, and H. J. Lee, “Call admission control scheme with bandwidth borrowing in integrated services network”, Proceedings of 20th international conference on Advanced Information Networking and Applications (AINA’06), Volume: 1, 2006, pp. 839-844.

[15] P. Koutsakis, “Call Admission Control for wireless videoconference traffic based on users willingness to pay”, 2010 European Wireless Conference, 2010, pp. 34-41.

[16] B. Almanthari, N. Nasser, N. A. Ali, H. Hassanein, “Efficient bandwidth management in broadband wireless access systems using CAC-based dynamic pricing”, IEEE 33rd Conference on Local Computer Networks, LCN 2008, 2008, pp. 484-491.

[17] N. C. Taher, Y. G. Doudane, B. E. Hassan, N. Agoulmine, “An efficient model-based admission control algorithm to support voice and video services in 802.11e WLANs”, Global Information Infrastructure Symposium, GIIS’09, 2009, pp. 1-8.

[18] A. Agbaria, G. Gershinsky, N. Naaman, and K. Shagin, “A bandwidth management approach for Quality of Service support in mobile Ad Hoc networks”, IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, 2009, pp. 1-5.

[19] H. T. Cheng, A. Abdrabou, and W. Zhuang, “Novel resource management approach for end-to-end QoS support for wireless mesh networks”, Proceedings of 2010 IEEE Wireless Communications and Networking Conference, WCNC, 2010, pp. 1-6.

[20] N. Nasser, “Enhanced blocking probability in adaptive multimedia wireless networks”, 25th IEEE International Performance, Computing, and Communication Conference, IPCCC 2006, 2006, pp. 647-652.

[21] M. Monemian, P. Khadivi, and M. Palhang, “Bandwidth degradation for dropping rate reduction in cellular networks”, Proceedings of IEEE 9th Malaysia International Conference on Communications, MICC’09, 2009, pp. 613-617.

[22] P. Khadivi, S. Samavi, H. Saidi, T. D. Todd, “Handoff in hybrid wireless networks based on self organization”, IEEE International Conference on Communications, ICC’06, Volume: 5, 2006, pp. 1996-2001.

[23] P. Khadivi, T. D. Todd, S. Samavi, H. Saidi, D. Zhao, “Mobile ad hoc relaying for upward vertical handoff in hybrid WLAN/cellular systems”, Elsevier Journal on Ad hoc Networks, Volume: 6, Issue: 2, 2008, pp. 307-324.

[24] L. Xie, J. Xiang, “A novel bandwidth degradation scheme for admission control in IEEE 802.16e networks”, 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM’08, 2008, pp. 1-4.

[25] W. Junqi, H. Zhengbing, Y. Jiuming, S. Jun, “The degradation of bandwidth in QoS scheme for 4G mobile networks”,

International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, 2007, pp. 2020-2024.

[26] M. Monemian, P. Khadivi, and M. Palhang, “Cost calculation in bandwidth degradation schemes in cellular networks”, 2010 18th Iranian Conference on Electrical Engineering, ICEE 2010, 2010, pp. 162-166.

[27] M. Monemian, P. Khadivi, and M. Palhang, “Analytical model of failure in LTE networks”, Proceedings of IEEE 9th Malaysia International Conference on Communications, MICC’09, 2009, pp. 821-825.

759